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市場調查報告書
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2072250

人工智慧在藥物發現領域的市場（第三版）：按藥物發現流程、人工智慧技術類型、治療領域、最終用戶和地區分類的趨勢和預測（至2035年）

AI in Drug Discovery Market (3rd Edition) by Drug Discovery Step, Type of AI Technology, Therapeutic Area, End User and Geographical Regions - Trends and Forecast, Till 2035
出版日期: | 出版商: Roots Analysis | 英文 365 Pages | 商品交期: 最快1-2個工作天內

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人工智慧在藥物研發領域的市場概覽

預計到 2035 年，人工智慧在藥物發現領域的全球市場規模將從今年的 86 億美元成長到 250 億美元，在預測期內（到 2035 年）的複合年成長率為 12.6%。

人工智慧在藥物研發領域的市場—成長與趨勢

在研發投入增加和對新療法的需求不斷成長的推動下，人工智慧（AI）工具和平台在藥物研發領域的應用正顯著加速。癌症、神經系統疾病、心血管疾病和感染疾病等慢性疾病的日益普及持續造成巨大的臨床和經濟負擔。因此，人們越來越需要開發更快、更有效、更經濟的治療方法。

此外，人口老化加劇了這些挑戰，促使製藥和生物技術公司將人工智慧技術融入其研發生態系統。這些平台能夠提高標靶識別、先導化合物生成和最佳化等關鍵階段的運作效率，同時克服傳統藥物研發固有的局限性，例如研發週期長和失敗率高。

人工智慧平台能夠分析大量的多組體學資料集，並透過支援虛擬篩檢、從頭分子設計和預測毒理學等進階應用，加速篩選出療效和安全性較佳的候選藥物。此外，包括生成模型和強化學習在內的前沿調查方法，有助於減少人為偏見，並加強藥物再利用，以滿足尚未滿足的醫療需求。例如， In Silico Medicine公司近期的進展——其人工智慧設計的候選藥物ISM001-055已進入治療特發性肺纖維化的II期臨床試驗——凸顯了業界對人工智慧加速和簡化藥物研發流程潛力的日益成長的信心。

因此，人工智慧驅動的解決方案正日益成為生物技術公司和大型企業在藥物研發早期階段的首選方法。平台供應商正透過整合用於資料提取的多模態大規模語言模型（LLM）、雲端工作流程以及用於病患分層的精準分析，進一步提升自身能力。持續的投資和策略合作正在進一步增強市場勢頭，預計這將支撐人工智慧驅動的藥物研發市場在可預見的未來保持持續成長。

成長促進因素：市場擴張的策略推動者

在研發投入增加和生物醫學資料集快速成長的推動下，人工智慧（AI）在藥物研發領域的應用正在加速發展。尤其值得一提的是，創業投資積極湧入能夠增強標靶識別、分子交互作用預測和先導化合物最佳化的AI平台，這成為市場成長的關鍵催化劑。同時，基因組學、蛋白質組學和真實世界數據（RE）帶來的數據量激增，也為基於機器學習的藥物研發提供了巨大的機會。 AI平台能夠有效地利用這些資料集來識別新的標靶並實現藥物重定位。這種數據驅動的能力提高了研發效率，縮短了研發週期。因此，生物技術和製藥公司正擴大採用AI驅動的解決方案。預計這些因素將共同推動市場在預測期內持續成長。

市場挑戰：阻礙進展的主要障礙

儘管人工智慧在藥物研發領域具有諸多優勢，但仍面臨著可能阻礙其廣泛應用的重大挑戰。這些挑戰包括資料整合難題和缺乏標準化的法規結構。製藥公司常常難以整合來自不同來源、以各種格式儲存的數據，例如基因體學、蛋白質體學和臨床前試驗數據，導致資料集碎片化。這種碎片化限制了高效的多組體學分析，並阻礙了在整個藥物研發流程中獲得可操作性見解。

互通性的缺失阻礙了人工智慧平台在標靶識別和先導化合物最佳化方面充分發揮其潛力，凸顯了標準化資料架構和統一框架的必要性。此外，缺乏清晰一致的資料安全、隱私權和智慧財產權保護監管準則也嚴重阻礙了人工智慧的廣泛應用。這些問題迫使各機構限制對敏感資料集（例如化合物庫和臨床結果）的訪問，導致合作模型開發受限，並降低了人工智慧驅動藥物發現的整體效率。

人工智慧在藥物研發市場的應用：關鍵洞察

本報告詳細分析了人工智慧在藥物研發領域的市場現狀，並指出了該行業潛在的成長機會。報告的主要發現包括：

  • 目前，已有超過 260 家公司致力於提供以人工智慧為基礎的藥物發現平台。
  • 大多數主要參與企業（76%）正在使用人工智慧平台創建自己的候選化合物，凸顯了製藥公司越來越重視整合人工智慧解決方案以加速藥物發現過程。
人工智慧在藥物發現市場的應用-IMG1
  • 目前，人工智慧藥物研發平台提供者的市場格局較為分散，既有新參與企業，也有老牌企業。此外，這些公司中的大多數（58%）位於北美。
  • 人工智慧在藥物研發領域的大量資金籌措投入表明，創投和策略投資者對該領域的興趣日益濃厚。
  • 大部分資金籌措（超過 35%）來自創業投資資金籌措，特別是 A 輪融資，其次是津貼和獎勵，約佔總額的 15%。
  • 對高效平台的需求不斷成長，以及藥物發現過程的縮短，導致過去幾年專利申請的複合年成長率達到 67%。
人工智慧在藥物發現市場的應用-IMG2
  • 在不久的將來，引入基於人工智慧的解決方案來治療各種疾病，特別是腫瘤疾病，有望顯著降低治療成本。
  • 目前人工智慧在藥物研發領域的市場規模估計約86億美元。預計該市場將以12.6%的複合年成長率成長，到2035年將達到約250億美元。
人工智慧在藥物發現市場的應用-IMG3
  • 鑑於慢性病和遺傳疾病的日益普遍，對先進的人工智慧驅動的藥物發現平台的需求顯著成長，因此，這些平台的市場機會也在不斷擴大。
  • 人工智慧驅動的藥物發現市場是由製藥和生物技術公司的大量研發投資、其藥物發現流程中的綜合能力以及這些公司產生的收入所驅動的。

人工智慧在藥物研發市場的應用

市場規模和機會分析是根據以下參數進行細分的：

按藥物發現階段分類

  • 目標識別/檢驗
  • 先導化合物發現/先導化合物鑑定
  • 潛在客戶最佳化

人工智慧技術類型

  • 機器學習
  • 分子建模與模擬
  • 深度學習
  • 體學整合
  • 生成模型
  • 基於結構的藥物發現
  • 其他

按治療區域

  • 腫瘤性疾病
  • 心血管疾病
  • 肌肉骨骼疾病
  • 神經系統疾病
  • 呼吸系統疾病
  • 免疫系統疾病
  • 消化器官系統疾病
  • 內分泌疾病
  • 血液疾病
  • 眼科疾病
  • 皮膚病
  • 感染疾病
  • 泌尿器官系統疾病

最終用戶

  • 製藥和生物技術公司
  • 研究與開發合約組織
  • 研究和學術機構

按地區

  • 北美洲
  • 歐洲
  • 亞太地區
  • 拉丁美洲
  • 中東和北非

人工智慧在藥物研發市場的應用—關鍵細分領域

先導藥物最適化正成為人工智慧驅動藥物發現的一個重要環節。

全球藥物研發領域的人工智慧市場可細分為幾個關鍵階段：標靶識別與檢驗、先導化合物（候選藥物）生成以及先導藥物最適化。目前估計，先導藥物最適化階段約佔整體市場的50%，是貢獻最大的階段。這一主導地位主要源於資源密集型活動，例如改進化學合成、全面的ADMET分析以及功效和效價最佳化，這些活動佔了臨床前研發支出的很大一部分。人工智慧平台在這一階段發揮著至關重要的作用，它能夠實現預測建模和高通量篩檢，從而提高效率並簡化整個藥物研發過程中的結果最佳化。

區域分析－北美引領人工智慧在藥物發現市場的成長

目前，北美在人工智慧藥物研發市場佔主導地位，市佔率超過全球50%。這一主導地位得益於多項因素，包括對研發的大量投入、先進的醫療IT基礎設施以及有利的法規環境。尤其值得一提的是，美國食品藥物管理局（FDA）為促進人工智慧和機器學習技術的應用而製定的有利框架，顯著加速了該全部區域的創新和市場擴張。

終端用戶分析－製藥和生物技術公司保持市場主導

從終端用戶的角度來看，人工智慧在藥物研發領域的市場可細分為製藥和生物技術公司、受託研究機構（CRO）以及學術和研究機構。根據目前的市場趨勢，製藥和生技公司佔主導地位，預計這一趨勢將在預測期內持續。其主導地位主要歸功於其雄厚的財力、專注於加速藥物研發流程的策略以及支援人工智慧技術在整個藥物研發工作流程中無縫整合的先進基礎設施。

第一次調查總結

本研究的觀點和見解受到了該領域多位相關人員的討論的影響。本市場報告包含與以下相關人員的詳細訪談記錄：

  • 英國某中小企業的首席商務官和首席產品官
  • 美國中小企業：聯合創始人、董事長和執行長
  • 首席調查員，韓國一家中型公司
  • 英國某小型企業的執行長
  • 一家英國中型公司的首席商務官
  • 以色列，中小企業，執行長聯合創始人
  • 美國一家小規模企業的董事長

人工智慧藥物研發市場中一些主要公司的例子

  • BenevolentAI
  • Collaborations Pharmaceuticals
  • CytoReason
  • Deargen
  • Deep Genomics
  • Genialis
  • Healx
  • Insilico Medicine
  • Iktos
  • Optibrium
  • XtalPi

人工智慧在藥物研發領域的市場：研究範圍

  • 市場規模和機會分析：本報告詳細分析了人工智慧在藥物發現市場中的應用，重點關注關鍵市場細分，例如 [A] 藥物發現的各個階段，[B] 人工智慧技術類型，[C] 治療領域，[D] 最終用戶，以及 [E] 地區。
  • 人工智慧藥物發現市場趨勢：除了對人工智慧藥物發現市場的整體情況進行詳細評估外，本報告還提供了詳細的評估，其中包括多個相關參數的信息，例如 [A]經營模式類型，[B] 平台使用情況，[C] 相應的藥物發現階段，[D] 採用的人工智慧技術類型，[E] 目標治療領域，[F] 分析的分子類型，用戶類型，[總部所在地。
  • 公司簡介：詳細介紹總部位於北美、歐洲和亞太地區的領先公司。這些簡介基於多個參數，包括[A]成立年份、[B]總部所在地、[C]產品系列、[D]近期發展和[E]未來展望。
  • 合作關係與合作研究：根據多個參數分析了近期人工智慧相關的藥物發現合作關係與合作研究，包括[A] 合約年份、合作類型、[B] 合作夥伴類型、[C] 區域分析和[D] 最活躍的參與者。
  • 資金籌措和投資分析：本分析基於幾個相關參數，詳細考察了該領域參與者的資金籌措，包括[A]資金籌措年份，[B]資金籌措類型，[C]投資金額，[D]最活躍的參與者（就資金籌措數量和投資金額而言），以及[F]主要投資者（就融資交易數量而言）。
  • 專利分析：我們根據以下關鍵參數對已提交和已註冊的專利進行詳細分析：[A] 專利類型，[B] 公開年份，[C] 申請年份，[D] 已授權專利和專利申請數量，[E] 專利管轄區，[F] CPC 代碼，[G] 專利已過年限，[H] 專利，以及 [I] 專利
  • 波特五力分析：量子網路市場中五個主要競爭因素的分析。這些因素包括新進業者的威脅、買方的議價能力、供應商的議價能力、替代品的威脅、以及現有競爭對手之間的競爭。
  • 企業估值分析：我們對為各種藥物發現業務提供人工智慧平台/技術的公司進行相對評估。
  • 主要科技公司在醫療保健領域的 AI 賦能計劃：本節概述了主要科技公司及其在醫療保健領域的 AI 賦能計劃。
  • 成本降低分析：這項富有洞察力的分析重點在於使用人工智慧驅動的藥物發現平台所帶來的成本降低潛力。
  • 市場影響分析：本部分詳細分析可能影響市場成長的因素，包括關鍵市場促進因素、潛在限制因素、新興機會以及該行業現有挑戰的識別和分析。

目錄

第1章：序言

第2章：調查方法

第3章 市場動態

第4章 宏觀經濟指標

第5章摘要整理

第6章：引言

  • 章節概要
  • 人工智慧
  • AI子集
    • 機器學習
  • 人工智慧在醫療領域的應用
    • 藥物發現
    • 疾病預測、診斷和治療
    • 製造和供應鏈運營
    • 醫藥行銷
    • 臨床試驗
  • 人工智慧在藥物研發的應用
    • 目標識別
    • 辨識命中或線索
    • 先導藥物最適化
  • 在藥物發現過程中使用人工智慧的優勢
  • 人工智慧實施面臨的挑戰
  • 前景

第7章 市場狀況

  • 章節概要
  • 基於人工智慧的藥物發現平台提供者：市場展望
  • 主要人工智慧藥物發現平台提供者：市場概況

第8章 公司簡介：北美基於人工智慧的藥物發現平台提供商

  • 章節概要
  • Collaborations Pharmaceuticals
  • Deep Genomics
  • Genialis
  • Insilico Medicine
  • XtalPi

第9章 公司簡介：歐洲基於人工智慧的藥物發現平台供應商

  • 章節概要
  • BenevolentAI
  • Healx
  • Iktos
  • Optibrium

第10章 公司簡介：亞太及其他地區的AI藥物發現平台提供者

  • 章節概要
  • CytoReason
  • Deargen

第11章夥伴關係與合作

  • 章節概要
  • 夥伴關係模式
  • 人工智慧驅動的藥物發現：夥伴關係與合作

第12章：資金籌措與投資分析

  • 章節概要
  • 資金籌措模式
  • 人工智慧驅動的藥物發現：資金籌措和投資

第13章 專利分析

  • 章節概要
  • 研究範圍和調查方法
  • 人工智慧驅動的藥物發現：專利分析
  • 專利基準分析
  • 專利評估
  • 被引用次數最多的專利

第14章：波特五力模型分析

第15章：公司估值分析

第16章：科技巨頭的AI驅動型醫療保健計劃

  • 章節概要
  • Alibaba Cloud
  • Google
  • IBM
  • Intel
  • Microsoft
  • Siemens

第17章：成本降低分析

第18章 市場影響分析：促進因素、阻礙因素、機會與挑戰

第19章：全球人工智慧藥物發現市場

第20章：人工智慧驅動的藥物發現市場（依藥物發現階段分類）

第21章：人工智慧驅動的藥物發現市場（按人工智慧技術類型分類）

第22章：人工智慧驅動的藥物發現市場（按治療領域分類）

第23章：人工智慧驅動的藥物發現市場（按最終用戶分類）

第24章：人工智慧驅動的藥物發現市場（按地區分類）

第25章 結論

第26章：高階主管洞察

第27章附錄一：表格形式數據

第28章 附錄二：公司與組織列表

Product Code: RA100342

AI in Drug Discovery Market: Overview

As per Roots Analysis, the global AI in drug discovery market is estimated to grow from USD 8.6 billion in the current year to USD 25.0 billion by 2035, at a CAGR of 12.6% during the forecast period, till 2035.

AI in Drug Discovery Market: Growth and Trends

The adoption of artificial intelligence (AI) tools and platforms in drug discovery is experiencing significant acceleration, driven by rising R&D investments and an increasing demand for novel therapeutic solutions. The growing prevalence of chronic diseases, including cancer, neurological disorders, cardiovascular conditions, and infectious diseases, continues to impose substantial clinical and economic burdens. This, in turn, is intensifying the need for faster, more effective, and cost-efficient treatment development.

Additionally, demographic shifts toward aging populations further exacerbate these challenges, prompting pharmaceutical and biotechnology companies to integrate AI-driven technologies into their R&D ecosystems. These platforms enhance operational efficiency across key stages such as target identification, lead generation, and optimization, while addressing inherent limitations of traditional drug discovery, including prolonged development timelines and high attrition rates.

AI-enabled platforms are capable of analyzing extensive multi-omics datasets, supporting advanced applications such as virtual screening, de novo molecule design, and predictive toxicology, thereby accelerating the identification of drug candidates with improved efficacy and safety profiles. Furthermore, cutting-edge methodologies, including generative models and reinforcement learning, help reduce human bias and strengthen drug repurpose efforts to address unmet medical needs. For instance, recent advancements by Insilico Medicine, where its AI-designed drug candidate ISM001-055 has advanced into Phase II clinical trials for idiopathic pulmonary fibrosis, underscore growing industry confidence in the potential of AI to accelerate and streamline drug development timelines.

As a result, AI-driven solutions are increasingly emerging as the preferred approach for early-stage discovery among both biotechnology firms and leading pharmaceutical companies. Platform providers are further enhancing capabilities through the integration of multimodal large language models (LLMs) for data extraction, cloud-enabled workflows, and precision analytics for patient stratification. Ongoing investments and strategic collaborations continue to reinforce strong market momentum, supporting sustained growth of the AI-driven drug discovery market in the foreseeable future.

Growth Drivers: Strategic Enablers of Market Expansion

The adoption of artificial intelligence (AI) in pharmaceutical drug discovery is accelerating, driven by increasing R&D investments and the rapid expansion of biomedical datasets. Strong venture capital inflows into AI platforms particularly those enhancing target identification, molecular interaction prediction, and lead optimization are acting as a key catalyst for market growth. At the same time, the surge in data from genomics, proteomics, and real-world evidence is creating significant opportunities for machine learning-based discovery. AI platforms effectively leverage these datasets to identify novel targets and enable drug repurposing. This data-driven capability is improving R&D efficiency and reducing development timelines. Consequently, biotechnology and pharmaceutical companies are increasingly adopting AI-powered solutions. Together, these factors are expected to support sustained market expansion over the forecast period.

Market Challenges: Critical Barriers Impeding Progress

Despite its numerous advantages, AI in drug discovery faces notable challenges that may hinder its widespread adoption. These include data integration challenges and the absence of standardized regulatory frameworks. Pharmaceutical companies often struggle to consolidate data from disparate sources such as genomics, proteomics, and preclinical studies stored in varied formats, leading to fragmented datasets. This fragmentation limits efficient multi-omics analysis and hinders the generation of actionable insights across discovery pipelines.

This lack of interoperability restricts the full potential of AI platforms in target identification and lead optimization, underscoring the need for standardized data architectures and unified frameworks. Additionally, the absence of clear and consistent regulatory guidelines around data security, privacy, and intellectual property protection poses a critical barrier to broader AI adoption. These concerns prompt organizations to limit access to sensitive datasets, including compound libraries and clinical outcomes, thereby constraining collaborative model development and reducing the overall effectiveness of AI-driven drug discovery.

AI in Drug Discovery Market: Key Insights

The report delves into the current state of the AI in drug discovery market and identifies potential growth opportunities within industry. Some key findings from the report include:

  • At present, over 260 companies are engaged in providing AI-based drug discovery platforms.
  • Majority players (76%) utilize their AI platforms for the discovery of in-house pipeline candidates, highlighting the rising focus of pharmaceutical firms in integrating AI-powered solutions to expedite discovery processes.
AI in Drug Discovery Market - IMG1
  • The current market landscape of AI-based drug discovery platform providers is fragmented, featuring the presence of both new entrants and established players; most (58%) of the players are based in North America.
  • The AI in drug discovery domain has witnessed significant funding and investments, which indicates the growing interest of venture capitalists and strategic investors in this domain.
  • Majority (>35%) of the funding amount has been raised through venture capital rounds, especially series A rounds; this is followed by grants / awards, which account for ~15% of the total number of instances.
  • The patent activity has increased at a CAGR of 67% in the last few years owing to the growing need for efficient platforms and reduced timeline of drug discovery processes.
AI in Drug Discovery Market - IMG2
  • The cost saving potential associated with the implementation of AI-based solutions for various therapeutic disorders, especially oncological disorders, is anticipated to significantly increase in the foreseen future.
  • The current AI in drug discovery market is estimated to be around USD 8.6 billion; this value is further projected to reach about USD 25.0 billion in 2035, growing at an annualized CAGR of 12.6%.
AI in Drug Discovery Market - IMG3
  • Given the increasing prevalence of chronic as well as genetic diseases, the need for advanced AI-based drug discovery platforms has increased considerably, thereby increasing the market opportunity for these platforms.
  • The AI-based drug discovery market is driven by revenues generated from pharma and biotech companies' owing to their substantial R&D investments and integrated capabilities in drug development pipelines.

AI in Drug Discovery Market

The market sizing and opportunity analysis has been segmented across the following parameters:

By Drug Discovery Step

  • Target Identification / Validation
  • Hit Generation / Lead Identification
  • Lead Optimization

By Type of AI Technology

  • Machine Learning
  • Molecular Modelling and Simulation
  • Deep Learning
  • Omics Integration
  • Generative Model
  • Structure-based Drug Design
  • Others

By Therapeutic Area

  • Oncological Disorders
  • Cardiovascular Diseases
  • Musculoskeletal diseases
  • Neurological Disorder
  • Respiratory Disorders
  • Immunological Disorders
  • Gastrointestinal Disorders
  • Endocrine Disorders
  • Blood Disorders
  • Ophthalmological Disorders
  • Dermatological Disorders
  • Infectious Diseases
  • Urinary Disorders

By End User

  • Pharma and Biotech Companies
  • Contract Research Organizations
  • Research and Academic Institutions

By Geographical Regions

  • North America
  • Europe
  • Asia-Pacific
  • Latin America
  • Middle East and North Africa

AI in Drug Discovery Market: Key Segments

Lead Optimization Emerged as the Dominant Segment in AI-Driven Drug Discovery

The global AI in drug discovery market is segmented across key stages, including target identification and validation, hit generation or lead identification, and lead optimization. Based on current estimates, the lead optimization segment accounts for approximately 50% of the overall market share, making it the largest contributor. This dominance is primarily attributed to the significant proportion of preclinical R&D expenditure allocated to this stage, driven by resource-intensive activities such as chemical synthesis refinement, comprehensive ADMET profiling, and efficacy and potency optimization. AI platforms play a critical role in this phase by enabling predictive modeling and high-throughput screening, thereby enhancing efficiency and streamlining outcome optimization across the drug development process.

Regional Analysis: North America Leads AI in Drug Discovery Market Growth

North America currently dominates the AI in drug discovery market, accounting for over 50% of the global share. This leadership position is driven by multiple factors, including substantial investments in research and development, the presence of advanced healthcare IT infrastructure, and a supportive regulatory environment. In particular, favorable frameworks established by the U.S. Food and Drug Administration (FDA) to facilitate the adoption of AI and machine learning technologies are significantly accelerating innovation and market expansion across the region.

End User Analysis: Pharma and Biotech Companies to maintain market Leadership

Based on end users, the AI in drug discovery market is segmented across pharmaceutical and biotechnology companies, contract research organizations, and academic and research institutions. Based on current market insights, pharmaceutical and biotech companies hold the dominant share, a trend expected to persist over the forecast period. This leadership is primarily attributed to their strong financial capabilities, strategic focus on accelerating drug development pipelines, and advanced infrastructure that supports seamless integration of AI technologies across discovery workflows.

Primary Research Overview

Discussions with multiple stakeholders in this domain influenced the opinions and insights presented in this study. The market report includes detailed transcripts of interviews conducted with the following individuals:

  • Chief Commercial Officer and Chief Product Officer, Small Company, UK
  • Co-founder, Chairman and Chief Executive Officer, Small Company, US
  • Head Researcher, Mid-sized Company, South Korea
  • Chief Executive Officer, Small Company, UK
  • Chief Commercial Officer, Mid-sized Company, UK
  • Chief Executive Officer and Co-Founder, Small Company, Israel
  • Chairman, Small Company, US

Example Players in AI in Drug Discovery Market

  • BenevolentAI
  • Collaborations Pharmaceuticals
  • CytoReason
  • Deargen
  • Deep Genomics
  • Genialis
  • Healx
  • Insilico Medicine
  • Iktos
  • Optibrium
  • XtalPi

AI in Drug Discovery Market: Research Coverage

  • Market Sizing and Opportunity Analysis: The report features an in-depth analysis of the AI in drug discovery market, focusing on key market segments, including [A] drug discovery steps, [B] type of AI technology, [C] therapeutic area, [D] end user, and [E] and geographical regions.
  • AI in Drug Discovery Market Landscape: A detailed assessment of the overall AI in drug discovery market landscape, along with information on several relevant parameters, such as [A] type of business model, [B] platform utilization, [C] drug discovery stages supported, [D] type of AI technology used, [E] target therapeutic area, [F] type of molecule analyzed, [G] end user, [H] company size, [I] year of establishment and [J] location of headquarters.
  • Company Profiles: In-depth profiles of key companies based in North America, Europe and Asia-Pacific based on several parameters such as [A] year of establishment, [B] location of headquarters, [C] product portfolio, [D] recent developments and [E] an informed future outlook.
  • Partnerships and Collaborations: An analysis of the recent partnerships and collaborations related to AI in drug discovery, based on several parameters, such as [A] year of agreement, type of partnership, [B] type of partner, [C] geographical analysis, [D] and most active players.
  • Funding and Investment Analysis: A detailed analysis of various investments made by players in this domain based on several relevant parameters, such as [A] year of funding, [B] type of funding, [C] amount invested, and [D] most active players (in terms of number of funding instances and amount invested) and [F] key investors (in terms of number of funding instances).
  • Patent Analysis: A detailed analysis of the patents that have been filed / granted based on important parameters such as, [A] type of patent, [B] publication year, [C] application year, [D] number of granted patents and patent applications, [E] patent jurisdiction, [F] CPC symbols, [G] patent age, [H] type of applicant, and [I] individual patent assignees (in terms of size of intellectual property portfolio).
  • Porter's Five Forces Analysis: An analysis of five competitive forces prevailing in the quantum networking market, including threats of new entrants, bargaining power of buyers, bargaining power of suppliers, threats of substitute products and rivalry among existing competitors.
  • Company Valuation Analysis: A relative valuation of companies offering AI platforms / technologies for various drug discovery operations.
  • AI based Healthcare Initiatives of Technology Giants: An overview of technology giants, along with AI-based initiatives undertaken by these firms in the healthcare domain.
  • Cost Saving Analysis: An insightful analysis, highlighting the cost saving potential associated with the use of AI-based drug discovery platforms.
  • Market Impact Analysis: An in-depth analysis of the factors that can impact the growth of the market. It also features identification and analysis of key drivers, potential restraints, emerging opportunities, and existing challenges in this domain.

Key Questions Answered in this Report

  • Which are the leading companies in the AI in drug discovery market?
  • Which region dominates the AI in drug discovery market?
  • What are the key trends observed in AI in drug discovery market?
  • What factors are likely to influence the evolution of this market?
  • What are the primary challenges faced by AI in drug discovery market?
  • What is the current and future market size?
  • What is the CAGR of this market?
  • How is the current and future market opportunity likely to be distributed across key market segments?

Reasons to Buy this Report

  • The report provides a comprehensive market analysis, offering detailed revenue projections of the overall market and its specific sub-segments. This information is valuable to both established market leaders and emerging entrants.
  • The report offers stakeholders a comprehensive overview of the market, including key drivers, barriers, opportunities, and challenges. This information empowers stakeholders to stay abreast of market trends and make data-driven decisions to capitalize on growth prospects.
  • The report can aid businesses in identifying future opportunities in any sector. It also helps in understanding if those opportunities are worth pursuing.
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TABLE OF CONTENTS

1. PREFACE

  • 1.1. Introduction
  • 1.2. Market Share Insights
  • 1.3. Key Market Insights
  • 1.4. Report Coverage
  • 1.5. Key Questions Answered

2. RESEARCH METHODOLOGY

  • 2.1. Chapter Overview
  • 2.2. Research Assumptions
    • 2.2.1. Market Landscape and Market Trends
    • 2.2.2. Market Forecast and Opportunity Analysis
    • 2.2.3. Comparative Analysis
  • 2.3. Database Building
    • 2.3.1. Data Collection
    • 2.3.2. Data Validation
    • 2.3.3. Data Analysis
  • 2.4. Project Methodology
    • 2.4.1. Secondary Research
      • 2.4.1.1. Annual Reports
      • 2.4.1.2. Academic Research Papers
      • 2.4.1.3. Company Websites
      • 2.4.1.4. Investor Presentations
      • 2.4.1.5. Regulatory Filings
      • 2.4.1.6. White Papers
      • 2.4.1.7. Industry Publications
      • 2.4.1.8. Conferences and Seminars
      • 2.4.1.9. Government Portals
      • 2.4.1.10. Media and Press Releases
      • 2.4.1.11. Newsletters
      • 2.4.1.12. Industry Databases
      • 2.4.1.13. Roots Proprietary Databases
      • 2.4.1.14. Paid Databases and Sources
      • 2.4.1.15. Social Media Portals
      • 2.4.1.16. Other Secondary Sources
    • 2.4.2. Primary Research
      • 2.4.2.1. Types of Primary Research
        • 2.4.2.1.1. Qualitative Research
        • 2.4.2.1.2. Quantitative Research
        • 2.4.2.1.3. Hybrid Approach
      • 2.4.2.2. Advantages of Primary Research
      • 2.4.2.3. Techniques for Primary Research
        • 2.4.2.3.1. Interviews
        • 2.4.2.3.2. Surveys
        • 2.4.2.3.3. Focus Groups
        • 2.4.2.3.4. Observational Research
        • 2.4.2.3.5. Social Media Interactions
      • 2.4.2.4. Key Opinion Leaders Considered in Primary Research
        • 2.4.2.4.1. Company Executives (CXOs)
        • 2.4.2.4.2. Board of Directors
        • 2.4.2.4.3. Company Presidents and Vice Presidents
        • 2.4.2.4.4. Research and Development Heads
        • 2.4.2.4.5. Technical Experts
        • 2.4.2.4.6. Subject Matter Experts
        • 2.4.2.4.7. Scientists
        • 2.4.2.4.8. Doctors and Other Healthcare Providers
      • 2.4.2.5. Ethics and Integrity
        • 2.4.2.5.1. Research Ethics
        • 2.4.2.5.2. Data Integrity
    • 2.4.3. Analytical Tools and Databases
  • 2.5. Robust Quality Control

3. MARKET DYNAMICS

  • 3.1. Chapter Overview
  • 3.2. Forecast Methodology
    • 3.2.1. Top-down Approach
    • 3.2.2. Bottom-up Approach
    • 3.2.3. Hybrid Approach
  • 3.3. Market Assessment Framework
    • 3.3.1. Total Addressable Market (TAM)
    • 3.3.2. Serviceable Addressable Market (SAM)
    • 3.3.3. Serviceable Obtainable Market (SOM)
    • 3.3.4. Currently Acquired Market (CAM)
  • 3.4. Forecasting Tools and Techniques
    • 3.4.1. Qualitative Forecasting
    • 3.4.2. Correlation
    • 3.4.3. Regression
    • 3.4.4. Extrapolation
    • 3.4.5. Convergence
    • 3.4.6. Sensitivity Analysis
    • 3.4.7. Scenario Planning
    • 3.4.8. Data Visualization
    • 3.4.9. Time Series Analysis
    • 3.4.10. Forecast Error Analysis
  • 3.5. Key Considerations
    • 3.5.1. Demographics
    • 3.5.2. Government Regulations
    • 3.5.3. Reimbursement Scenarios
    • 3.5.4. Market Access
    • 3.5.5. Supply Chain
    • 3.5.6. Industry Consolidation
    • 3.5.7. Pandemic / Unforeseen Disruptions Impact
  • 3.6. Limitations

4. MACRO-ECONOMIC INDICATORS

  • 4.1. Chapter Overview
  • 4.2. Market Dynamics
    • 4.2.1. Time Period
      • 4.2.1.1. Historical Trends
      • 4.2.1.2. Current and Forecasted Estimates
    • 4.2.2. Currency Coverage
      • 4.2.2.1. Major Currencies Affecting the Market
      • 4.2.2.2. Factors Affecting Currency Fluctuations on the Industry
      • 4.2.2.3. Impact of Currency Fluctuations on the Industry
    • 4.2.3. Foreign Currency Exchange Rate
      • 4.2.3.1. Impact of Foreign Exchange Rate Volatility on the Market
      • 4.2.3.2. Strategies for Mitigating Foreign Exchange Risk
    • 4.2.4. Recession
      • 4.2.4.1. Assessment of Current Economic Conditions and Potential Impact on the Market
      • 4.2.4.2. Historical Analysis of Past Recessions and Lessons Learnt
    • 4.2.5. Inflation
      • 4.2.5.1. Measurement and Analysis of Inflationary Pressures in the Economy
      • 4.2.5.2. Potential Impact of Inflation on the Market Evolution
    • 4.2.6. Interest Rates
      • 4.2.6.1. Interest Rates and Their Impact on the Market
      • 4.2.6.2. Strategies for Managing Interest Rate Risk
    • 4.2.7. Commodity Flow Analysis
      • 4.2.7.1. Type of Commodity
      • 4.2.7.2. Origins and Destinations
      • 4.2.7.3. Values and Weights
      • 4.2.7.4. Modes of Transportation
    • 4.2.8. Global Trade Dynamics
      • 4.2.8.1. Import Scenario
      • 4.2.8.2. Export Scenario
      • 4.2.8.3. Trade Policies
      • 4.2.8.4. Strategies for Mitigating the Risks Associated with Trade Barriers
      • 4.2.8.5. Impact of Trade Barriers on the Market
    • 4.2.9. War Impact Analysis
      • 4.2.9.1. Russian-Ukraine War
      • 4.2.9.2. Israel-Hamas War
    • 4.2.10. COVID Impact / Related Factors
      • 4.2.10.1. Global Economic Impact
      • 4.2.10.2. Industry-specific Impact
      • 4.2.10.3. Government Response and Stimulus Measures
      • 4.2.10.4. Future Outlook and Adaptation Strategies
    • 4.2.11. Other Indicators
      • 4.2.11.1. Fiscal Policy
      • 4.2.11.2. Consumer Spending
      • 4.2.11.3. Gross Domestic Product
      • 4.2.11.4. Employment
      • 4.2.11.5. Taxes
      • 4.2.11.6. Stock Market Performance
      • 4.2.11.7. Cross Border Dynamics
  • 4.3. Conclusion

5. EXECUTIVE SUMMARY

  • 5.1. AI-based Drug Discovery: Market Landscape
  • 5.2. AI-based Drug Discovery: Market Trends
  • 5.3. AI-based Drug Discovery: Market Forecast and Opportunity Analysis

6. INTRODUCTION

  • 6.1. Chapter Overview
  • 6.2. Artificial Intelligence
  • 6.3. Subsets of AI
    • 6.3.1. Machine Learning
      • 6.3.1.1. Supervised Learning
      • 6.3.1.2. Unsupervised Learning
      • 6.3.1.3. Reinforced / Reinforcement Learning
      • 6.3.1.4. Deep Learning
      • 6.3.1.5. Large Language Models (LLMs)
      • 6.3.1.6. Natural Language Processing
      • 6.3.1.7. Generative AI
      • 6.3.1.8. Computer Vision
  • 6.4. Applications of AI in Healthcare
    • 6.4.1. Drug Discovery
    • 6.4.2. Disease Prediction, Diagnosis and Treatment
    • 6.4.3. Manufacturing and Supply Chain Operations
    • 6.4.4. Drug Marketing
    • 6.4.5. Clinical Trials
  • 6.5. AI in Drug Discovery
    • 6.5.1. Target Identification
    • 6.5.2. Identification of Hit or Lead
    • 6.5.3. Lead Optimization
  • 6.6. Advantages of Using AI in Drug Discovery Process
  • 6.7. Challenges Associated with the Adoption of AI
  • 6.8. Future Perspective

7. MARKET LANDSCAPE

  • 7.1. Chapter Overview
  • 7.2. AI-based Drug Discovery Platform Providers: Overall Market Landscape
    • 7.2.1. Analysis by Year of Establishment
    • 7.2.2. Analysis by Company Size
    • 7.2.3. Analysis by Location of Headquarters
  • 7.3. Key AI-based Drug Discovery Platform Providers: Market Landscape
    • 7.3.1. Analysis by Type of Business Model
    • 7.3.2. Analysis by Platform Utilization
    • 7.3.3. Analysis by Drug Discovery Stages Supported
    • 7.3.4. Analysis by Type of AI Technology Used
    • 7.3.5. Analysis by Target Therapeutic Area
    • 7.3.6. Analysis by Type of Molecule Analyzed
    • 7.3.7. Analysis by End User

8. COMPANY PROFILES: AI-BASED DRUG DISCOVERY PLATFORM PROVIDERS IN NORTH AMERICA

  • 8.1. Chapter Overview
  • 8.2. Collaborations Pharmaceuticals
    • 8.2.1. Company Overview
    • 8.2.2. AI-based Drug Discovery Platform / Technology Portfolio
    • 8.2.3. Recent Developments and Future Outlook
  • 8.3. Deep Genomics
    • 8.3.1. Company Overview
    • 8.3.2. AI-based Drug Discovery Platform / Technology Portfolio
    • 8.3.3. Recent Developments and Future Outlook
  • 8.4. Genialis
    • 8.4.1. Company Overview
    • 8.4.2. AI-based Drug Discovery Platform / Technology Portfolio
    • 8.4.3. Recent Developments and Future Outlook
  • 8.5. Insilico Medicine
    • 8.5.1. Company Overview
    • 8.5.2. AI-based Drug Discovery Platform / Technology Portfolio
    • 8.5.3. Recent Developments and Future Outlook
  • 8.6. XtalPi
    • 8.6.1. Company Overview
    • 8.6.2. AI-based Drug Discovery Platform / Technology Portfolio
    • 8.6.3. Recent Developments and Future Outlook

9. COMPANY PROFILES: AI-BASED DRUG DISCOVERY PLATFORM PROVIDERS IN EUROPE

  • 9.1. Chapter Overview
  • 9.2. BenevolentAI
    • 9.2.1. Company Overview
    • 9.2.2. AI-based Drug Discovery Platform / Technology Portfolio
    • 9.2.3. Recent Developments and Future Outlook
  • 9.3. Healx
    • 9.3.1. Company Overview
    • 9.3.2. AI-based Drug Discovery Platform / Technology Portfolio
    • 9.3.3. Recent Developments and Future Outlook
  • 9.4. Iktos
    • 9.4.1. Company Overview
    • 9.4.2. AI-based Drug Discovery Platform / Technology Portfolio
    • 9.4.3. Recent Developments and Future Outlook
  • 9.5. Optibrium
    • 9.5.1. Company Overview
    • 9.5.2. AI-based Drug Discovery Platform / Technology Portfolio
    • 9.5.3. Recent Developments and Future Outlook

10. COMPANY PROFILES: AI-BASED DRUG DISCOVERY PLATFORM PROVIDERS IN ASIA-PACIFIC AND REST OF WORLD

  • 10.1. Chapter Overview
  • 10.2. CytoReason
    • 10.2.1. Company Overview
    • 10.2.2. AI-based Drug Discovery Platform / Technology Portfolio
    • 10.2.3. Recent Developments and Future Outlook
  • 10.3. Deargen
    • 10.3.1. Company Overview
    • 10.3.2. AI-based Drug Discovery Platform / Technology Portfolio
    • 10.3.3. Recent Developments and Future Outlook

11. PARTNERSHIPS AND COLLABORATIONS

  • 11.1. Chapter Overview
  • 11.2. Partnership Models
  • 11.3. AI-based Drug Discovery: Partnerships and Collaborations
    • 11.3.1. Quarterly Trend of Partnerships
    • 11.3.2. Analysis by Type of Partnership
    • 11.3.3. Analysis by Quarter and Type of Partnership
    • 11.3.4. Analysis by Type of Partner
    • 11.3.5. Most Active Players: Analysis by Number of Partnerships
    • 11.3.6. Analysis by Geography
      • 11.3.6.1. Intercontinental and Intracontinental Deals
      • 11.3.6.2. International and Local Deals

12. FUNDING AND INVESTMENTS ANALYSIS

  • 12.1. Chapter Overview
  • 12.2. Funding Models
  • 12.3. AI-based Drug Discovery: Funding and Investments
    • 12.3.1. Quarterly Trend of Funding
    • 12.3.2. Quarterly Trend of Amount Invested
    • 12.3.3. Analysis of Funding Instances by Type of Funding
    • 12.3.4. Analysis of Funding Instances by Quarter and Type of Funding
    • 12.3.5. Analysis of Amount Invested by Type of Funding
    • 12.3.6. Analysis of Amount Invested by Quarter and Type of Funding
    • 12.3.7. Analysis by Geography
    • 12.3.8. Most Active Players: Analysis by Number of Funding Instances
    • 12.3.9. Most Active Players: Analysis by Amount Raised
    • 12.3.10. Leading Investors: Distribution by Number of Funding Instances

13. PATENT ANALYSIS

  • 13.1. Chapter Overview
  • 13.2. Scope And Methodology
  • 13.3. AI-based Drug Discovery: Patent Analysis
    • 13.3.1. Analysis by Patent Publication Year
    • 13.3.2. Analysis by Type of Patent and Publication Year
    • 13.3.3. Analysis by Patent Application Year
    • 13.3.4. Analysis by Patent Jurisdiction
    • 13.3.5. Analysis by CPC Symbols
    • 13.3.6. Analysis by Type of Applicant
    • 13.3.7. Leading Industry Players: Analysis by Number of Patents
    • 13.3.8. Leading Non-Industry Players: Analysis by Number of Patents
    • 13.3.9. Leading Individual Assignees: Analysis by Number of Patents
  • 13.4. Patent Benchmarking Analysis
    • 13.4.1. Analysis by Patent Characteristics
  • 13.5. Patent Valuation
  • 13.6. Leading Patents by Number of Citations

14. PORTER'S FIVE FORCES ANALYSIS

  • 14.1. Chapter Overview
  • 14.2. Methodology and Assumptions
  • 14.3. Key Elements of Porter's Five Forces
  • 14.4. Threat of New Entrants
  • 14.5. Bargaining Power of Buyers
  • 14.6. Bargaining Power of Solution Providers
  • 14.7. Threats of Substitute Products
  • 14.8. Rivalry Among Existing Competitors
  • 14.9. Concluding Remarks

15. COMPANY VALUATION ANALYSIS

  • 15.1. Chapter Overview
  • 15.2. Company Valuation Analysis: Key Parameters
  • 15.3. Methodology
  • 15.4. Company Valuation Analysis: Roots Analysis Proprietary Scores

16. AI-BASED HEALTHCARE INITIATIVES OF TECHNOLOGY GIANTS

  • 16.1. Chapter Overview
  • 16.2. Alibaba Cloud
  • 16.3. Google
  • 16.4. IBM
  • 16.5. Intel
  • 16.6. Microsoft
  • 16.7. Siemens

17. COST SAVING ANALYSIS

  • 17.1. Chapter Overview
  • 17.2. Key Assumptions and Methodology
  • 17.3. Overall Cost Saving Potential Associated with Use of AI-based Drug Discovery Platforms, Till 2035
    • 17.3.1. Cost Saving Potential: Distribution by Drug Discovery Steps
      • 17.3.1.1. Cost Saving Potential in Target Identification / Validation, Till 2035
      • 17.3.1.2. Cost Saving Potential in Hit Generation / Lead Identification and Optimization, Till 2035
    • 17.3.2. Cost Saving Potential: Distribution by Type of AI Technology
      • 17.3.2.1. Cost Saving Potential with Machine Learning, Till 2035
      • 17.3.2.2. Cost Saving Potential with Molecular Modelling and Simulation, Till 2035
      • 17.3.2.3. Cost Saving Potential with Deep Learning, Till 2035
      • 17.3.2.4. Cost Saving Potential with Omics Integration, Till 2035
      • 17.3.2.5. Cost Saving Potential with Generative Model, Till 2035
      • 17.3.2.6. Cost Saving Potential with Structure-based Drug Design, Till 2035
      • 17.3.2.7. Cost Saving Potential with Other Technologies, Till 2035
    • 17.3.3. Cost Saving Potential: Distribution by Therapeutic Area, Till 2035
      • 17.3.3.1. Cost Saving Potential in Drug Discovery for Oncological Disorders, Till 2035
      • 17.3.3.2. Cost Saving Potential in Drug Discovery for Cardiovascular Disorders, Till 2035
      • 17.3.3.3. Cost Saving Potential in Drug Discovery for Musculoskeletal Disorders, Till 2035
      • 17.3.3.4. Cost Saving Potential in Drug Discovery for Neurological Disorders, Till 2035
      • 17.3.3.5. Cost Saving Potential in Drug Discovery for Respiratory Disorders, Till 2035
      • 17.3.3.6. Cost Saving Potential in Drug Discovery for Immunological Disorders, Till 2035
      • 17.3.3.7. Cost Saving Potential in Drug Discovery for Gastrointestinal Disorders, Till 2035
      • 17.3.3.8. Cost Saving Potential in Drug Discovery for Endocrine Disorders, Till 2035
      • 17.3.3.9. Cost Saving Potential in Drug Discovery for Ophthalmological Disorders, Till 2035
      • 17.3.3.10. Cost Saving Potential in Drug Discovery for Blood Disorders, Till 2035
      • 17.3.3.11. Cost Saving Potential in Drug Discovery for Dermatological Disorders, Till 2035
      • 17.3.3.12. Cost Saving Potential in Drug Discovery for Infectious Diseases, Till 2035
      • 17.3.3.13. Cost Saving Potential in Drug Discovery for Urinary Disorders, Till 2035
    • 17.3.4. Cost Saving Potential: Distribution by End User
      • 17.3.4.1. Cost Saving Potential for Pharma and Biotech Companies, Till 2035
      • 17.3.4.2. Cost Saving Potential for Contract Research Organizations (CROs), Till 2035
      • 17.3.4.3. Cost Saving Potential for Research and Academic Institutions, Till 2035
    • 17.3.5. Cost Saving Potential: Distribution by Geographical Regions
      • 17.3.5.1. Cost Saving Potential in North America, Till 2035
      • 17.3.5.2. Cost Saving Potential in Europe, Till 2035
      • 17.3.5.3. Cost Saving Potential in Asia-Pacific, Till 2035
      • 17.3.5.4. Cost Saving Potential in MENA, Till 2035
      • 17.3.5.5. Cost Saving Potential in Latin America, Till 2035
  • 17.4. Conclusion

18. MARKET IMPACT ANALYSIS: DRIVERS, RESTRAINTS, OPPORTUNITIES AND CHALLENGES

  • 18.1. Chapter Overview
  • 18.2. Market Drivers
  • 18.3. Market Restraints
  • 18.4. Market Opportunities
  • 18.5. Market Challenges
  • 18.6. Conclusion

19. GLOBAL AI-BASED DRUG DISCOVERY MARKET

  • 19.1. Chapter Overview
  • 19.2. Key Assumptions and Methodology
  • 19.3. Global AI-based Drug Discovery Market, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 19.3.1. Scenario Analysis
      • 19.3.1.1. Conservative Scenario
      • 19.3.1.2. Optimistic Scenario
  • 19.4. Key Market Segmentations

20. AI-BASED DRUG DISCOVERY MARKET, BY DRUG DISCOVERY STEP

  • 20.1. Chapter Overview
  • 20.2. Key Assumptions and Methodology
  • 20.3. AI-based Drug Discovery Market: Distribution by Drug Discovery Step
    • 20.3.1. AI-based Drug Discovery Market for Target Identification / Validation, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 20.3.2. AI-based Drug Discovery Market for Hit Generation / Lead Identification, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 20.3.3. AI-based Drug Discovery Market for Lead Optimization, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
  • 20.4. Data Triangulation and Validation

21. AI-BASED DRUG DISCOVERY MARKET, BY TYPE OF AI TECHNOLOGY

  • 21.1. Chapter Overview
  • 21.2. Key Assumptions and Methodology
    • 21.3.1. AI-based Drug Discovery Market for Machine Learning, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 21.3.2. AI-based Drug Discovery Market for Molecular Modelling and Simulation, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 21.3.3. AI-based Drug Discovery Market for Deep Learning, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 21.3.4. AI-based Drug Discovery Market for Omics Integration, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 21.3.5. AI-based Drug Discovery Market for Generative Models, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 21.3.6. AI-based Drug Discovery Market for Structure-Based Drug Design, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 21.3.7. AI-based Drug Discovery Market for Others, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
  • 21.4. Data Triangulation and Validation

22. AI-BASED DRUG DISCOVERY MARKET, BY THERAPEUTIC AREA

  • 22.1. Chapter Overview
  • 22.2. Key Assumptions and Methodology
  • 22.3. AI-based Drug Discovery Market: Distribution by Therapeutic Area
    • 22.3.1. AI-based Drug Discovery Market for Oncological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 22.3.2. AI-based Drug Discovery Market for Cardiovascular Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 22.3.3. AI-based Drug Discovery Market for Musculoskeletal Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 22.3.4. AI-based Drug Discovery Market for Neurological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 22.3.5. AI-based Drug Discovery Market for Respiratory Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 22.3.6. AI-based Drug Discovery Market for Immunological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 22.3.7. AI-based Drug Discovery Market for Gastrointestinal Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 22.3.8. AI-based Drug Discovery Market for Endocrine Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 22.3.9. AI-based Drug Discovery Market for Blood Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 22.3.10. AI-based Drug Discovery Market for Ophthalmological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 22.3.11. AI-based Drug Discovery Market for Dermatological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 22.3.12. AI-based Drug Discovery Market for Infectious Diseases, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 22.3.13. AI-based Drug Discovery Market for Urinary Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
  • 22.4. Data Triangulation and Validation

23. AI-BASED DRUG DISCOVERY MARKET, BY END USER

  • 23.1. Chapter Overview
  • 23.2. Key Assumptions and Methodology
  • 23.3. AI-based Drug Discovery Market: Distribution by End User
    • 23.3.1. AI-based Drug Discovery Market for Pharma and Biotech Companies, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 23.3.2. AI-based Drug Discovery Market for Contract Research Organizations, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 23.3.3. AI-based Drug Discovery Market for Research and Academic Institutions, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
  • 23.4. Data Triangulation and Validation

24. AI-BASED DRUG DISCOVERY MARKET, BY GEOGRAPHICAL REGIONS

  • 23.1. Chapter Overview
  • 23.2. Key Assumptions and Methodology
  • 24.3. AI-based Drug Discovery Market: Distribution by Geographical Regions
    • 24.3.1. AI-based Drug Discovery Market in North America, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.1.1. AI-based Drug Discovery Market in the US, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.1.2. AI-based Drug Discovery Market in Canada, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 24.3.2. AI-based Drug Discovery Market in Europe, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.2.1. AI-based Drug Discovery Market in the UK, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.2.2. AI-based Drug Discovery Market in Germany, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.2.3. AI-based Drug Discovery Market in France, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.2.4. AI-based Drug Discovery Market in Spain, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.2.5. AI-based Drug Discovery Market in Italy, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.2.6. AI-based Drug Discovery Market in Rest of Europe, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 24.3.3. AI-based Drug Discovery Market in Asia-Pacific, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.3.1. AI-based Drug Discovery Market in China, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.3.2. AI-based Drug Discovery Market in Japan, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.3.3. AI-based Drug Discovery Market in South Korea, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.3.4. AI-based Drug Discovery Market in Australia, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.3.5. AI-based Drug Discovery Market in India, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 24.3.4. AI-based Drug Discovery Market in MENA, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.4.1. AI-based Drug Discovery Market in Saudi Arabia, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.4.2. AI-based Drug Discovery Market in UAE, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.4.3. AI-based Drug Discovery Market in Egypt, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
    • 24.3.5. AI-based Drug Discovery Market in Latin America, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.5.1. AI-based Drug Discovery Market in Brazil, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.5.2. AI-based Drug Discovery Market in Mexico, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
      • 24.3.5.3. AI-based Drug Discovery Market in Argentina, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035)
  • 24.4. Market Dynamics Assessment
    • 24.4.1. Market Movement Analysis
    • 24.4.2. Penetration-Growth (P-G) Matrix
  • 24.5. Data Triangulation and Validation

25. CONCLUDING REMARKS

26. EXECUTIVE INSIGHTS

  • 26.1. Chapter Overview
  • 26.2. Company A
    • 26.2.1. Company Snapshot
    • 26.2.2. Interview Transcript
  • 26.3. Company B
    • 26.3.1. Company Snapshot
    • 26.3.2. Interview Transcript
  • 26.4. Company C
    • 26.4.1. Company Snapshot
    • 26.4.2. Interview Transcript
  • 26.5. Company D
    • 26.5.1. Company Snapshot
    • 26.5.2. Interview Transcript
  • 26.6. Company E
    • 26.6.1. Company Snapshot
    • 26.6.2. Interview Transcript
  • 26.7. Company F
    • 26.7.1. Company Snapshot
    • 26.7.2. Interview Transcript
  • 26.8. Company G
    • 26.8.1. Company Snapshot
    • 26.8.2. Interview Transcript

27. APPENDIX I: TABULATED DATA

28. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS

List of Tables

  • Table 7.1 AI-based Drug Discovery Platform Providers: Information on Year of Establishment, Company Size, and Location of Headquarters
  • Table 7.2 Key AI-based Drug Discovery Platform Providers: Information on Platform, Type of Business Model, Platform Utilization and Drug Discovery Stages Supported
  • Table 7.3 Key AI-based Drug Discovery Platform Providers: Information on Type of AI Technology Used
  • Table 7.4 Key AI-based Drug Discovery Platform Providers: Information on Target Therapeutic Area
  • Table 7.5 Key AI-based Drug Discovery Platform Providers: Information on Type of Molecule Analyzed and End User
  • Table 8.1 AI-based Drug Discovery Platform Providers based in North America: List of Companies Profiled
  • Table 8.2 Collaborations Pharmaceuticals: Company Overview
  • Table 8.3 Collaborations Pharmaceuticals: AI-based Drug Discovery Platform / Technology Portfolio
  • Table 8.4 Collaborations Pharmaceuticals: Recent Developments and Future Outlook
  • Table 8.5 Deep Genomics: Company Overview
  • Table 8.6 Deep Genomics: AI-based Drug Discovery Platform / Technology Portfolio
  • Table 8.7 Deep Genomics: Recent Developments and Future Outlook
  • Table 8.8 Genialis: Company Overview
  • Table 8.9 Genialis: AI-based Drug Discovery Platform / Technology Portfolio
  • Table 8.10 Genialis: Recent Developments and Future Outlook
  • Table 8.11 Insilico Medicine: Company Overview
  • Table 8.12 Insilico Medicine: AI-based Drug Discovery Platform / Technology Portfolio
  • Table 8.13 Insilico Medicine: Recent Developments and Future Outlook
  • Table 8.14 XtalPi: Company Overview
  • Table 8.15 XtalPi: AI-based Drug Discovery Platform / Technology Portfolio
  • Table 8.16 XtalPi: Recent Developments and Future Outlook
  • Table 9.1 AI-based Drug Discovery Platform Providers based in Europe: List of Companies Profiled
  • Table 9.2 BenevolentAI: Company Overview
  • Table 9.3 BenevolentAI: AI-based Drug Discovery Platform / Technology Portfolio
  • Table 9.4 Healx: Company Overview
  • Table 9.5 Healx: AI-based Drug Discovery Platform / Technology Portfolio
  • Table 9.6 Healx: Recent Developments and Future Outlook
  • Table 9.7 Iktos: Company Overview
  • Table 9.8 Iktos: AI-based Drug Discovery Platform / Technology Portfolio
  • Table 9.9 Iktos: Recent Developments and Future Outlook
  • Table 9.10 Optibrium: Company Overview
  • Table 9.11 Optibrium: AI-based Drug Discovery Platform / Technology Portfolio
  • Table 9.12 Optibrium: Recent Developments and Future Outlook
  • Table 10.1 AI-based Drug Discovery Platform Providers based in Asia-Pacific and Rest of World: List of Companies Profiled
  • Table 10.2 CytoReason: Company Overview
  • Table 10.3 CytoReason: AI-based Drug Discovery Platform / Technology Portfolio
  • Table 10.4 Deargen: Company Overview
  • Table 10.5 Deargen: AI-based Drug Discovery Platform / Technology Portfolio
  • Table 11.1 AI-based Drug Discovery: List of Partnerships and Collaborations
  • Table 12.1 AI-based Drug Discovery Market: List of Funding and Investments
  • Table 13.1 Patent Analysis CPC Sections
  • Table 13.2 Patent Analysis Top CPC Symbols
  • Table 13.3 Patent Analysis Top CPC Codes
  • Table 13.4 Patent Analysis Summary of Benchmarking Analysis
  • Table 13.5 Patent Analysis Categorization based on Weighted Valuation Scores
  • Table 13.6 Patent Portfolio List of Leading Patents by Highest Relative Valuation
  • Table 13.7 Patent Portfolio List of Leading Patents by Number of Citations
  • Table 15.1 Company Valuation Analysis: Scoring Sheet
  • Table 15.2 Company Valuation Analysis: Estimated Valuation
  • Table 18.1 Growth Drivers: Market Impact and Time Period
  • Table 18.2 Growth Restraints: Market Impact and Time Period
  • Table 18.3 Growth Opportunities: Market Impact and Time Period
  • Table 18.4 Growth Challenges: Market Impact and Time Period
  • Table 26.1 Aigenpulse: Company Snapshot
  • Table 26.2 Cloud Pharmaceuticals: Company Snapshot
  • Table 26.3 Deargen: Company Snapshot
  • Table 26.4 Intelligent Omics: Company Snapshot
  • Table 26.5 Optibrium: Company Snapshot
  • Table 26.6 Pepticom: Company Snapshot
  • Table 26.7 Sage-N Research: Company Snapshot
  • Table 27.1 AI-based Drug Discovery Platform Providers: Distribution by Year of Establishment
  • Table 27.2 AI-based Drug Discovery Platform Providers: Distribution by Company Size
  • Table 27.3 AI-based Drug Discovery Platform Providers: Distribution by Location of Headquarters (Region)
  • Table 27.4 AI-based Drug Discovery Platform Providers: Distribution by Location of Headquarters (Country)
  • Table 27.5 Key AI-based Drug Discovery Platform Providers: Distribution by Type of Business Model
  • Table 27.6 Key AI-based Drug Discovery Platform Providers: Distribution by Platform Utilization
  • Table 27.7 Key AI-based Drug Discovery Platform Providers: Distribution by Drug Discovery Stages Supported
  • Table 27.8 Key AI-based Drug Discovery Platform Providers: Distribution by Type of AI Technology Used
  • Table 27.9 Key AI-based Drug Discovery Platform Providers: Distribution by Target Therapeutic Area
  • Table 27.10 Key AI-based Drug Discovery Platform Providers: Distribution by Type of Molecule Analyzed
  • Table 27.11 Key AI-based Drug Discovery Platform Providers: Distribution by End User
  • Table 27.12 Partnerships and Collaborations: Quarterly Trend of Partnerships
  • Table 27.13 Partnerships and Collaborations: Distribution by Type of Partnership
  • Table 27.14 Partnerships and Collaborations: Distribution by Quarter and Type of Partnership
  • Table 27.15 Partnerships and Collaborations: Distribution by Type of Partner
  • Table 27.16 Most Active Players: Distribution by Number of Partnerships
  • Table 27.17 Most Active Players: Distribution by Number of Partnerships
  • Table 27.18 Partnerships and Collaborations: Local and International Agreements
  • Table 27.19 Partnerships and Collaborations: Intracontinental and Intercontinental Agreements
  • Table 27.20 Funding and Investments Analysis: Quarterly Trend of Funding
  • Table 27.21 Funding and Investments Analysis: Quarterly Trend of Amount Invested (USD Million)
  • Table 27.22 Funding and Investments: Distribution of Instances by Type of Funding
  • Table 27.23 Funding and Investments: Distribution by Quarter and Type of Funding (USD Million)
  • Table 27.24 Funding and Investments: Distribution of Amount Invested by Type of Funding (USD Million)
  • Table 27.25 Funding and Investment Analysis: Distribution of Amount Invested by Quarter and Type of Funding
  • Table 27.26 Funding and Investments: Distribution by Geography (Region)
  • Table 27.27 Funding and Investments: Distribution by Geography (Country)
  • Table 27.28 Most Active Players: Distribution by Number of Funding Instances
  • Table 27.29 Most Active Players: Distribution by Amount Raised (USD Million)
  • Table 27.30 Leading Investors: Distribution by Number of Funding Instances
  • Table 27.31 Patent Analysis: Distribution by Type of Patent
  • Table 27.32 Patent Analysis: Cumulative Distribution by Patent Publication Year
  • Table 27.33 Patent Analysis: Distribution by Type of Patent and Patent Publication Year
  • Table 27.34 Patent Analysis: Distribution by Patent Application Year, 2018-2025
  • Table 27.35 Patent Analysis: Distribution by Patent Jurisdiction
  • Table 27.36 Patent Analysis: Cumulative Year-wise Distribution by Type of Applicant
  • Table 27.37 Leading Industry Players: Distribution by Number of Patents
  • Table 27.38 Leading Non-Industry Players: Distribution by Number of Patents
  • Table 27.39 Leading Individual Assignees: Distribution by Number of Patents
  • Table 27.40 Patent Analysis: Distribution by Patent Age
  • Table 27.41 Patent Analysis: Distribution by Patent Valuation
  • Table 27.42 Overall Cost Saving Potential Associated with Use of AI-based Drug Discovery Platforms, Till 2035 (USD Billion)
  • Table 27.43 Cost Saving Potential: Distribution by Drug Discovery Steps (USD Billion)
  • Table 27.44 Cost Saving Potential in Target Identification/Validation, Till 2035 (USD Billion)
  • Table 27.45 Cost Saving Potential in Hit Generation/Lead Identification and Optimization, Till 2035 (USD Billion)
  • Table 27.46 Cost Saving Potential in Hit Generation/Lead Identification and Optimization, Till 2035 (USD Billion)
  • Table 27.47 Cost Saving Potential: Distribution by Type of AI Technology (USD Billion)
  • Table 27.48 Cost Saving Potential with Machine Learning, Till 2035 (USD Billion)
  • Table 27.49 Cost Saving Potential with Molecular Modelling and Simulation, Till 2035 (USD Billion)
  • Table 27.50 Cost Saving Potential with Deep Learning, Till 2035 (USD Billion)
  • Table 27.51 Cost Saving Potential with Omics Integration, Till 2035 (USD Billion)
  • Table 27.52 Cost Saving Potential with Generative Model, Till 2035 (USD Billion)
  • Table 27.53 Cost Saving Potential with Structure-Based Drug Design, Till 2035 (USD Billion)
  • Table 27.54 Cost Saving Potential with Other Technologies, Till 2035 (USD Billion)
  • Table 27.55 Cost Saving Potential: Distribution by Therapeutic Area (USD Billion)
  • Table 27.56 Cost Saving Potential in Drug Discovery for Oncological Disorders, Till 2035 (USD Billion)
  • Table 27.57 Cost Saving Potential in Drug Discovery for Cardiovascular Disorders, Till 2035 (USD Billion)
  • Table 27.58 Cost Saving Potential in Drug Discovery for Musculoskeletal Disorders, Till 2035 (USD Billion)
  • Table 27.59 Cost Saving Potential in Drug Discovery for Neurological Disorders, Till 2035 (USD Billion)
  • Table 27.60 Cost Saving Potential in Drug Discovery for Respiratory Disorders, Till 2035 (USD Billion)
  • Table 27.61 Cost Saving Potential in Drug Discovery for Immunological Disorders, Till 2035 (USD Billion)
  • Table 27.62 Cost Saving Potential in Drug Discovery for Gastrointestinal Disorders, Till 2035 (USD Billion)
  • Table 27.63 Cost Saving Potential in Drug Discovery for Endocrine Disorders, Till 2035 (USD Billion)
  • Table 27.64 Cost Saving Potential in Drug Discovery for Ophthalmological Disorders, Till 2035 (USD Billion)
  • Table 27.65 Cost Saving Potential in Drug Discovery for Blood Disorders, Till 2035 (USD Billion)
  • Table 27.66 Cost Saving Potential in Drug Discovery for Dermatological Disorders, Till 2035 (USD Billion)
  • Table 27.67 Cost Saving Potential in Drug Discovery for Infectious Diseases, Till 2035 (USD Billion)
  • Table 27.68 Cost Saving Potential in Drug Discovery for Urinary Disorders, Till 2035 (USD Billion)
  • Table 27.69 Cost Saving Potential: Distribution by End User (USD Billion)
  • Table 27.70 Cost Saving Potential for Pharma and Biotech Companies, Till 2035 (USD Billion)
  • Table 27.71 Cost Saving Potential for Contract Research Organizations (CRO), Till 2035 (USD Billion)
  • Table 27.72 Cost Saving Potential for Research and Academic Institutions, Till 2035 (USD Billion)
  • Table 27.73 Cost Saving Potential: Distribution by Geographical Regions (USD Billion)
  • Table 27.74 Cost Saving Potential in North America, Till 2035 (USD Billion)
  • Table 27.75 Cost Saving Potential in Europe, Till 2035 (USD Billion)
  • Table 27.76 Cost Saving Potential in Asia-Pacific, Till 2035 (USD Billion)
  • Table 27.77 Cost Saving Potential in MENA, Till 2035 (USD Billion)
  • Table 27.78 Cost Saving Potential in Latin America, Till 2035 (USD Billion)
  • Table 27.79 Global AI-based Drug Discovery Market, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.80 Global AI-based Drug Discovery Market, Forecasted Estimates (Till 2035), Conservative Scenario (USD Million)
  • Table 27.81 Global AI-based Drug Discovery Market, Forecasted Estimates (Till 2035), Optimistic Scenario (USD Million)
  • Table 27.82 AI-based Drug Discovery Market: Distribution by Drug Discovery Steps
  • Table 27.83 AI-based Drug Discovery Market for Target Identification/Validation, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.84 AI-based Drug Discovery Market for Hit Generation/Lead Identification, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.85 AI-based Drug Discovery Market for Lead Optimization, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.86 AI-based Drug Discovery Market: Distribution by Type of AI Technology
  • Table 27.87 AI-based Drug Discovery Market for Machine Learning, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.88 AI-based Drug Discovery Market for Molecular Modelling and Simulation, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.89 AI-based Drug Discovery Market for Deep Learning, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.90 AI-based Drug Discovery Market for Omics Integration, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.91 AI-based Drug Discovery Market for Generative Models, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.92 AI-based Drug Discovery Market for Structure-Based Drug Design, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.93 AI-based Drug Discovery Market for Others, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.94 AI-based Drug Discovery Market: Distribution by Therapeutic Area
  • Table 27.95 AI-based Drug Discovery Market for Oncological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.96 AI-based Drug Discovery Market for Cardiovascular Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.97 AI-based Drug Discovery Market for Musculoskeletal Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.98 AI-based Drug Discovery Market for Neurological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.99 AI-based Drug Discovery Market for Respiratory Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.100 AI-based Drug Discovery Market: Distribution by End User
  • Table 27.101 AI-based Drug Discovery Market for Pharma and Biotech Companies, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.102 AI-based Drug Discovery Market for Contract Research Organizations, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.103 AI-based Drug Discovery Market for Research and Academic Institutions, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.104 AI-based Drug Discovery Market: Distribution by Geographical Regions
  • Table 27.105 AI-based Drug Discovery Market in North America, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.106 AI-based Drug Discovery Market in the US, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.107 AI-based Drug Discovery Market in Canada, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.108 AI-based Drug Discovery Market in Europe, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.109 AI-based Drug Discovery Market in the UK, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.110 AI-based Drug Discovery Market in Germany, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.111 AI-based Drug Discovery Market in France, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.112 AI-based Drug Discovery Market in Spain, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.113 AI-based Drug Discovery Market in Italy, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.114 AI-based Drug Discovery Market in Rest of Europe, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.115 AI-based Drug Discovery Market in Asia-Pacific, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.116 AI-based Drug Discovery Market in China, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.117 AI-based Drug Discovery Market in Japan, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.118 AI-based Drug Discovery Market in South Korea, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.119 AI-based Drug Discovery Market in Australia, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.120 AI-based Drug Discovery Market in India, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.121 AI-based Drug Discovery Market in MENA, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.122 AI-based Drug Discovery Market in Saudi Arabia, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.123 AI-based Drug Discovery Market in UAE, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.124 AI-based Drug Discovery Market in Egypt, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.125 AI-based Drug Discovery Market in Latin America, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD

Million)

  • Table 27.126 AI-based Drug Discovery Market in Brazil, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.127 AI-based Drug Discovery Market in Mexico, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Table 27.128 AI-based Drug Discovery Market in Argentina, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)

List of Figures

  • Figure 2.1 Research Methodology: Project Methodology
  • Figure 2.2 Research Methodology: Data Sources for Secondary Research
  • Figure 2.3 Research Methodology: Robust Quality Control
  • Figure 3.1 Market Dynamics: Forecast Methodology
  • Figure 3.2 Market Dynamics: Market Assessment Framework
  • Figure 4.1 Lessons Learnt from Past Recessions
  • Figure 5.1 Executive Summary: Overall Market Landscape
  • Figure 5.2 Executive Summary: Market Trends
  • Figure 5.3 Executive Summary: Market Forecast and Opportunity Analysis
  • Figure 6.1 Subsets of AI
  • Figure 6.2 Applications of AI in Healthcare
  • Figure 6.3 Advantages of Using AI in Drug Discovery
  • Figure 7.1 AI-based Drug Discovery Platform Providers: Distribution by Year of Establishment
  • Figure 7.2 AI-based Drug Discovery Platform Providers: Distribution by Company Size
  • Figure 7.3 AI-based Drug Discovery Platform Providers: Distribution by Location of Headquarters (Region)
  • Figure 7.4 AI-based Drug Discovery Platform Providers: Distribution by Location of Headquarters (Country)
  • Figure 7.5 Key AI-based Drug Discovery Platform Providers: Distribution by Type of Business Model
  • Figure 7.6 Key AI-based Drug Discovery Platform Providers: Distribution by Platform Utilization
  • Figure 7.7 Key AI-based Drug Discovery Platform Providers: Distribution by Drug Discovery Stages Supported
  • Figure 7.8 Key AI-based Drug Discovery Platform Providers: Distribution by Type of AI Technology Used
  • Figure 7.9 Key AI-based Drug Discovery Platform Providers: Distribution by Target Therapeutic Area
  • Figure 7.10 Key AI-based Drug Discovery Platform Providers: Distribution by Type of Molecule Analyzed
  • Figure 7.11 Key AI-based Drug Discovery Platform Providers: Distribution by End User
  • Figure 11.1 Partnerships and Collaborations: Quarterly Trend of Partnerships
  • Figure 11.2 Partnerships and Collaborations: Distribution by Type of Partnership
  • Figure 11.3 Partnerships and Collaborations: Distribution by Quarter and Type of Partnership
  • Figure 11.4 Partnerships and Collaborations: Distribution by Type of Partner
  • Figure 11.5 Most Active Players: Distribution by Number of Partnerships
  • Figure 11.6 Partnerships and Collaborations: Local and International Agreements
  • Figure 11.7 Partnerships and Collaborations: Intracontinental and Intercontinental Agreements
  • Figure 12.1 Funding and Investments Analysis: Quarterly Trend of Funding
  • Figure 12.2 Funding and Investments Analysis: Quarterly Trend of Amount Invested (USD Million)
  • Figure 12.3 Funding and Investment Analysis: Distribution of Instances by Type of Funding
  • Figure 12.4 Funding and Investment Analysis: Distribution by Quarter and Type of Funding
  • Figure 12.5 Funding and Investment Analysis: Distribution of Amount Invested by Type of Funding (USD Million)
  • Figure 12.6 Funding and Investment Analysis: Distribution of Amount Invested by Quarter and Type of Funding
  • Figure 12.7 Funding and Investment Analysis: Distribution by Geography (Region)
  • Figure 12.8 Funding and Investment Analysis: Distribution by Geography (Country)
  • Figure 12.9 Most Active Players: Distribution by Number of Funding Instances
  • Figure 12.10 Most Active Players: Distribution by Amount Raised (USD Million)
  • Figure 12.11 Leading Investors: Distribution by Number of Funding Instances
  • Figure 13.1 Patent Analysis: Distribution by Type of Patent
  • Figure 13.2 Patent Analysis: Cumulative Distribution by Patent Publication Year
  • Figure 13.3 Patent Analysis: Distribution by Type of Patent and Publication Year
  • Figure 13.4 Patent Analysis: Distribution by Patent Application Year
  • Figure 13.5 Patent Analysis: Distribution by Patent Jurisdiction
  • Figure 13.6 Patent Analysis Distribution by CPC Symbols
  • Figure 13.7 Patent Analysis: Cumulative Distribution by Type of Applicant
  • Figure 13.8 Leading Industry Players: Distribution by Number of Patents
  • Figure 13.9 Leading Non-Industry Players: Distribution by Number of Patents
  • Figure 13.10 Leading Individual Assignees: Distribution by Number of Patents
  • Figure 13.11 Patent Benchmarking Analysis: Distribution of Patent Characteristics CPC Codes by Leading Industry Players
  • Figure 13.12 Patent Benchmarking Analysis: Distribution of Leading Industry Players by Patent Characteristics CPC Codes
  • Figure 13.13 Patent Analysis: Distribution by Patent Age
  • Figure 13.14 AI-based Drug Discovery: Patent Valuation
  • Figure 14.1 Key Elements of Porter's Five Forces
  • Figure 14.2 Threat of New Entrants
  • Figure 14.3 Bargaining Power of Buyers
  • Figure 14.4 Bargaining Power of Solution Providers
  • Figure 14.5 Threats of Substitute Products
  • Figure 14.6 Rivalry Among Existing Competitors
  • Figure 14.7 Porter's Five Forces Analysis: Concluding Remarks
  • Figure 17.1 Overall Cost Saving Potential Associated with Use of AI-based Drug Discovery Platforms, Till 2035 (USD Billion)
  • Figure 17.2 Cost Saving Potential: Distribution by Drug Discovery Steps (USD Billion)
  • Figure 17.3 Cost Saving Potential in Target Identification/Validation, Till 2035 (USD Billion)
  • Figure 17.4 Cost Saving Potential in Hit Generation/Lead Identification and Optimization, Till 2035 (USD Billion)
  • Figure 17.5 Cost Saving Potential in Hit Generation/Lead Identification and Optimization, Till 2035 (USD Billion)
  • Figure 17.6 Cost Saving Potential: Distribution by Type of AI Technology (USD Billion)
  • Figure 17.7 Cost Saving Potential with Machine Learning, Till 2035 (USD Billion)
  • Figure 17.8 Cost Saving Potential with Molecular Modelling and Simulation, Till 2035 (USD Billion)
  • Figure 17.9 Cost Saving Potential with Deep Learning, Till 2035 (USD Billion)
  • Figure 17.10 Cost Saving Potential with Omics Integration, Till 2035 (USD Billion)
  • Figure 17.11 Cost Saving Potential with Generative Model, Till 2035 (USD Billion)
  • Figure 17.12 Cost Saving Potential with Structure-Based Drug Design, Till 2035 (USD Billion)
  • Figure 17.13 Cost Saving Potential with Other Technologies, Till 2035 (USD Billion)
  • Figure 17.14 Cost Saving Potential: Distribution by Therapeutic Area (USD Billion)
  • Figure 17.15 Cost Saving Potential in Drug Discovery for Oncological Disorders, Till 2035 (USD Billion)
  • Figure 17.16 Cost Saving Potential in Drug Discovery for Cardiovascular Disorders, Till 2035 (USD Billion)
  • Figure 17.17 Cost Saving Potential in Drug Discovery for Musculoskeletal Disorders, Till 2035 (USD Billion)
  • Figure 17.18 Cost Saving Potential in Drug Discovery for Neurological Disorders, Till 2035 (USD Billion)
  • Figure 17.19 Cost Saving Potential in Drug Discovery for Respiratory Disorders, Till 2035 (USD Billion)
  • Figure 17.20 Cost Saving Potential in Drug Discovery for Immunological Disorders, Till 2035 (USD Billion)
  • Figure 17.21 Cost Saving Potential in Drug Discovery for Gastrointestinal Disorders, Till 2035 (USD Billion)
  • Figure 17.22 Cost Saving Potential in Drug Discovery for Endocrine Disorders, Till 2035 (USD Billion)
  • Figure 17.23 Cost Saving Potential in Drug Discovery for Ophthalmological Disorders, Till 2035 (USD Billion)
  • Figure 17.24 Cost Saving Potential in Drug Discovery for Blood Disorders, Till 2035 (USD Billion)
  • Figure 17.25 Cost Saving Potential in Drug Discovery for Dermatological Disorders, Till 2035 (USD Billion)
  • Figure 17.26 Cost Saving Potential in Drug Discovery for Infectious Diseases, Till 2035 (USD Billion)
  • Figure 17.27 Cost Saving Potential in Drug Discovery for Urinary Disorders, Till 2035 (USD Billion)
  • Figure 17.28 Cost Saving Potential: Distribution by End User (USD Billion)
  • Figure 17.29 Cost Saving Potential for Pharma and Biotech Companies, Till 2035 (USD Billion)
  • Figure 17.30 Cost Saving Potential for Contract Research Organizations (CRO), Till 2035 (USD Billion)
  • Figure 17.31 Cost Saving Potential for Research and Academic Institutions, Till 2035 (USD Billion)
  • Figure 17.32 Cost Saving Potential: Distribution by Geographical Regions (USD Billion)
  • Figure 17.33 Cost Saving Potential in North America, Till 2035 (USD Billion)
  • Figure 17.34 Cost Saving Potential in Europe, Till 2035 (USD Billion)
  • Figure 17.35 Cost Saving Potential in Asia-Pacific, Till 2035 (USD Billion)
  • Figure 17.36 Cost Saving Potential in MENA, Till 2035 (USD Billion)
  • Figure 17.37 Cost Saving Potential in Latin America, Till 2035 (USD Billion)
  • Figure 19.1 Global AI-based Drug Discovery Market, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 19.2 Global AI-based Drug Discovery Market, Forecasted Estimates (Till 2035), Conservative Scenario (USD Million)
  • Figure 19.3 Global AI-based Drug Discovery Market, Forecasted Estimates (Till 2035), Optimistic Scenario (USD Million)
  • Figure 20.1 AI-based Drug Discovery Market: Distribution by Drug Discovery Steps
  • Figure 20.2 AI-based Drug Discovery Market for Target Identification / Validation, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 20.3 AI-based Drug Discovery Market for Hit Generation / Lead Identification, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 20.4 AI-based Drug Discovery Market for Lead Optimization, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 21.1 AI-based Drug Discovery Market: Distribution by Type of AI Technology
  • Figure 21.2 AI-based Drug Discovery Market for Machine Learning, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 21.3 AI-based Drug Discovery Market for Molecular Modelling and Simulation, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 21.4 AI-based Drug Discovery Market for Deep Learning, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 21.5 AI-based Drug Discovery Market for Omics Integration, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 21.6 AI-based Drug Discovery Market for Generative Models, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 21.7 AI-based Drug Discovery Market for Structure-Based Drug Design, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 21.8 AI-based Drug Discovery Market for Others, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 22.1 AI-based Drug Discovery Market: Distribution by Therapeutic Area
  • Figure 22.2 AI-based Drug Discovery Market for Oncological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 22.3 AI-based Drug Discovery Market for Cardiovascular Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 22.4 AI-based Drug Discovery Market for Musculoskeletal Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 22.5 AI-based Drug Discovery Market for Neurological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 22.6 AI-based Drug Discovery Market for Respiratory Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 22.7 AI-based Drug Discovery Market for Immunological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 22.8 AI-based Drug Discovery Market for Gastrointestinal Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 22.9 AI-based Drug Discovery Market for Endocrine Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 22.10 AI-based Drug Discovery Market for Blood Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 22.11 AI-based Drug Discovery Market for Ophthalmological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 22.12 AI-based Drug Discovery Market for Dermatological Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 22.13 AI-based Drug Discovery Market for Infectious Diseases, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 22.14 AI-based Drug Discovery Market for Urinary Disorders, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 23.1 AI-based Drug Discovery Market: Distribution by End User
  • Figure 23.2 AI-based Drug Discovery Market for Pharma and Biotech Companies, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 23.3 AI-based Drug Discovery Market for Contract Research Organizations, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 23.4 AI-based Drug Discovery Market for Research and Academic Institutions, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.1 AI-based Drug Discovery Market: Distribution by Geographical Regions
  • Figure 24.2 AI-based Drug Discovery Market in North America, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.3 AI-based Drug Discovery Market in the US, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.4 AI-based Drug Discovery Market in Canada, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.5 AI-based Drug Discovery Market in Europe, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.6 AI-based Drug Discovery Market in the UK, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.7 AI-based Drug Discovery Market in Germany, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.8 AI-based Drug Discovery Market in France, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.9 AI-based Drug Discovery Market in Spain, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.10 AI-based Drug Discovery Market in Italy, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.11 AI-based Drug Discovery Market in Rest of Europe, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.12 AI-based Drug Discovery Market in Asia-Pacific, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.13 AI-based Drug Discovery Market in China, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.14 AI-based Drug Discovery Market in Japan, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.15 AI-based Drug Discovery Market in South Korea, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.16 AI-based Drug Discovery Market in Australia, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.17 AI-based Drug Discovery Market in India, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.18 AI-based Drug Discovery Market in MENA, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.19 AI-based Drug Discovery Market in Saudi Arabia, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.20 AI-based Drug Discovery Market in UAE, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.21 AI-based Drug Discovery Market in Egypt, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.22 AI-based Drug Discovery Market in Latin America, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.23 AI-based Drug Discovery Market in Brazil, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.24 AI-based Drug Discovery Market in Mexico, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.25 AI-based Drug Discovery Market in Argentina, Historical Trends (Since 2023) and Forecasted Estimates (Till 2035) (USD Million)
  • Figure 24.26 Market Movement Analysis: Geographical Regions
  • Figure 24.27 Penetration-Growth (P-G) Matrix: Geographical Regions
  • Figure 25.1 Concluding Remarks: Current Market Landscape
  • Figure 25.2 Concluding Remarks: Partnerships and Collaborations
  • Figure 25.3 Concluding Remarks: Funding and Investments
  • Figure 25.4 Concluding Remarks: Patent Analysis
  • Figure 25.6 Concluding Remarks: Market Forecast and Opportunity Analysis (I/II)
  • Figure 25.7 Concluding Remarks: Market Forecast and Opportunity Analysis (II/II)