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人工智慧在藥物研發領域的市場：產業趨勢及全球預測（至2040年）－按應用、影像處理類型和主要地區劃分

AI In Drug Discovery Market, till 2040: Distribution by Drug Discovery Steps, Therapeutic Area, and Key Geographical Regions: Industry Trends and Global Forecasts
出版日期: | 出版商: Roots Analysis | 英文 387 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

人工智慧在藥物研發領域的市場展望

全球人工智慧在藥物研發領域的市場規模預計將從目前的18.1億美元增長至2040年的410.8億美元，預測期內（至2040年）的複合年增長率（CAGR）為25%。本研究提供了市場規模、成長情境、產業趨勢和未來預測。

人工智慧（AI）正在透過虛擬篩選、預測療效和毒性建模以及新藥設計等技術，加速藥物研發進程、降低成本並提高成功率，從而徹底改變藥物研發方式。機器學習和深度學習技術可以評估大型資料集，以識別有前景的候選藥物，預測其在體內的行為，甚至創造全新的分子。 人工智慧也被應用於藥物再利用和個人化醫療領域，透過發現現有藥物的新用途，並根據患者的個別數據量身定制治療方案。

由於對各種疾病的先進療法的需求不斷增長，人工智慧在藥物發現領域的市場預計將顯著增長。隨著慢性病在全球的盛行率不斷上升，製藥公司正在加大研發投入，以滿足對新藥的持續需求。

AI In Drug Discovery Market-IMG1

高階主管的策略洞察

推動人工智慧在藥物發現市場成長的關鍵因素

推動人工智慧在藥物發現市場成長的關鍵因素包括人工智慧能夠快速分析大型資料集、預測分子特性和毒性、發現新的藥物標靶以及加速現有藥物的再利用過程。人工智慧利用機器學習來增強藥物發現過程，更準確地預測候選藥物的療效、安全性和藥物動力學特性，最終降低成本並縮短研發時間。其他關鍵成長因素包括來自私營和公共部門的投資和資金增加，以及人工智慧平台在標靶識別、先導化合物優化、毒性預測和安全性分析方面的應用日益廣泛。

人工智慧在個人化醫療中的作用

人工智慧透過分析大型資料集，促進個人化治療、提高診斷準確性並加速藥物發現進程，為個人化醫療做出重大貢獻。它整合了來自基因組資訊、電子健康記錄和可穿戴技術的訊息，以預測疾病風險、優化治療方案並發現新的治療標靶。這使得診斷更加精準，改善患者預後，並創建更有效率的醫療保健系統。

人工智慧在藥物發現領域的市場競爭格局

人工智慧在藥物發現領域的市場競爭格局的特徵是來自大型企業和小型企業的激烈競爭。 該領域的關鍵參與者包括英偉達 (NVIDIA)、Insilico Medicine、Exscientia、BenevolentAI、Google DeepMind、IBM 和微軟，它們正在開發用於靶點識別、新興化學和臨床試驗優化的先進人工智慧系統。阿斯特捷利康、輝瑞、羅氏、諾華和拜耳等大型製藥公司正積極與人工智慧公司合作，利用機器學習實現更快、更經濟高效的藥物研發流程。

Atomwise、Recursion Pharmaceuticals 和 BenchSci 等新創公司正憑藉其獨特的人工智慧研究方法進行創新。同時，為了滿足對精準醫療和新型療法日益增長的需求，投資和合作也迅速增加。 人工智慧能夠處理複雜的生物數據，縮短研發週期，並提高全球候選藥物的成功率，這推動了該市場強勁的發展勢頭，預計將實現顯著成長。

人工智慧在藥物發現領域的演進－新興產業趨勢

該領域的新興趨勢包括：利用生成式人工智慧產生新型分子；整合多組學資料以全面了解疾病；以及利用大規模語言模型（LLM）分析科學文獻。此外，我們還看到個人化醫療領域取得了進展，該領域正在引入狀態空間模型（SSM）以提高計算效率，並利用人工智慧評估個別患者數據，制定個人化治療方案。

主要市場挑戰

人工智慧在藥物發現領域的市場面臨諸多挑戰，包括數據和技術限制、監管和倫理挑戰以及營運障礙。資料品質和可用性是關鍵問題，而藥物資料集經常存在碎片化、不一致、不完整和註釋不足等問題。 這可能導致人工智慧系統做出偏差的預測，並產生不可靠的結果。

此外，生物系統的複雜性使得全面的計算建模極具挑戰性，而高昂的計算成本更是令小型機構難以承受。另外，監管方面的不確定性源於FDA和EMA指南的不斷變化，這些指南與人工智慧的迭代特性不符；倫理困境，例如HIPAA/GDPR下的資料隱私問題；以及關於人工智慧開發藥物專利申請的智慧財產權糾紛。

人工智慧在藥物發現領域的市場：主要細分市場

藥物發現流程階段

  • 標靶識別/驗證
  • 先導化合物發現/先導化合物篩選
  • 先導化合物優化

治療領域

  • 腫瘤學
  • 中樞神經系統疾病
  • 傳染病
  • 呼吸系統疾病
  • 心血管疾病
  • 內分泌疾病
  • 胃腸道疾病
  • 肌肉骨骼疾病
  • 免疫系統疾病
  • 皮膚病
  • 其他

地理區域

  • 北美
  • 美國美國
  • 加拿大
  • 墨西哥
  • 其他北美國家
  • 歐洲
  • 奧地利
  • 比利時
  • 丹麥
  • 法國
  • 德國
  • 愛爾蘭
  • 義大利
  • 荷蘭
  • 挪威
  • 俄羅斯
  • 西班牙
  • 瑞典
  • 瑞士
  • 英國
  • 其他歐洲國家
  • 亞洲
  • 中國
  • 印度
  • 日本
  • 新加坡
  • 韓國
  • 其他亞洲國家
  • 拉丁美洲
  • 巴西
  • 智利
  • 哥倫比亞
  • 委內瑞拉
  • 其他拉丁美洲國家
  • 中東和北非非洲
  • 埃及
  • 伊朗
  • 伊拉克
  • 以色列
  • 科威特
  • 沙烏地阿拉伯
  • 阿拉伯聯合大公國
  • 其他中東和北非國家
  • 世界其他地區
  • 澳大利亞
  • 紐西蘭
  • 其他國家

人工智慧在藥物發現市場的應用：關鍵市場佔有率洞察

依藥物發現流程階段劃分的市佔率

依藥物發現步驟劃分，全球市場可分為標靶識別與驗證、先導化合物生成與篩選、先導化合物優化。據我們估計，目前先導化合物優化佔了大部分市場佔有率。人工智慧在藥物發現早期階段的應用，尤其是在先導化合物優化階段，對於提高藥物的療效、可及性和安全性至關重要。此外，先導化合物優化對於提高藥物的溶解度、細胞滲透性和穩定性也至關重要。

依地區劃分的市佔率

據我們估計，北美目前在人工智慧藥物研發市場中佔較大佔有率。這主要歸功於製藥公司越來越多地使用人工智慧工具進行藥物研發，以及旨在改善北美產品交付的合作協議不斷增加。值得注意的是，預計亞太地區人工智慧藥物研發市場在預測期內將以更高的複合年增長率成長。

人工智慧藥物發現市場代表性企業

  • Aiforia Technologies
  • Atomwise
  • BioSyntagma
  • Chemalive
  • Collaborations Pharmaceuticals
  • Cyclica
  • DeepMatter
  • Recursion
  • InveniAI
  • MAbSilico
  • Optibrium
  • Recursion Pharmaceuticals
  • Sensyne Health
  • Valo Health

人工智慧藥物發現市場：報告內容

本報告在以下幾個部分對人工智慧藥物發現市場進行了詳細分析：

  • 市場規模與機會分析：對人工智慧藥物發現市場進行詳細分析。 重點在於關鍵市場細分：[A] 應用領域，[B] 處理的影像類型，以及 [C] 主要地區。
  • 競爭格局：基於多個相關參數，對參與人工智慧藥物研發市場的公司進行全面分析，例如 [A] 成立年份，[B] 公司規模，[C] 總部所在地，[D] 所有權結構等。
  • 公司簡介：提供參與人工智慧藥物研發市場的主要公司的詳細簡介，包括 [A] 總部所在地，[B] 公司規模，[C] 企業使命，[D] 營運區域，[E] 管理團隊，[F] 聯絡方式，[G] 財務資訊，[H] 業務板塊，[I] 產品組合，以及近期展望] 發展和未來。
  • 宏觀趨勢：評估人工智慧藥物研發產業當前的宏觀趨勢。
  • 專利分析：基於相關參數（例如[A]專利類型、[B]專利公開年份、[C]專利年齡和[D]主要參與者）對藥物發現領域人工智慧相關的專利申請和註冊情況進行深入分析。
  • 近期發展：概述藥物發現領域人工智慧市場的最新發展，並基於相關參數（例如[A]項目年份、[B]項目類型、[C]地理分佈和[D]主要參與者）進行分析。
  • 波特五力分析：分析藥物發現領域人工智慧市場的五種競爭力量（新進入者的威脅、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）。
  • SWOT分析：提供深入的SWOT框架，突顯該領域的優勢、劣勢、機會和威脅。此外，還提供哈維鮑爾分析，突顯每個SWOT參數的相對影響。
  • 價值鏈分析：全面的價值鏈分析提供人工智慧在藥物研發市場中各個階段和利害關係人的資訊。

目錄

第一章：引言

第二章摘要整理

第三章：導論

  • 章節概述
  • 人工智慧
  • 人工智慧的子集
    • 機器學習
  • 數據科學
  • 人工智慧在醫療保健領域的應用
    • 藥物發現
    • 疾病預測、診斷與治療
    • 生產與供應鏈運營
    • 行銷
    • 臨床試驗
  • 人工智慧在藥物發現的應用
    • 路徑和標靶識別
    • 先導化合物識別
    • 先導化合物優化
    • 類藥化合物的合成
  • 人工智慧在藥物發現過程中的優勢
  • 人工智慧面臨的挑戰實施
  • 結論

第四章：競爭格局

  • 章節概述
  • 人工智慧在藥物發現的應用：市場格局

第五章 公司簡介：北美人工智慧藥物發現服務提供者

  • 章節概述
  • Atomwise
  • BioSyntagma
  • Collaborations Pharmaceuticals
  • Cyclica
  • InveniAI
  • Recursion Pharmaceuticals
  • Valo Health

第六章：公司簡介：歐洲人工智慧藥物發現服務提供者

  • 章節概述
  • Aiforia技術
  • Chemalive
  • DeepMatter
  • Exscientia
  • MAbSilico
  • Optibrium
  • Sensyne Health

第七章 公司簡介：亞太地區人工智慧藥物發現服務提供者

  • 章節概述
  • 3BIGS
  • Gero
  • Insilico Medicine
  • KeenEye

第八章：合作與夥伴關係

  • 章節概述
  • 合作模式
  • 人工智慧在藥物發現的應用：合作與夥伴關係

第九章：資金與投資分析

  • 章節概述
  • 資金類型
  • 基於人工智慧的藥物發現：資金和投資

第十章 專利分析

  • 章節概述
  • 範圍與研究方法
  • 人工智慧驅動的藥物發現：專利分析
  • 人工智慧驅動的藥物發現：專利基準分析
  • 人工智慧驅動的藥物發現：專利估值
  • 主要專利：引用分析

第十一章：波特五力分析

第十二章：企業估值分析

第十三章：科技巨頭的AI醫療保健計畫

  • 章節概述
    • 亞馬遜網路服務
    • 微軟
    • 英特爾
    • 阿里巴巴雲
    • 西門子
    • Google
    • IBM

第十四章 成本節約分析

  • 章節概述
  • 關鍵假設與研究方法
  • 在藥物發現中使用人工智慧解決方案的整體成本節約潛力

第十五章 市場預測

  • 章節概述
  • 關鍵假設與研究方法
  • 全球藥物發現人工智慧市場

第十六章 結論

第十七章 高階主管洞察

  • 章節概述
  • Aigenpulse
  • 雲端製藥公司
  • DEARGEN
  • Intelligent Omics
  • Pepticom
  • Sage-N Research

第18章 附錄一：表格資料

第19章 附錄二：公司與機構列表

簡介目錄
Product Code: RA100342

AI in Drug Discovery Market Outlook

As per Roots Analysis, the global AI in drug discovery market size is estimated to grow from USD 1.81 billion in the current year to USD 41.08 billion by 2040, at a CAGR of 25% during the forecast period, till 2040. The new study provides market size, growth scenarios, industry trend and future forecast.

Artificial intelligence (AI) is revolutionizing drug discovery by speeding up the process, lowering costs, and enhancing success rates through methods, such as virtual screening, predictive modeling for efficacy and toxicity, and de novo drug design. Machine learning and deep learning techniques evaluate large datasets to pinpoint promising drug candidates, anticipate their behavior within the body, and even create completely new molecules. AI is also applied in drug repurposing and personalizing therapies by discovering new applications for existing medications or customizing treatments for individual patients based on their specific data.

The market for AI in drug discovery is expected to grow significantly due to the increasing need for advanced therapeutic medications aimed at a wide array of medical conditions. With the rising prevalence of chronic illnesses worldwide, pharmaceutical companies are enhancing their investment in research and development to fulfill the persistent demand for new medications.

AI In Drug Discovery Market - IMG1

Strategic Insights for Senior Leaders

Key Drivers Propelling Growth of AI in Drug Discovery Market

The primary factors propelling the AI in drug discovery market include the use of AI in drug discovery are its capability to quickly analyze large datasets, forecast molecular characteristics and toxicity, discover new drug targets, and speed up the process of repurposing existing medications. AI enhances the drug development process by employing machine learning to better predict a drug candidate's effectiveness, safety, and pharmacokinetic traits, ultimately resulting in lower expenses and shorter timelines. Other significant growth drivers, include the increasing investments and funding from private and public sectors, rising adoption of AI-driven platforms for target identification, lead optimization, toxicity prediction, and safety profiling.

Role of AI in Personalized Medicine

Artificial intelligence significantly contributes to personalized medicine by examining large datasets to facilitate tailored treatments, enhance diagnostics, and speed up the process of drug discovery. It combines information from genomics, electronic health records, and wearable technology to forecast disease risk, refine medication plans, and discover new therapeutic targets. This results in more precise diagnostics, improved patient outcomes, and more effective healthcare systems.

AI in Drug Discovery Market: Competitive Landscape of Companies in this Industry

The competitive landscape of AI in drug discovery market is characterized by intense competition, featuring a combination of large and smaller firms. Key players in this field include NVIDIA, Insilico Medicine, Exscientia, BenevolentAI, Google DeepMind, IBM, and Microsoft, which have created sophisticated AI systems for target identification, generative chemistry, and optimizing clinical trials. Major pharmaceutical organizations, such as AstraZeneca, Pfizer, Roche, Novartis, and Bayer are actively collaborating with AI firms to utilize machine learning for more rapid and economical drug development processes.

Startups such as Atomwise, Recursion Pharmaceuticals, and BenchSci bring innovation with their distinct AI-focused methodologies, while investments and partnerships are rapidly rising to meet the growing demand for precision medicine and new therapeutics. The market, which is projected for substantial growth, shows robust momentum driven by the capability of AI capability to process intricate biological data, shorten R&D timelines, and improve the success rates of drug candidates worldwide.

AI in Drug Discovery Evolution: Emerging Trends in the Industry

Emerging trends in this domain include the utilization of generative AI to create new molecules, the incorporation of multi-omics data for a comprehensive understanding of diseases, and the use of Large Language Models (LLMs) to examine scientific literature. Additional advancements include the employment of State Space Models (SSMs), which provide enhanced computational efficiency, and the integration of AI in personalized medicine, where AI develops customized treatment plans by evaluating individual patient data.

Key Market Challenges

The market for AI in drug discovery faces significant challenges, including data and technical limitations, as well as regulatory and ethical issues, and operational obstacles. The quality and availability of data are pivotal concerns, as pharmaceutical datasets frequently suffer from fragmentation, inconsistencies, incompleteness, or poor annotations. This can result in biased predictions and unreliable outcomes from AI systems.

Further, the intricate nature of biological systems makes comprehensive computational modeling difficult and is further complicated by the high computational expenses that can be burdensome for smaller organizations. Additionally, regulatory uncertainties arise from changing FDA and EMA guidelines that do not align well with the iterative characteristics of AI, ethical dilemmas such as data privacy issues under HIPAA/GDPR, and intellectual property disputes concerning the patenting of drugs developed by AI.

AI in Drug Discovery Market: Key Market Segmentation

Drug Discovery Steps

  • Target identification / validation
  • Hit generation / lead identification
  • Lead optimization

Therapeutic Area

  • Oncological disorders
  • CNS disorders
  • Infectious diseases
  • Respiratory disorders
  • Cardiovascular disorders
  • Endocrine disorders
  • Gastrointestinal disorders
  • Musculoskeletal disorders
  • Immunological disorders
  • Dermatological disorders
  • Others

Geographical Regions

  • North America
  • US
  • Canada
  • Mexico
  • Other North American countries
  • Europe
  • Austria
  • Belgium
  • Denmark
  • France
  • Germany
  • Ireland
  • Italy
  • Netherlands
  • Norway
  • Russia
  • Spain
  • Sweden
  • Switzerland
  • UK
  • Other European countries
  • Asia
  • China
  • India
  • Japan
  • Singapore
  • South Korea
  • Other Asian countries
  • Latin America
  • Brazil
  • Chile
  • Colombia
  • Venezuela
  • Other Latin American countries
  • Middle East and North Africa
  • Egypt
  • Iran
  • Iraq
  • Israel
  • Kuwait
  • Saudi Arabia
  • UAE
  • Other MENA countries
  • Rest of the World
  • Australia
  • New Zealand
  • Other countries

AI in Drug Discovery Market: Key Market Share Insights

Market Share by Drug Discovery Step

Based on the drug discovery step, the global market is segmented into target identification / validation, hit generation / lead identification and lead optimization. According to our estimates, currently, lead optimization captures majority share of the market. The application of AI in the initial phases of drug discovery, particularly in lead optimization, is crucial for improving the drug's efficacy, accessibility, and safety profile. Additionally, lead optimization is vital for enhancing solubility, cellular permeability, and stability.

Market Share by Geography

According to our estimates North America currently captures a significant share of the AI in drug discovery market. This is due to the increasing utilization of AI-based tools by pharmaceutical companies for drug discovery and the rise in partnership agreements aimed at improving product offerings in North America. It is also important to note that the AI in drug discovery market in the Asia-Pacific region is expected to grow at a higher CAGR over the forecast period.

Example Players in AI in Drug Discovery Market

  • Aiforia Technologies
  • Atomwise
  • BioSyntagma
  • Chemalive
  • Collaborations Pharmaceuticals
  • Cyclica
  • DeepMatter
  • Recursion
  • InveniAI
  • MAbSilico
  • Optibrium
  • Recursion Pharmaceuticals
  • Sensyne Health
  • Valo Health

AI in Drug Discovery Market: Report Coverage

The report on the AI in drug discovery market features insights on various sections, including:

  • Market Sizing and Opportunity Analysis: An in-depth analysis of the AI in drug discovery market, focusing on key market segments, including [A] drug discovery steps, [B] therapeutic area, and [C] key geographical regions.
  • Competitive Landscape: A comprehensive analysis of the companies engaged in the AI in drug discovery market, based on several relevant parameters, such as [A] year of establishment, [B] company size, [C] location of headquarters and [D] ownership structure.
  • Company Profiles: Elaborate profiles of prominent players engaged in the AI in drug discovery market, providing details on [A] location of headquarters, [B] company size, [C] company mission, [D] company footprint, [E] management team, [F] contact details, [G] financial information, [H] operating business segments, [I] portfolio, [J] recent developments, and an informed future outlook.
  • Megatrends: An evaluation of ongoing megatrends in the AI in drug discovery industry.
  • Patent Analysis: An insightful analysis of patents filed / granted in the AI in drug discovery domain, based on relevant parameters, including [A] type of patent, [B] patent publication year, [C] patent age and [D] leading players.
  • Recent Developments: An overview of the recent developments made in the AI in drug discovery market, along with analysis based on relevant parameters, including [A] year of initiative, [B] type of initiative, [C] geographical distribution and [D] most active players.
  • Porter's Five Forces Analysis: An analysis of five competitive forces prevailing in the AI in drug discovery market, including threats of new entrants, bargaining power of buyers, bargaining power of suppliers, threats of substitute products and rivalry among existing competitors.
  • SWOT Analysis: An insightful SWOT framework, highlighting the strengths, weaknesses, opportunities and threats in the domain. Additionally, it provides Harvey ball analysis, highlighting the relative impact of each SWOT parameter.
  • Value Chain Analysis: A comprehensive analysis of the value chain, providing information on the different phases and stakeholders involved in the AI in drug discovery market.

Key Questions Answered in this Report

  • What is the current and future market size?
  • Who are the leading companies in this market?
  • What are the growth drivers that are likely to influence the evolution of this market?
  • What are the key partnership and funding trends shaping this industry?
  • Which region is likely to grow at higher CAGR till 2040?
  • How is the current and future market opportunity likely to be distributed across key market segments?

Reasons to Buy this Report

  • Detailed Market Analysis: 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.
  • In-depth Analysis of Trends: Stakeholders can leverage the report to gain a deeper understanding of the competitive dynamics within the market. Each report maps ecosystem activity across partnerships, funding, and patent landscapes to reveal growth hotspots and white spaces in the industry.
  • Opinion of Industry Experts: The report features extensive interviews and surveys with key opinion leaders and industry experts to validate market trends mentioned in the report.
  • Decision-ready Deliverables: The report offers stakeholders with strategic frameworks (Porter's Five Forces, value chain, SWOT), and complimentary Excel / slide packs with customization support.

Additional Benefits

  • Complimentary Dynamic Excel Dashboards for Analytical Modules
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TABLE OF CONTENTS

1. PREFACE

  • 1.1. Scope of the Report
  • 1.2. Research Methodology
  • 1.3. Key Questions Answered
  • 1.4. Chapter Outlines

2. EXECUTIVE SUMMARY

3. INTRODUCTION

  • 3.1. Chapter Overview
  • 3.2. Artificial Intelligence
  • 3.3. Subsets of AI
    • 3.3.1. Machine Learning
      • 3.3.1.1. Supervised Learning
      • 3.3.1.2. Unsupervised Learning
      • 3.3.1.3. Reinforced / Reinforcement Learning
      • 3.3.1.4. Deep Learning
      • 3.3.1.5. Natural Language Processing (NLP)
  • 3.4. Data Science
  • 3.5. Applications of AI in Healthcare
    • 3.5.1. Drug Discovery
    • 3.5.2. Disease Prediction, Diagnosis and Treatment
    • 3.5.3. Manufacturing and Supply Chain Operations
    • 3.5.4. Marketing
    • 3.5.5. Clinical Trials
  • 3.6. AI in Drug Discovery
    • 3.6.1. Identification of Pathway and Target
    • 3.6.2. Identification of Hit or Lead
    • 3.6.3. Lead Optimization
    • 3.6.4. Synthesis of Drug-Like Compounds
  • 3.7. Advantages of Using AI in the Drug Discovery Process
  • 3.8. Challenges Associated with the Adoption of AI
  • 3.9. Concluding Remarks

4. COMPETITIVE LANDSCAPE

  • 4.1. Chapter Overview
  • 4.2. AI-based Drug Discovery: Overall Market Landscape
    • 4.2.1. Analysis by Year of Establishment
    • 4.2.2. Analysis by Company Size
    • 4.2.3. Analysis by Location of Headquarters
    • 4.2.4. Analysis by Type of Company
    • 4.2.5. Analysis by Type of Technology
    • 4.2.6. Analysis by Drug Discovery Steps
    • 4.2.7. Analysis by Type of Drug Molecule
    • 4.2.8. Analysis by Drug Development Initiatives
    • 4.2.9. Analysis by Technology Licensing Option
    • 4.2.10. Analysis by Target Therapeutic Area
    • 4.2.11. Key Players: Analysis by Number of Platforms / Tools Available

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

  • 5.1. Chapter Overview
  • 5.2. Atomwise
    • 5.2.1. Company Overview
    • 5.2.2. AI-based Drug Discovery Technology Portfolio
    • 5.2.3. Recent Developments and Future Outlook
  • 5.3. BioSyntagma
    • 5.3.1. Company Overview
    • 5.3.2. AI-based Drug Discovery Technology Portfolio
    • 5.3.3. Recent Developments and Future Outlook
  • 5.4. Collaborations Pharmaceuticals
    • 5.4.1. Company Overview
    • 5.4.2. AI-based Drug Discovery Technology Portfolio
    • 5.4.3. Recent Developments and Future Outlook
  • 5.5. Cyclica
    • 5.5.1. Company Overview
    • 5.5.2. AI-based Drug Discovery Technology Portfolio
    • 5.5.3. Recent Developments and Future Outlook
  • 5.6. InveniAI
    • 5.6.1. Company Overview
    • 5.6.2. AI-based Drug Discovery Technology Portfolio
    • 5.6.3. Recent Developments and Future Outlook
  • 5.7. Recursion Pharmaceuticals
    • 5.7.1. Company Overview
    • 5.7.2. AI-based Drug Discovery Technology Portfolio
    • 5.7.3. Recent Developments and Future Outlook
  • 5.8. Valo Health
    • 5.8.1. Company Overview
    • 5.8.2. AI-based Drug Discovery Technology Portfolio
    • 5.8.3. Recent Developments and Future Outlook

6. COMPANY PROFILES: AI-BASED DRUG DISOCVERY SERVICE PROVIDERS IN EUROPE

  • 6.1. Chapter Overview
  • 6.2. Aiforia Technologies
    • 6.2.1. Company Overview
    • 6.2.2. AI-based Drug Discovery Technology Portfolio
    • 6.2.3. Recent Developments and Future Outlook
  • 6.3. Chemalive
    • 6.3.1. Company Overview
    • 6.3.2. AI-based Drug Discovery Technology Portfolio
    • 6.3.3. Recent Developments and Future Outlook
  • 6.4. DeepMatter
    • 6.4.1. Company Overview
    • 6.4.2. AI-based Drug Discovery Technology Portfolio
    • 6.4.3. Recent Developments and Future Outlook
  • 6.5. Exscientia
    • 6.5.1. Company Overview
    • 6.5.2. AI-based Drug Discovery Technology Portfolio
    • 6.5.3. Recent Developments and Future Outlook
  • 6.6. MAbSilico
    • 6.6.1. Company Overview
    • 6.6.2. AI-based Drug Discovery Technology Portfolio
    • 6.6.3. Recent Developments and Future Outlook
  • 6.7. Optibrium
    • 6.7.1. Company Overview
    • 6.7.2. AI-based Drug Discovery Technology Portfolio
    • 6.7.3. Recent Developments and Future Outlook
  • 6.8. Sensyne Health
    • 6.8.1. Company Overview
    • 6.8.2. AI-based Drug Discovery Technology Portfolio
    • 6.8.3. Recent Developments and Future Outlook

7. COMPANY PROFILES: AI-BASED DRUG DISOCVERY SERVICE PROVIDERS IN ASIA PACIFIC

  • 7.1. Chapter Overview
  • 7.2. 3BIGS
    • 7.2.1. Company Overview
    • 7.2.2. AI-based Drug Discovery Technology Portfolio
    • 7.2.3. Recent Developments and Future Outlook
  • 7.3. Gero
    • 7.3.1. Company Overview
    • 7.3.2. AI-based Drug Discovery Technology Portfolio
    • 7.3.3. Recent Developments and Future Outlook
  • 7.4. Insilico Medicine
    • 7.4.1. Company Overview
    • 7.4.2. AI-based Drug Discovery Technology Portfolio
    • 7.4.3. Recent Developments and Future Outlook
  • 7.5. KeenEye
    • 7.5.1. Company Overview
    • 7.5.2. AI-based Drug Discovery Technology Portfolio
    • 7.5.3. Recent Developments and Future Outlook

8. PARTNERSHIPS AND COLLABORATIONS

  • 8.1. Chapter Overview
  • 8.2. Partnership Models
  • 8.3. AI-based Drug Discovery: Partnerships and Collaborations
    • 8.3.1. Analysis by Year of Partnership
    • 8.3.2. Analysis by Type of Partnership
    • 8.3.3. Analysis by Year and Type of Partnership
    • 8.3.4. Analysis by Target Therapeutic Area
    • 8.3.5. Analysis by Focus Area
    • 8.3.6. Analysis by Year of Partnership and Focus Area
    • 8.3.7. Analysis by Type of Partner Company
    • 8.3.8. Analysis by Type of Partnership and Type of Partner Company
    • 8.3.9. Most Active Players: Analysis by Number of Partnerships
    • 8.3.10. Analysis by Region
      • 8.3.11.1. Intercontinental and Intracontinental Deals
      • 8.3.11.2. International and Local Deals

9. FUNDING AND INVESTMENT ANALYSIS

  • 9.1. Chapter Overview
  • 9.2. Types of Funding
  • 9.3. AI-based Drug Discovery: Funding and Investments
    • 9.3.1. Analysis of Number of Funding Instances by Year
    • 9.3.2. Analysis of Amount Invested by Year
    • 9.3.3. Analysis by Type of Funding
    • 9.3.4. Analysis of Amount Invested and Type of Funding
    • 9.3.5. Analysis of Amount Invested by Company Size
    • 9.3.6. Analysis by Type of Investor
    • 9.3.7. Analysis of Amount Invested by Type of Investor
    • 9.3.8. Most Active Players: Analysis by Number of Funding Instances
    • 9.3.9. Most Active Players: Analysis by Amount Invested
    • 9.3.10. Most Active Investors: Analysis by Number of Funding Instances
    • 9.3.11. Analysis of Amount Invested by Geography
      • 9.3.11.1. Analysis by Region
      • 9.3.11.2. Analysis by Country

10. PATENT ANALYSIS

  • 10.1. Chapter Overview
  • 10.2. Scope and Methodology
  • 10.3. AI-based Drug Discovery: Patent Analysis
    • 10.3.1 Analysis by Application Year
    • 10.3.2. Analysis by Geography
    • 10.3.3. Analysis by CPC Symbols
    • 10.3.4. Analysis by Emerging Focus Areas
    • 10.3.5. Analysis by Type of Applicant
    • 10.3.6. Leading Players: Analysis by Number of Patents
  • 10.4. AI-based Drug Discovery: Patent Benchmarking
    • 10.4.1. Analysis by Patent Characteristics
  • 10.5. AI-based Drug Discovery: Patent Valuation
  • 10.6. Leading Patents: Analysis by Number of Citations

11. PORTER'S FIVE FORCES ANALYSIS

  • 11.1. Chapter Overview
  • 11.2. Methodology and Assumptions
  • 11.3. Key Parameters
    • 11.3.1. Threats of New Entrants
    • 11.3.2. Bargaining Power of Drug Developers
    • 11.3.3. Bargaining Power of Companies Using AI for Drug Discovery
    • 11.3.4. Threats of Substitute Technologies
    • 11.3.5. Rivalry Among Existing Competitors
  • 11.4. Concluding Remarks

12. COMPANY VALUATION ANALYSIS

  • 12.1. Chapter Overview
  • 12.2. Company Valuation Analysis: Key Parameters
  • 12.3. Methodology
  • 12.4. Company Valuation Analysis: Roots Analysis Proprietary Scores

13. AI-BASED HEALTHCARE INITIATIVES OF TECHNOLOGY GIANTS

  • 13.1. Chapter Overview
    • 13.1.1. Amazon Web Services
    • 13.1.2. Microsoft
    • 13.1.3. Intel
    • 13.1.4. Alibaba Cloud
    • 13.1.5. Siemens
    • 13.1.6. Google
    • 13.1.7. IBM

14. COST SAVING ANALYSIS

  • 14.1. Chapter Overview
  • 14.2. Key Assumptions and Methodology
  • 14.3. Overall Cost Saving Potential Associated with Use of AI-based Solutions in Drug Discovery
    • 14.3.1. Likely Cost Savings: Analysis by Drug Discovery Steps
      • 14.3.1.1. Likely Cost Savings During Target Identification / Validation
      • 14.3.1.2. Likely Cost Savings During Hit Generation / Lead Identification
      • 14.3.1.3. Likely Cost Savings During Lead Optimization
    • 14.3.2. Likely Cost Savings: Analysis by Target Therapeutic Area
      • 14.3.2.1. Likely Cost Savings for Drugs Targeting Oncological Disorders
      • 14.3.2.2. Likely Cost Savings for Drugs Targeting Neurological Disorders
      • 14.3.2.3. Likely Cost Savings for Drugs Targeting Infectious Diseases
      • 14.3.2.4. Likely Cost Savings for Drugs Targeting Respiratory Disorders
      • 14.3.2.5. Likely Cost Savings for Drugs Targeting Cardiovascular Disorders
      • 14.3.2.6. Likely Cost Savings for Drugs Targeting Endocrine Disorders
      • 14.3.2.7. Likely Cost Savings for Drugs Targeting Gastrointestinal Disorders
      • 14.3.2.8. Likely Cost Savings for Drugs Targeting Musculoskeletal Disorders
      • 14.3.2.9. Likely Cost Savings for Drugs Targeting Immunological Disorders
      • 14.3.2.10. Likely Cost Savings for Drugs Targeting Dermatological Disorders
      • 14.3.2.11. Likely Cost Savings for Drugs Targeting Other Disorders
    • 14.3.3. Likely Cost Savings: Analysis by Geography
      • 14.3.3.1. Likely Cost Savings in North America
      • 14.3.3.2. Likely Cost Savings in Europe
      • 14.3.3.3. Likely Cost Savings in Asia Pacific
      • 14.3.3.4. Likely Cost Savings in MENA
      • 14.3.3.5. Likely Cost Savings in Latin America
      • 14.3.3.6. Likely Cost Savings in Rest of the World

15. MARKET FORECAST

  • 15.1. Chapter Overview
  • 15.2. Key Assumptions and Methodology
  • 15.3. Global AI-based Drug Discovery Market
    • 15.3.1. AI-based Drug Discovery Market: Distribution by Drug Discovery Steps
      • 15.3.1.1. AI-based Drug Discovery Market for Target Identification / Validation
      • 15.3.1.2. AI-based Drug Discovery Market for Hit Generation / Lead Identification
      • 15.3.1.3. AI-based Drug Discovery Market for Lead Optimization
    • 15.3.2. AI-based Drug Discovery Market: Distribution by Target Therapeutic Area
      • 15.3.2.1. AI-based Drug Discovery Market for Oncological Disorders
      • 15.3.2.2. AI-based Drug Discovery Market for Neurological Disorders
      • 15.3.2.3. AI-based Drug Discovery Market for Infectious Diseases
      • 15.3.2.4. AI-based Drug Discovery Market for Respiratory Disorders
      • 15.3.2.5. AI-based Drug Discovery Market for Cardiovascular Disorders
      • 15.3.2.6. AI-based Drug Discovery Market for Endocrine Disorders
      • 15.3.2.7. AI-based Drug Discovery Market for Gastrointestinal Disorders
      • 15.3.2.8. AI-based Drug Discovery Market for Musculoskeletal Disorders
      • 15.3.2.9. AI-based Drug Discovery Market for Immunological Disorders
      • 15.3.2.10. AI-based Drug Discovery Market for Dermatological Disorders
      • 15.3.2.11. AI-based Drug Discovery Market for Other Disorders
    • 15.3.3. AI-based Drug Discovery Market: Distribution by Geography
      • 15.3.3.1. AI-based Drug Discovery Market in North America
        • 15.3.3.1.1. AI-based Drug Discovery Market in the US
        • 15.3.3.1.2. AI-based Drug Discovery Market in Canada
      • 15.3.3.2. AI-based Drug Discovery Market in Europe
        • 15.3.3.2.1. AI-based Drug Discovery Market in the UK
        • 15.3.3.2.2. AI-based Drug Discovery Market in France
        • 15.3.3.2.3. AI-based Drug Discovery Market in Germany
        • 15.3.3.2.4. AI-based Drug Discovery Market in Spain
        • 15.3.3.2.5. AI-based Drug Discovery Market in Italy
        • 15.3.3.2.6. AI-based Drug Discovery Market in Rest of Europe
      • 15.3.3.3. AI-based Drug Discovery Market in Asia Pacific
        • 15.3.3.3.1. AI-based Drug Discovery Market in China
        • 15.3.3.3.2. AI-based Drug Discovery Market in India
        • 15.3.3.3.3. AI-based Drug Discovery Market in Japan
        • 15.3.3.3.4. AI-based Drug Discovery Market in Australia
        • 15.3.3.3.5. AI-based Drug Discovery Market in South Korea
      • 15.3.3.4. AI-based Drug Discovery Market in MENA
        • 15.3.3.4.1. AI-based Drug Discovery Market in Saudi Arabia
        • 15.3.3.4.2. AI-based Drug Discovery Market in UAE
        • 15.3.3.4.3. AI-based Drug Discovery Market in Iran
      • 15.3.3.5. AI-based Drug Discovery Market in Latin America
        • 15.3.3.5.1. AI-based Drug Discovery Market in Argentina
      • 15.3.3.6. AI-based Drug Discovery Market in Rest of the World

16. CONCLUSION

17. EXECUTIVE INSIGHTS

  • 17.1. Chapter Overview
  • 17.2. Aigenpulse
    • 17.2.1. Company Snapshot
    • 17.2.2. Interview Transcript: Steve Yemm (Chief Commercial Officer) and Satnam Surae (Chief Product Officer)
  • 17.3. Cloud Pharmaceuticals
    • 17.3.1. Company Snapshot
    • 17.3.2. Interview Transcript: Ed Addison (Co-founder, Chairman and Chief Executive Officer)
  • 17.4. DEARGEN
    • 17.4.1. Company Snapshot
    • 17.4.2. Interview Transcript: Bo Ram Beck (Head Researcher)
  • 17.5. Intelligent Omics
    • 17.5.1. Company Snapshot
    • 17.5.2. Interview Transcript: Simon Haworth (Chief Executive Officer)
  • 17.6. Pepticom
    • 17.6.1. Company Snapshot
    • 17.6.2. Interview Transcript: Immanuel Lerner (Chief Executive Officer, Co-Founder)
  • 17.7. Sage-N Research
    • 17.7.1. Company Snapshot
    • 17.7.2. Interview Transcript: David Chiang (Chairman)

18. APPENDIX I: TABULATED DATA

19. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS