查看購物車
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
2149710

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

AI-Driven Drug Discovery Market Forecasts to 2034 - Global Analysis By Offering, Technology, Application, Drug Type, Therapeutic Area, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球人工智慧驅動的藥物發現市場將達到 105 億美元，並在預測期內以 9.3% 的複合年成長率成長，到 2034 年達到 215 億美元。

人工智慧驅動的藥物發現是指應用計算演算法和機器學習模型來識別、設計和最佳化新型治療化合物。其工作原理是透過分析龐大的生物資料集來預測分子相互作用、毒性和療效，從而加速發現有前景的候選藥物。這些系統利用深度學習、自然語言處理和生成模型來簡化標靶檢驗和先導化合物最佳化。這種方法可以顯著降低傳統藥物研發開發平臺所需的時間和成本。

藥品研發成本飆升

傳統藥物研發成本的飆升迫使企業採用人工智慧驅動的藥物發現平台，以提供更有效率的化合物篩選替代方案。標靶檢驗日益複雜以及臨床試驗的高失敗率正在加速這些計算工具融入藥物開發框架。機器學習技術的進步提高了預測準確性，為此轉變提供了支援。因此，製藥公司正在投資人工智慧技術，以在滿足效率標準的同時最佳化整體研發支出。

數據品質和整合方面的挑戰

整合和標準化各種生物資料集所面臨的巨大挑戰，是商業性應用的主要障礙。開發高精度預測模型通常需要複雜的資料清洗流程和先進的整合技術，推高了整體開發成本。此外，資料格式的多樣性也限制了通用人工智慧演算法的運作可靠性。這些因素共同限制了市場擴張，尤其對於數據基礎設施預算有限的中小型企業而言更是如此。

利用生成式人工智慧拓展分子設計

由於對新型化合物的需求日益成長，生成式人工智慧領域為藥物發現平台提供者帶來了巨大的成長機會。先進的生成模型以深度學習演算法為主要輸入，無需依賴現有化合物庫即可有效地設計獨特的分子結構。隨著全球對計算化學投資的不斷增加，先進生成式人工智慧解決方案的應用預計將顯著成長。

與傳統高通量篩檢的競爭

對傳統高通量篩檢調查方法的持續依賴對人工智慧驅動的藥物發現市場構成重大威脅。傳統的實體篩檢方法和成熟的機器人平台通常更易於理解監管規定，對於擁有現有基礎設施的機構而言，它們可能是更經濟可行的選擇。此外，自動化篩檢技術的快速發展正在提高傳統藥物發現方法的效率。這種競爭可能會阻礙市場滲透。

新型冠狀病毒（COVID-19）的影響：

疫情初期，由於實驗室關閉，實驗室運作受到干擾，導致臨床試驗延期。然而，隨後對快速藥物研發的需求加速了人工智慧驅動的藥物發現平台在虛擬篩檢和化合物最佳化方面的應用。疫情後，人們對疫情防範和加速藥物研發的關注，加強了對人工智慧技術的長期投資，推動了全球各製藥領域的強勁市場復甦和擴張。

在預測期內，軟體領域預計將佔據最大的市場佔有率。

由於軟體在藥物研發領域的基礎性作用及其在各種藥物研發環境中的廣泛適用性，預計在預測期內，軟體領域將佔據最大的市場佔有率。軟體平台具有卓越的運算可擴充性，能夠有效率地分析海量生物資料集。這顯著縮短了藥物研發週期，並最大限度地減少了日常研究方法中的人工干預。隨著業界日益重視高效的藥物研發方法，對專業人工智慧軟體的需求持續激增，鞏固了其市場主導地位。

預計在預測期內，生成式人工智慧領域將呈現最高的複合年成長率。

在預測期內，生成式人工智慧領域預計將呈現最高的成長率，這主要得益於深度學習架構和分子設計工程的快速發展。這些技術能夠精準地合成新型化合物，從而產生高度專業化且穩健的治療效果，並可針對特定疾病進行客製化。透過整合先進的生成式人工智慧技術，藥物發現速度、營運規模和分子多樣性的提升將顯著提高製程經濟性，並加速全球商業性部署。

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，這得益於其成熟的製藥產業和先進的運算基礎設施，這些都為人工智慧驅動的藥物研發提供了強力的支持。該地區受益於大量的研發投入、健全的智慧財產權保護以及政府對醫療保健領域技術創新的支持。此外，美國和加拿大主要產業參與者對先進人工智慧平台的早期採用，進一步鞏固了該地區的全球主導地位。

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，這主要得益於新興經濟體製藥行業的快速現代化以及生物技術行業的擴張。中國、印度和日本等國家正增加對運算基礎設施和人工智慧驅動的研究技術的投資，以滿足日益成長的國內醫療保健需求和日益嚴格的效率監管要求。此外，有利的政府政策、不斷成長的外國直接投資以及經濟高效的技術資源的可用性，正在加速人工智慧驅動的藥物研發在全部區域的應用。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 對主要公司進行SWOT分析（最多3家公司）
  • 區域細分
    • 根據客戶要求，我們可以提供主要國家的市場估算和預測，以及複合年成長率（註：需經可行性確認）。
  • 競爭性標竿分析
    • 根據產品系列、企業發展和策略聯盟對重點公司進行基準分析。

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
  • 主要公司市佔率分析
  • 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：按產品/服務分類

  • 軟體
  • 服務
  • 人工智慧藥物發現平台
  • 人工智慧藥物發現引擎
  • 基於雲端的藥物發現解決方案
  • 人工智慧驅動的藥物發現諮詢服務
  • 整合式人工智慧藥物發現解決方案

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
  • 電腦視覺
  • 人工智慧世代
  • 知識圖譜與圖神經網路

第7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
  • 虛擬篩檢與核磁共振篩檢
  • 德諾和製藥
  • 先導化合物發現與最佳化
  • 藥物再利用
  • 臨床前試驗和預測
  • 生物標記發現

第8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
  • 生物製劑
  • 抗體
  • 胜肽和蛋白質
  • 基於核酸的治療藥物
  • 細胞和基因治療
  • 其他藥物類型

第9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
  • 代謝性疾病
  • 感染疾病
  • 免疫疾病
  • 罕見疾病
  • 呼吸系統疾病
  • 其他治療領域

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
  • 合約研發生產組織（CDMO）
  • 學術研究機構
  • 政府附屬研究機構
  • 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、聯盟和合資企業
  • 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

  • Schrodinger, Inc.
  • Recursion Pharmaceuticals, Inc.
  • Exscientia plc
  • Insilico Medicine
  • Tempus AI, Inc.
  • Relay Therapeutics, Inc.
  • Absci Corporation
  • Owkin, Inc.
  • Structure Therapeutics Inc.
  • NVIDIA Corporation
  • International Business Machines Corporation
  • Microsoft Corporation
  • Alphabet Inc.
  • Amazon.com, Inc.
  • Dassault Systemes SE
  • Merck KGaA
  • Illumina, Inc.
  • AbCellera Biologics Inc.
Product Code: SMRC40126

According to Stratistics MRC, the Global AI-Driven Drug Discovery Market is accounted for $10.5 billion in 2026 and is expected to reach $21.5 billion by 2034 growing at a CAGR of 9.3% during the forecast period. AI-driven drug discovery refers to the application of computational algorithms and machine learning models to identify, design, and optimize novel therapeutic compounds. It functions by analyzing vast biological datasets to predict molecular interactions, toxicity, and efficacy, thereby accelerating the identification of viable drug candidates. These systems utilize deep learning, natural language processing, and generative models to streamline target validation and lead optimization. This approach significantly reduces the time and cost associated with traditional pharmaceutical research and development pipelines.

Market Dynamics:

Driver:

Rising Pharmaceutical R&D Costs

The escalating costs of traditional pharmaceutical research compel companies to adopt AI-driven discovery platforms offering efficient compound identification alternatives. Growing complexity in target validation and high failure rates in clinical trials are accelerating the integration of these computational tools into drug development frameworks. This transition is supported by advancements in machine learning, which enhance predictive accuracy. Consequently, pharmaceutical firms are investing in AI technologies to achieve compliance with efficiency benchmarks while optimizing overall R&D expenditures.

Restraint:

Data Quality and Integration Challenges

The substantial difficulties associated with aggregating and standardizing diverse biological datasets represent a significant barrier to widespread commercial adoption. Developing highly accurate predictive models often requires complex data cleaning processes and sophisticated integration techniques, which escalate overall development costs. Furthermore, the variability in data formats limits the operational reliability of generalized AI algorithms. These factors collectively constrain market expansion, particularly for smaller enterprises with limited data infrastructure budgets.

Opportunity:

Expansion in Generative AI Molecule Design

The generative AI sector presents substantial growth opportunities for drug discovery platform providers due to increasing demand for novel compound creation. Advanced generative models offer a highly effective pathway to design unique molecular structures without relying on existing libraries, utilizing deep learning algorithms as primary inputs. As global investments in computational chemistry expand, the adoption of advanced generative AI solutions is expected to surge significantly.

Threat:

Competition from Traditional High-Throughput Screening

The continuous reliance on traditional high-throughput screening methodologies poses a considerable threat to the AI-driven drug discovery market. Conventional physical screening assays and established robotic platforms often exhibit superior regulatory familiarity and can be more economically viable for facilities with existing infrastructure. Additionally, the rapid advancement of automated screening technologies is enhancing the efficiency of conventional discovery methods. This competitive pressure may hinder market penetration.

Covid-19 Impact:

The pandemic initially disrupted laboratory operations and delayed clinical trials due to facility closures. However, the subsequent need for rapid therapeutic development accelerated the adoption of AI-driven discovery platforms for virtual screening and compound optimization. Post-pandemic, the heightened focus on pandemic preparedness and accelerated drug development has reinforced long-term investments in AI technologies, driving robust market recovery and expansion across diverse pharmaceutical sectors globally.

The software segment is expected to be the largest during the forecast period

The software segment is expected to account for the largest market share during the forecast period, due to its foundational role and widespread applicability across diverse pharmaceutical research environments. Software platforms offer exceptional computational scalability and operate effectively in analyzing massive biological datasets, which significantly reduces discovery timelines and minimizes manual intervention in daily research processes. As industries increasingly prioritize efficient drug development methods, the demand for specialized AI software continues to surge, thereby solidifying its dominant market position.

The generative AI segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the generative AI segment is predicted to witness the highest growth rate, driven by rapid advancements in deep learning architectures and molecular design engineering. These technologies enable the precise creation of novel compounds to produce highly specialized and robust therapeutic outputs tailored for specific disease applications. The ability to enhance discovery speed, operational scalability, and molecular diversity through advanced generative integration significantly improves process economics, accelerating commercial adoption globally.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to the presence of well-established pharmaceutical industries and advanced computational infrastructure that heavily utilize AI-driven drug discovery. The region benefits from substantial research and development investments, robust intellectual property protection, and supportive government initiatives promoting technological innovation in healthcare. Furthermore, the early adoption of advanced AI platforms by key industry players in the United States and Canada reinforces the region's dominant position globally.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, due to rapid pharmaceutical modernization and expanding biotechnology sectors in emerging economies. Countries such as China, India, and Japan are increasingly investing in computational infrastructure and AI-driven research technologies to meet growing domestic healthcare demands and stringent efficiency regulations. Additionally, favorable government policies, rising foreign direct investment, and the availability of cost-effective technological resources are collectively driving the accelerated adoption of AI drug discovery across the region.

Key players in the market

Some of the key players in AI-Driven Drug Discovery Market include Schrodinger, Inc., Recursion Pharmaceuticals, Inc., Exscientia plc, Insilico Medicine, Tempus AI, Inc., Relay Therapeutics, Inc., Absci Corporation, Owkin, Inc., Structure Therapeutics Inc., NVIDIA Corporation, International Business Machines Corporation, Microsoft Corporation, Alphabet Inc., Amazon.com, Inc., Dassault Systemes SE, Merck KGaA, Illumina, Inc., and AbCellera Biologics Inc.

Key Developments:

In September 2026, Schrodinger, Inc. launched a next-generation generative AI platform optimized for novel molecule design, achieving a thirty percent improvement in compound viability while significantly reducing computational time requirements for global pharmaceutical researchers.

In August 2026, Recursion Pharmaceuticals, Inc. expanded its automated biology capacity through a strategic partnership with a leading tech firm, enabling the scalable deployment of novel phenotypic screening modules for drug discovery.

In July 2026, Exscientia plc secured a major supply agreement to provide customized AI-driven drug design services for a prominent biotech company, facilitating the efficient integration of advanced computational tools into next-generation therapeutic ecosystems globally.

Offerings Covered:

  • Software
  • Services
  • AI Drug Discovery Platforms
  • AI Drug Discovery Engines
  • Cloud-Based Drug Discovery Solutions
  • AI-Enabled Drug Discovery Consulting Services
  • Integrated AI Drug Discovery Solutions

Technologies Covered:

  • Machine Learning
  • Deep Learning
  • Natural Language Processing
  • Computer Vision
  • Generative AI
  • Knowledge Graphs and Graph Neural Networks

Applications Covered:

  • Target Identification and Validation
  • Virtual Screening and Molecule Screening
  • De Novo Drug Design
  • Lead Discovery and Optimization
  • Drug Repurposing
  • Preclinical Testing and Prediction
  • Biomarker Discovery

Drug Types Covered:

  • Small Molecules
  • Biologics
  • Antibodies
  • Peptides and Proteins
  • Nucleic Acid-Based Therapeutics
  • Cell and Gene Therapies
  • Other Drug Types

Therapeutic Areas Covered:

  • Oncology
  • Neurodegenerative Diseases
  • Cardiovascular Diseases
  • Metabolic Diseases
  • Infectious Diseases
  • Immunological Diseases
  • Rare Diseases
  • Respiratory Diseases
  • Other Therapeutic Areas

End Users Covered:

  • Pharmaceutical Companies
  • Biotechnology Companies
  • Contract Research Organizations
  • Contract Development and Manufacturing Organizations
  • Academic and Research Institutes
  • Government Research Organizations
  • 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 Offering

  • 5.1 Software
  • 5.2 Services
  • 5.3 AI Drug Discovery Platforms
  • 5.4 AI Drug Discovery Engines
  • 5.5 Cloud-Based Drug Discovery Solutions
  • 5.6 AI-Enabled Drug Discovery Consulting Services
  • 5.7 Integrated AI Drug Discovery Solutions

6 Global AI-Driven Drug Discovery Market, By Technology

  • 6.1 Machine Learning
  • 6.2 Deep Learning
  • 6.3 Natural Language Processing
  • 6.4 Computer Vision
  • 6.5 Generative AI
  • 6.6 Knowledge Graphs and Graph Neural Networks

7 Global AI-Driven Drug Discovery Market, By Application

  • 7.1 Target Identification and Validation
  • 7.2 Virtual Screening and Molecule Screening
  • 7.3 De Novo Drug Design
  • 7.4 Lead Discovery and Optimization
  • 7.5 Drug Repurposing
  • 7.6 Preclinical Testing and Prediction
  • 7.7 Biomarker Discovery

8 Global AI-Driven Drug Discovery Market, By Drug Type

  • 8.1 Small Molecules
  • 8.2 Biologics
  • 8.3 Antibodies
  • 8.4 Peptides and Proteins
  • 8.5 Nucleic Acid-Based Therapeutics
  • 8.6 Cell and Gene Therapies
  • 8.7 Other Drug Types

9 Global AI-Driven Drug Discovery Market, By Therapeutic Area

  • 9.1 Oncology
  • 9.2 Neurodegenerative Diseases
  • 9.3 Cardiovascular Diseases
  • 9.4 Metabolic Diseases
  • 9.5 Infectious Diseases
  • 9.6 Immunological Diseases
  • 9.7 Rare Diseases
  • 9.8 Respiratory Diseases
  • 9.9 Other Therapeutic Areas

10 Global AI-Driven Drug Discovery Market, By End User

  • 10.1 Pharmaceutical Companies
  • 10.2 Biotechnology Companies
  • 10.3 Contract Research Organizations
  • 10.4 Contract Development and Manufacturing Organizations
  • 10.5 Academic and Research Institutes
  • 10.6 Government Research Organizations
  • 10.7 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 Schrodinger, Inc.
  • 14.2 Recursion Pharmaceuticals, Inc.
  • 14.3 Exscientia plc
  • 14.4 Insilico Medicine
  • 14.5 Tempus AI, Inc.
  • 14.6 Relay Therapeutics, Inc.
  • 14.7 Absci Corporation
  • 14.8 Owkin, Inc.
  • 14.9 Structure Therapeutics Inc.
  • 14.10 NVIDIA Corporation
  • 14.11 International Business Machines Corporation
  • 14.12 Microsoft Corporation
  • 14.13 Alphabet Inc.
  • 14.14 Amazon.com, Inc.
  • 14.15 Dassault Systemes SE
  • 14.16 Merck KGaA
  • 14.17 Illumina, Inc.
  • 14.18 AbCellera Biologics Inc.

List of Tables

  • Table 1 Global AI-Driven Drug Discovery Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI-Driven Drug Discovery Market Outlook, By Offering (2023-2034) ($MN)
  • Table 3 Global AI-Driven Drug Discovery Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global AI-Driven Drug Discovery Market Outlook, By Services (2023-2034) ($MN)
  • Table 5 Global AI-Driven Drug Discovery Market Outlook, By AI Drug Discovery Platforms (2023-2034) ($MN)
  • Table 6 Global AI-Driven Drug Discovery Market Outlook, By AI Drug Discovery Engines (2023-2034) ($MN)
  • Table 7 Global AI-Driven Drug Discovery Market Outlook, By Cloud-Based Drug Discovery Solutions (2023-2034) ($MN)
  • Table 8 Global AI-Driven Drug Discovery Market Outlook, By AI-Enabled Drug Discovery Consulting Services (2023-2034) ($MN)
  • Table 9 Global AI-Driven Drug Discovery Market Outlook, By Integrated AI Drug Discovery Solutions (2023-2034) ($MN)
  • Table 10 Global AI-Driven Drug Discovery Market Outlook, By Technology (2023-2034) ($MN)
  • Table 11 Global AI-Driven Drug Discovery Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 12 Global AI-Driven Drug Discovery Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 13 Global AI-Driven Drug Discovery Market Outlook, By Natural Language Processing (2023-2034) ($MN)
  • Table 14 Global AI-Driven Drug Discovery Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 15 Global AI-Driven Drug Discovery Market Outlook, By Generative AI (2023-2034) ($MN)
  • Table 16 Global AI-Driven Drug Discovery Market Outlook, By Knowledge Graphs and Graph Neural Networks (2023-2034) ($MN)
  • Table 17 Global AI-Driven Drug Discovery Market Outlook, By Application (2023-2034) ($MN)
  • Table 18 Global AI-Driven Drug Discovery Market Outlook, By Target Identification and Validation (2023-2034) ($MN)
  • Table 19 Global AI-Driven Drug Discovery Market Outlook, By Virtual Screening and Molecule Screening (2023-2034) ($MN)
  • Table 20 Global AI-Driven Drug Discovery Market Outlook, By De Novo Drug Design (2023-2034) ($MN)
  • Table 21 Global AI-Driven Drug Discovery Market Outlook, By Lead Discovery and Optimization (2023-2034) ($MN)
  • Table 22 Global AI-Driven Drug Discovery Market Outlook, By Drug Repurposing (2023-2034) ($MN)
  • Table 23 Global AI-Driven Drug Discovery Market Outlook, By Preclinical Testing and Prediction (2023-2034) ($MN)
  • Table 24 Global AI-Driven Drug Discovery Market Outlook, By Biomarker Discovery (2023-2034) ($MN)
  • Table 25 Global AI-Driven Drug Discovery Market Outlook, By Drug Type (2023-2034) ($MN)
  • Table 26 Global AI-Driven Drug Discovery Market Outlook, By Small Molecules (2023-2034) ($MN)
  • Table 27 Global AI-Driven Drug Discovery Market Outlook, By Biologics (2023-2034) ($MN)
  • Table 28 Global AI-Driven Drug Discovery Market Outlook, By Antibodies (2023-2034) ($MN)
  • Table 29 Global AI-Driven Drug Discovery Market Outlook, By Peptides and Proteins (2023-2034) ($MN)
  • Table 30 Global AI-Driven Drug Discovery Market Outlook, By Nucleic Acid-Based Therapeutics (2023-2034) ($MN)
  • Table 31 Global AI-Driven Drug Discovery Market Outlook, By Cell and Gene Therapies (2023-2034) ($MN)
  • Table 32 Global AI-Driven Drug Discovery Market Outlook, By Other Drug Types (2023-2034) ($MN)
  • Table 33 Global AI-Driven Drug Discovery Market Outlook, By Therapeutic Area (2023-2034) ($MN)
  • Table 34 Global AI-Driven Drug Discovery Market Outlook, By Oncology (2023-2034) ($MN)

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