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市場調查報告書
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2007823

人工智慧驅動的藥物發現市場預測至2034年：全球按組件、技術、藥物類型、治療領域、應用、最終用戶和地區分類的分析

AI Driven Drug Discovery Market Forecasts to 2034 - Global Analysis By Component (Software and Services), Technology, Drug Type, Therapeutic Area, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，全球人工智慧驅動的藥物發現市場預計將在 2026 年達到 42 億美元，並在預測期內以 17.5% 的複合年成長率成長，到 2034 年達到 161 億美元。

人工智慧驅動的藥物發現是一項利用機器學習、深度學習和進階數據分析等人工智慧技術來增強和加速新藥研發的計畫。這些技術分析海量的生物學、化學和臨床數據，以識別有前景的藥物靶點，設計和最佳化分子化合物，並評估藥物的安全性和有效性。透過自動化複雜的研發方法並挖掘龐大資料集中的模式，人工智慧有助於降低傳統藥物研發所需的時間、成本和風險。

研發加速與成本壓力

製藥業正面臨巨大的壓力，需要縮短新藥上市所需的時間和資金投入。傳統上，新藥上市往往需要十多年時間，耗資超過26億美元。人工智慧平台正透過自動化標靶識別、早期預測藥物毒性以及最佳化臨床試驗設計，直接應對這項挑戰。機器學習演算法可以在幾天內（而非幾年）分析大量資料集，使企業能夠及早淘汰前景不佳的候選藥物，並將資源集中在最有希望的資產上。這種對提高研發效率的需求正迫使大型製藥公司將人工智慧解決方案整合到其整個藥物研發流程中，從而顯著提升營運效率。

數據可用性和互通性挑戰

人工智慧模型的有效性很大程度上取決於高品質、標準化且經過標註的資料集的可用性。然而，生物醫學資料領域往往支離破碎，包含不相容且分散的電子健康記錄、專有化學庫和非結構化的研究論文。對資料隱私、智慧財產權以及專有資料集孤島的擔憂進一步限制了穩健演算法的訓練。如果無法取得全面、乾淨且統一的數據，人工智慧模型就有可能產生偏差或不準確的預測，導致其潛力無法充分發揮，並減緩其在整個產業的應用。

開發針對複雜疾病的新治療方法和應用

隨著人工智慧演算法日益複雜，其應用範圍已從傳統的小分子藥物擴展到基因療法、RNA療法和抗體藥物複合體（ADC）等複雜治療方法，並湧現出巨大的機會。生成式人工智慧和深度學習正在引領新型生物製劑的設計，並揭示神經退化性疾病疾病和罕見遺傳疾病等多標靶疾病的複雜性。將多體學資料（基因體學、蛋白質體學）與人工智慧結合，能夠發現以往難以治療的全新藥物類別。這項技術將為專注於人工智慧的公司帶來龐大的新收入來源，並加速在歷來極具挑戰性的治療領域開發治療方法。

不斷演變的監管和檢驗框架

許多人工智慧演算法的「黑箱」特性對其廣泛應用構成重大威脅。美國食品藥物管理局（FDA）和歐洲藥品管理局（EMA）等監管機構正努力尋找檢驗和核准透過不透明的人工智慧流程發現的藥物的方法。目前，尚缺乏用於檢驗人工智慧產生的候選藥物的安全性、有效性和可重複性的標準化指南。人工智慧發明化合物的智慧財產權不確定性也進一步加劇了商業化策略的複雜性。隨著市場擴張，監管路徑的製定若出現延誤，或人工智慧預測的候選化合物在後期臨床試驗中失敗，都可能削弱投資人信心，並減緩市場成長動能。

新冠疫情的影響

新冠疫情加速了人工智慧驅動的藥物研發市場的發展，研究人員迫切需要快速解決方案。人工智慧平台被廣泛用於現有藥物的再利用以及針對SARS-CoV-2病毒設計新型抗病毒藥物，顯著縮短了早期藥物研發階段。這場危機展現了人工智慧前所未有的快速反應能力，促使創業投資和資金籌措合作激增。然而，供應鏈中斷和臨床資源的轉移最初阻礙了檢驗工作。疫情過後，該行業採取了更具韌性的策略，利用人工智慧已取得的成功，建立強大而靈活的藥物研發流程，以應對未來的流行病和慢性疾病。

在預測期內，機器學習領域預計將佔據最大的市場佔有率。

機器學習領域預計將在預測期內佔據最大的市場佔有率，因為它在複雜生物資料集的分析中發揮至關重要的作用。作為最成熟的人工智慧技術，機器學習演算法被廣泛應用於基因組學、蛋白質折疊和生物標記識別等領域的模式識別。其多功能性使其能夠應用於從標靶檢驗到臨床前建模的各個階段。

在預測期內，製藥公司板塊預計將呈現最高的複合年成長率。

在預測期內，受急需補充非專利藥物產品組合的驅動，製藥公司板塊預計將呈現最高的成長率。大型製藥企業正積極採用人工智慧來降低研發風險、簡化營運流程並降低臨床試驗的高失敗率。從內部研發轉向策略性收購人工智慧Start-Ups新創公司的混合模式，正在加速人工智慧的普及應用。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這主要得益於其成熟的製藥生態系統和人工智慧技術公司的高度集中。美國在研發投入方面處於主導地位，這得益於美國國立衛生研究院 (NIH) 的大力政府資助和有利的創業投資投資。大型製藥公司和科技巨頭在藥物研發平台上的合作，構成了一個強大的創新中心。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於快速的數位化和不斷壯大的合約研究組織（CRO）行業。中國、印度和韓國等國家正大力投資人工智慧基礎設施和生物資訊學，以降低生產成本並加速學名藥的研發。各國政府推行的「人工智慧醫療」措施正在培育本土Start-Ups生態系統並吸引外資。

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    • 根據產品系列、地理覆蓋範圍和策略聯盟對主要企業進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章：全球人工智慧驅動藥物發現市場：按組成部分分類

  • 軟體
    • 人工智慧藥物發現平台
    • 數據分析和建模軟體
    • 分子建模和模擬工具
  • 服務
    • 人工智慧諮詢服務
    • 資料處理和整合服務
    • 藥物發現支持服務

第6章：全球人工智慧驅動藥物發現市場：按技術分類

  • 機器學習
  • 深度學習
  • 自然語言處理（NLP）
  • 人工智慧世代
  • 電腦視覺
  • 其他人工智慧技術

第7章：全球人工智慧驅動藥物發現市場：按藥物類型分類

  • 低分子化合物
  • 聚合物化合物/生物製藥

第8章：全球人工智慧驅動藥物發現市場：按治療領域分類

  • 腫瘤學
  • 神經退化性疾病
  • 心血管疾病
  • 感染疾病
  • 代謝性疾病
  • 免疫學
  • 呼吸系統疾病
  • 其他治療領域

第9章：全球人工智慧驅動藥物發現市場：按應用領域分類

  • 目標識別與檢驗
  • 命中辨識/分子篩檢
  • 潛在客戶開發
  • 先導藥物最適化
  • 藥物再利用
  • 臨床前試驗
  • 臨床試驗的最佳化

第10章：全球人工智慧驅動藥物發現市場：按最終用戶分類

  • 製藥公司
  • 生技公司
  • 受託研究機構（CRO）
  • 學術研究機構
  • 其他最終用戶

第11章：全球人工智慧驅動藥物發現市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第12章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第13章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟、合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第14章：公司簡介

  • Insilico Medicine
  • BenevolentAI
  • Exscientia plc
  • Recursion Pharmaceuticals
  • Atomwise Inc.
  • Deep Genomics
  • Schrodinger, Inc.
  • Evotec SE
  • Valo Health
  • Verge Genomics
  • Healx
  • XtalPi
  • Standigm
  • Cyclica Inc.
  • Iktos
Product Code: SMRC34698

According to Stratistics MRC, the Global AI Driven Drug Discovery Market is accounted for $4.2 billion in 2026 and is expected to reach $16.1 billion by 2034 growing at a CAGR of 17.5% during the forecast period. AI-driven drug discovery involves the application of artificial intelligence technologies such as machine learning, deep learning, and advanced data analytics to enhance and accelerate the development of new medicines. These technologies analyze large volumes of biological, chemical, and clinical data to identify promising drug targets, design and optimize molecular compounds, and evaluate drug safety and effectiveness. By automating complex research processes and uncovering patterns within extensive datasets, AI helps reduce the time, cost, and risk traditionally associated with pharmaceutical research and drug development.

Market Dynamics:

Driver:

Accelerating R&D timelines and cost pressures

The pharmaceutical industry faces immense pressure to reduce the substantial time and financial investment required to bring a drug to market, which traditionally exceeds a decade and costs over $2.6 billion. AI-driven platforms directly address this by automating target identification, predicting drug toxicity early, and optimizing clinical trial designs. Machine learning algorithms can analyze vast datasets in days rather than years, allowing companies to fail unsuccessful candidates faster and focus resources on the most promising assets. This imperative to improve R&D productivity is compelling pharmaceutical giants to integrate AI solutions across their discovery pipelines, transforming operational efficiency.

Restraint:

Data availability and interoperability challenges

The effectiveness of AI models is heavily dependent on the availability of high-quality, standardized, and annotated datasets. However, the biomedical data landscape is often fragmented, consisting of disparate electronic health records, proprietary chemical libraries, and unstructured research papers that lack interoperability. Concerns regarding data privacy, intellectual property rights, and the siloed nature of proprietary datasets further restrict the training of robust algorithms. Without access to comprehensive, clean, and harmonized data, AI models risk generating biased or inaccurate predictions, which limits their full potential and slows down mainstream adoption across the industry.

Opportunity:

Expansion into novel therapeutic modalities and complex diseases

As AI algorithms become more sophisticated, there is a significant opportunity to apply them beyond traditional small molecules to complex modalities such as gene therapies, RNA therapeutics, and antibody-drug conjugates. Generative AI and deep learning are unlocking the ability to design novel biologics and navigate the complexities of multi-target diseases like neurodegeneration and rare genetic disorders. The integration of multi-omics data (genomics, proteomics) with AI is enabling the discovery of entirely new classes of drugs that were previously undruggable. This capability opens vast new revenue streams for AI-focused firms and accelerates the development of cures for historically challenging therapeutic areas.

Threat:

Evolving regulatory and validation frameworks

The "black box" nature of many AI algorithms poses a significant threat to widespread adoption, as regulatory bodies like the FDA and EMA grapple with how to validate and approve drugs discovered through opaque AI processes. There is currently a lack of standardized guidelines for verifying the safety, efficacy, and reproducibility of AI-generated drug candidates. Uncertainty surrounding intellectual property rights for AI-invented compounds further complicates commercialization strategies. As the market grows, any delays in establishing clear regulatory pathways or failures in AI-predicted candidates during late-stage trials could erode investor confidence and slow market momentum.

Covid-19 Impact

The COVID-19 pandemic served as a catalyst for the AI-driven drug discovery market, as researchers urgently sought rapid solutions. AI platforms were deployed extensively to repurpose existing drugs and design novel antivirals against the SARS-CoV-2 virus, significantly compressing the initial discovery phase. The crisis validated AI's capability to operate at unprecedented speeds, leading to a surge in venture capital funding and strategic partnerships. However, supply chain disruptions and the redirection of clinical resources initially hampered validation efforts. Post-pandemic, the industry has adopted a more resilient mindset, leveraging the proven success of AI to build robust, agile discovery pipelines for future pandemics and chronic diseases.

The Machine Learning segment is expected to be the largest during the forecast period

The Machine Learning segment is expected to account for the largest market share during the forecast period, due to its foundational role in analyzing complex biological datasets. As the most mature AI technology, ML algorithms are extensively used for pattern recognition in genomics, protein folding, and biomarker identification. Its versatility allows for application across various stages, from target validation to preclinical modeling.

The Pharmaceutical Companies segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the Pharmaceutical Companies segment is predicted to witness the highest growth rate, driven by the urgent need to replenish patent-expired drug portfolios. Major pharma players are aggressively adopting AI to de-risk R&D, streamline operations, and lower the high attrition rates associated with clinical trials. The shift from in-house R&D to hybrid models involving strategic acquisitions of AI-native startups is accelerating adoption.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, fuelled by a mature pharmaceutical ecosystem and high concentration of AI technology firms. The United States leads in R&D expenditure, supported by strong government funding through the NIH and favorable venture capital investments. The presence of major pharmaceutical companies and tech giants collaborating on drug discovery platforms creates a robust innovation hub.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, supported by rapid digitalization and a growing contract research organization (CRO) sector. Countries like China, India, and South Korea are investing heavily in AI infrastructure and bioinformatics to reduce manufacturing costs and accelerate generic drug development. Government initiatives promoting "AI for Healthcare" are fostering local startup ecosystems and attracting foreign investment.

Key players in the market

Some of the key players in AI Driven Drug Discovery Market include Insilico Medicine, BenevolentAI, Exscientia plc, Recursion Pharmaceuticals, Atomwise Inc., Deep Genomics, Schrodinger, Inc., Evotec SE, Valo Health, Verge Genomics, Healx, XtalPi, Standigm, Cyclica Inc., and Iktos.

Key Developments:

In March 2026, Insilico Medicine announced a strategic research collaboration with ASKA Pharmaceutical Co., Ltd., a specialized pharmaceutical company with a strong focus on internal medicine, obstetrics, and gynecology. This partnership aims to identify novel therapeutic targets with high drug development potential for challenging gynecological conditions, including endometriosis, uterine fibroids, and adenomyosis, by leveraging Insilico's proprietary AI-driven target identification engine, PandaOmics.

Components Covered:

  • Software
  • Services

Technologies Covered:

  • Machine Learning
  • Deep Learning
  • Natural Language Processing (NLP)
  • Generative AI
  • Computer Vision
  • Other AI Technologies

Drug Types Covered:

  • Small Molecules
  • Large Molecules / Biologics

Therapeutic Areas Covered:

  • Oncology
  • Neurodegenerative Diseases
  • Cardiovascular Diseases
  • Infectious Diseases
  • Metabolic Disorders
  • Immunology
  • Respiratory Diseases
  • Other Therapeutic Areas

Applications Covered:

  • Target Identification & Validation
  • Hit Identification / Molecule Screening
  • Lead Generation
  • Lead Optimization
  • Drug Repurposing
  • Preclinical Testing
  • Clinical Trial Optimization

End Users Covered:

  • Pharmaceutical Companies
  • Biotechnology Companies
  • Contract Research Organizations (CROs)
  • Academic & Research Institutes
  • Other End Users

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI Driven Drug Discovery Market, By Component

  • 5.1 Software
    • 5.1.1 AI Drug Discovery Platforms
    • 5.1.2 Data Analytics & Modeling Software
    • 5.1.3 Molecular Modeling & Simulation Tools
  • 5.2 Services
    • 5.2.1 AI Consulting Services
    • 5.2.2 Data Processing & Integration Services
    • 5.2.3 Drug Discovery Support Services

6 Global AI Driven Drug Discovery Market, By Technology

  • 6.1 Machine Learning
  • 6.2 Deep Learning
  • 6.3 Natural Language Processing (NLP)
  • 6.4 Generative AI
  • 6.5 Computer Vision
  • 6.6 Other AI Technologies

7 Global AI Driven Drug Discovery Market, By Drug Type

  • 7.1 Small Molecules
  • 7.2 Large Molecules / Biologics

8 Global AI Driven Drug Discovery Market, By Therapeutic Area

  • 8.1 Oncology
  • 8.2 Neurodegenerative Diseases
  • 8.3 Cardiovascular Diseases
  • 8.4 Infectious Diseases
  • 8.5 Metabolic Disorders
  • 8.6 Immunology
  • 8.7 Respiratory Diseases
  • 8.8 Other Therapeutic Areas

9 Global AI Driven Drug Discovery Market, By Application

  • 9.1 Target Identification & Validation
  • 9.2 Hit Identification / Molecule Screening
  • 9.3 Lead Generation
  • 9.4 Lead Optimization
  • 9.5 Drug Repurposing
  • 9.6 Preclinical Testing
  • 9.7 Clinical Trial Optimization

10 Global AI Driven Drug Discovery Market, By End User

  • 10.1 Pharmaceutical Companies
  • 10.2 Biotechnology Companies
  • 10.3 Contract Research Organizations (CROs)
  • 10.4 Academic & Research Institutes
  • 10.5 Other End Users

11 Global AI Driven Drug Discovery Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 Insilico Medicine
  • 14.2 BenevolentAI
  • 14.3 Exscientia plc
  • 14.4 Recursion Pharmaceuticals
  • 14.5 Atomwise Inc.
  • 14.6 Deep Genomics
  • 14.7 Schrodinger, Inc.
  • 14.8 Evotec SE
  • 14.9 Valo Health
  • 14.10 Verge Genomics
  • 14.11 Healx
  • 14.12 XtalPi
  • 14.13 Standigm
  • 14.14 Cyclica Inc.
  • 14.15 Iktos

List of Tables

  • Table 1 Global AI Driven Drug Discovery Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI Driven Drug Discovery Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI Driven Drug Discovery Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global AI Driven Drug Discovery Market Outlook, By AI Drug Discovery Platforms (2023-2034) ($MN)
  • Table 5 Global AI Driven Drug Discovery Market Outlook, By Data Analytics & Modeling Software (2023-2034) ($MN)
  • Table 6 Global AI Driven Drug Discovery Market Outlook, By Molecular Modeling & Simulation Tools (2023-2034) ($MN)
  • Table 7 Global AI Driven Drug Discovery Market Outlook, By Services (2023-2034) ($MN)
  • Table 8 Global AI Driven Drug Discovery Market Outlook, By AI Consulting Services (2023-2034) ($MN)
  • Table 9 Global AI Driven Drug Discovery Market Outlook, By Data Processing & Integration Services (2023-2034) ($MN)
  • Table 10 Global AI Driven Drug Discovery Market Outlook, By Drug Discovery Support Services (2023-2034) ($MN)
  • Table 11 Global AI Driven Drug Discovery Market Outlook, By Technology (2023-2034) ($MN)
  • Table 12 Global AI Driven Drug Discovery Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 13 Global AI Driven Drug Discovery Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 14 Global AI Driven Drug Discovery Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 15 Global AI Driven Drug Discovery Market Outlook, By Generative AI (2023-2034) ($MN)
  • Table 16 Global AI Driven Drug Discovery Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 17 Global AI Driven Drug Discovery Market Outlook, By Other AI Technologies (2023-2034) ($MN)
  • Table 18 Global AI Driven Drug Discovery Market Outlook, By Drug Type (2023-2034) ($MN)
  • Table 19 Global AI Driven Drug Discovery Market Outlook, By Small Molecules (2023-2034) ($MN)
  • Table 20 Global AI Driven Drug Discovery Market Outlook, By Large Molecules / Biologics (2023-2034) ($MN)
  • Table 21 Global AI Driven Drug Discovery Market Outlook, By Therapeutic Area (2023-2034) ($MN)
  • Table 22 Global AI Driven Drug Discovery Market Outlook, By Oncology (2023-2034) ($MN)
  • Table 23 Global AI Driven Drug Discovery Market Outlook, By Neurodegenerative Diseases (2023-2034) ($MN)
  • Table 24 Global AI Driven Drug Discovery Market Outlook, By Cardiovascular Diseases (2023-2034) ($MN)
  • Table 25 Global AI Driven Drug Discovery Market Outlook, By Infectious Diseases (2023-2034) ($MN)
  • Table 26 Global AI Driven Drug Discovery Market Outlook, By Metabolic Disorders (2023-2034) ($MN)
  • Table 27 Global AI Driven Drug Discovery Market Outlook, By Immunology (2023-2034) ($MN)
  • Table 28 Global AI Driven Drug Discovery Market Outlook, By Respiratory Diseases (2023-2034) ($MN)
  • Table 29 Global AI Driven Drug Discovery Market Outlook, By Other Therapeutic Areas (2023-2034) ($MN)
  • Table 30 Global AI Driven Drug Discovery Market Outlook, By Application (2023-2034) ($MN)
  • Table 31 Global AI Driven Drug Discovery Market Outlook, By Target Identification & Validation (2023-2034) ($MN)
  • Table 32 Global AI Driven Drug Discovery Market Outlook, By Hit Identification / Molecule Screening (2023-2034) ($MN)
  • Table 33 Global AI Driven Drug Discovery Market Outlook, By Lead Generation (2023-2034) ($MN)
  • Table 34 Global AI Driven Drug Discovery Market Outlook, By Lead Optimization (2023-2034) ($MN)
  • Table 35 Global AI Driven Drug Discovery Market Outlook, By Drug Repurposing (2023-2034) ($MN)
  • Table 36 Global AI Driven Drug Discovery Market Outlook, By Preclinical Testing (2023-2034) ($MN)
  • Table 37 Global AI Driven Drug Discovery Market Outlook, By Clinical Trial Optimization (2023-2034) ($MN)
  • Table 38 Global AI Driven Drug Discovery Market Outlook, By End User (2023-2034) ($MN)
  • Table 39 Global AI Driven Drug Discovery Market Outlook, By Pharmaceutical Companies (2023-2034) ($MN)
  • Table 40 Global AI Driven Drug Discovery Market Outlook, By Biotechnology Companies (2023-2034) ($MN)
  • Table 41 Global AI Driven Drug Discovery Market Outlook, By Contract Research Organizations (CROs) (2023-2034) ($MN)
  • Table 42 Global AI Driven Drug Discovery Market Outlook, By Academic & Research Institutes (2023-2034) ($MN)
  • Table 43 Global AI Driven Drug Discovery Market Outlook, By Other End Users (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.