PUBLISHER: Global Market Insights Inc. | PRODUCT CODE: 1858963
PUBLISHER: Global Market Insights Inc. | PRODUCT CODE: 1858963
Artificial Intelligence in Drug Discovery Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2025 - 2034
The Global Artificial Intelligence in Drug Discovery Market was valued at USD 3.6 billion in 2024 and is estimated to grow at a CAGR of 30.1% to reach USD 49.5 billion by 2034.
This exceptional growth is being driven by the rising incidence of complex and chronic health conditions, combined with increasing interest from pharmaceutical companies in streamlining the drug development process using AI-driven platforms. The demand for faster, more accurate discovery processes is pushing biotech firms and research institutions to integrate advanced technologies like deep learning and predictive analytics into their R&D workflows. Additionally, ongoing innovation in data integration, growing digital infrastructure, and greater awareness among stakeholders are accelerating adoption. Expanding collaboration between AI startups and pharmaceutical manufacturers, particularly in technologically advanced regions, is reshaping how therapeutics are identified and developed, opening new possibilities across the healthcare ecosystem. AI's role in the drug discovery space involves using sophisticated technologies such as natural language processing, generative algorithms, and deep learning tools to enhance target validation, optimize lead compounds, and support efficient clinical trial planning.
| Market Scope | |
|---|---|
| Start Year | 2024 |
| Forecast Year | 2025-2034 |
| Start Value | $3.6 Billion |
| Forecast Value | $49.5 Billion |
| CAGR | 30.1% |
The software segment held 68.1% share in 2024. This dominance is primarily due to widespread adoption during early drug development stages such as compound screening and structure-activity predictions. Software-based AI tools are now integral in providing automation, accuracy, and scalability, meeting the growing demand among pharma companies for efficient R&D workflows. Rapid advancements in core technologies like NLP and neural networks are pushing the boundaries of what software can deliver in precision medicine.
The machine learning segment held a 62.5% share in 2024, owing to its extensive utility across various stages of drug discovery. This segment encompasses supervised and unsupervised learning models along with other ML algorithms. Cloud computing improvements and the availability of open-source frameworks are enabling more flexible, fast, and scalable model training and deployment. Ongoing collaborations between pharmaceutical giants and AI-focused firms continue to spark innovation in model design and accelerate the development of predictive tools and real-time analytics across discovery pipelines.
North America Artificial Intelligence in Drug Discovery Market held 47.6% share in 2024, propelled by strong R&D investments, broad digital infrastructure, and favorable regulatory frameworks supporting AI integration. Government-backed initiatives and regulatory clarity around digital therapeutics are encouraging market growth. Major collaborations between tech firms and pharma companies in the U.S. and Canada are driving progress in the creation of advanced drug discovery solutions and deepening the regional footprint of AI platforms.
Key players in the Global Artificial Intelligence in Drug Discovery Market are Exscientia, BenevolentAI, Orakl Oncology, AVAYL, Atomwise, Aevai Health, Cyclica, Examol, IBM Corporation, NVIDIA Corporation, Microsoft, Insilico Medicine, Deep Genomics, DenovAI Biotech, chAIron, Aureka Biotechnologies, LinkGevity, 9Bio Therapeutics, Helical, Google (DeepMind), and Deargen. To secure a competitive edge in the Global Artificial Intelligence in Drug Discovery Market, companies are focusing on technology innovation, strategic collaborations, and data-driven product development. Leading players are investing in the development of proprietary AI algorithms that enhance target identification, molecule generation, and clinical success rates. Partnerships between biotech startups and pharma leaders are becoming more prevalent, enabling access to large datasets, domain knowledge, and scalable computing resources.
Table of Contents
Chapter 1 Methodology and Scope
- 1.1 Market scope and definitions
- 1.2 Research design
- 1.2.1 Research approach
- 1.2.2 Data collection methods
- 1.3 Data mining sources
- 1.3.1 Global
- 1.3.2 Regional/country
- 1.4 Base estimates and calculations
- 1.4.1 Base year calculation
- 1.4.2 Key trends for market estimation
- 1.5 Primary research and validation
- 1.5.1 Primary sources
- 1.6 Forecast model
- 1.7 Research assumption and limitations
Chapter 2 Executive Summary
- 2.1 Industry 3600 synopsis
- 2.2 Key market trends
- 2.2.1 Regional trends
- 2.2.2 Component trends
- 2.2.3 Technology trends
- 2.2.4 Application type trends
- 2.2.5 Therapeutic area trends
- 2.2.6 End Use trends
- 2.3 CXO perspectives: Strategic imperatives
- 2.3.1 Key decision points for industry executives
- 2.3.2 Critical success factors for market players
- 2.4 Future outlook and strategic recommendations
Chapter 3 Industry Insights
- 3.1 Industry ecosystem analysis
- 3.1.1 Supplier landscape
- 3.1.2 Value addition at each stage
- 3.1.3 Factor affecting the value chain
- 3.2 Industry impact forces
- 3.2.1 Growth drivers
- 3.2.1.1 Increasing prevalence of complex and chronic diseases
- 3.2.1.2 Data explosion and digitization in healthcare
- 3.2.1.3 Advancements in AI algorithms and computing power
- 3.2.1.4 Growing collaboration between tech and pharma companies
- 3.2.2 Industry pitfalls and challenges
- 3.2.2.1 Data quality and integration issues
- 3.2.2.2 Regulatory and ethical concerns
- 3.2.3 Market opportunities
- 3.2.3.1 Expansion of personalized and precision medicine
- 3.2.3.2 Emergence of generative AI in molecule design
- 3.2.1 Growth drivers
- 3.3 Growth potential analysis
- 3.4 Regulatory landscape
- 3.4.1 North America
- 3.4.2 Europe
- 3.4.3 Asia Pacific
- 3.4.4 Latin America
- 3.4.5 Middle East and Africa
- 3.5 Future market trends
- 3.6 Technological landscape
- 3.6.1 Current technologies
- 3.6.2 Emerging technologies
- 3.7 Patent analysis
- 3.8 Investment and funding landscape
- 3.9 Porter's analysis
- 3.10 PESTEL analysis
Chapter 4 Competitive Landscape, 2024
- 4.1 Introduction
- 4.2 Company market share analysis
- 4.2.1 Global
- 4.2.2 North America
- 4.2.3 Europe
- 4.3 Company matrix analysis
- 4.4 Competitive analysis of major market players
- 4.5 Competitive positioning matrix
- 4.6 Key developments
- 4.6.1 Merger and acquisition
- 4.6.2 Partnership and collaboration
- 4.6.3 New product launches
- 4.6.4 Expansion plans
Chapter 5 Market Estimates and Forecast, By Component, 2021 - 2034 ($ Mn)
- 5.1 Key trends
- 5.2 Software
- 5.2.1 On-premises
- 5.2.2 Cloud-based
- 5.3 Services
Chapter 6 Market Estimates and Forecast, By Technology, 2021 - 2034 ($ Mn)
- 6.1 Key trends
- 6.2 Machine learning
- 6.3 Deep learning
- 6.3.1 Supervised learning
- 6.3.2 Unsupervised learning
- 6.3.3 Other machine learning technologies
- 6.4 Other technology
Chapter 7 Market Estimates and Forecast, By Application Type, 2021 - 2034 ($ Mn)
- 7.1 Key trends
- 7.2 Molecular library screening
- 7.3 Target Identification
- 7.4 Drug optimization and repurposing
- 7.5 De novo drug designing
- 7.6 Preclinical testing
- 7.7 Other applications
Chapter 8 Market Estimates and Forecast, By Therapeutic Area, 2021 - 2034 ($ Mn)
- 8.1 Key trends
- 8.2 Oncology
- 8.3 Neurodegenerative diseases
- 8.4 Inflammatory
- 8.5 Infectious diseases
- 8.6 Metabolic diseases
- 8.7 Rare diseases
- 8.8 Cardiovascular diseases
- 8.9 Other therapeutic areas
Chapter 9 Market Estimates and Forecast, By End Use, 2021 - 2034 ($ Mn)
- 9.1 Key trends
- 9.2 Pharmaceutical and biotechnology companies
- 9.3 Contract research organization (CROs)
- 9.4 Other End uses
Chapter 10 Market Estimates and Forecast, By Region, 2021 - 2034 ($ Mn)
- 10.1 Key trends
- 10.2 North America
- 10.2.1 U.S.
- 10.2.2 Canada
- 10.3 Europe
- 10.3.1 Germany
- 10.3.2 UK
- 10.3.3 France
- 10.3.4 Spain
- 10.3.5 Italy
- 10.3.6 Netherlands
- 10.4 Asia Pacific
- 10.4.1 China
- 10.4.2 Japan
- 10.4.3 India
- 10.4.4 Australia
- 10.4.5 South Korea
- 10.5 Latin America
- 10.5.1 Brazil
- 10.5.2 Mexico
- 10.5.3 Argentina
- 10.6 Middle East and Africa
- 10.6.1 South Africa
- 10.6.2 Saudi Arabia
- 10.6.3 UAE
Chapter 11 Company Profiles
- 11.1 9Bio Therapeutics
- 11.2 Aevai Health
- 11.3 Atomwise
- 11.4 Aureka Biotechnologies
- 11.5 AVAYL
- 11.6 BenevolentAI
- 11.7 chAIron
- 11.8 Cyclica
- 11.9 Deargen
- 11.10 Deep Genomics
- 11.11 DenovAI Biotech
- 11.12 Examol
- 11.13 Exscientia
- 11.14 Google (DeepMind)
- 11.15 Helical
- 11.16 IBM Corporation
- 11.17 Insilico Medicine
- 11.18 LinkGevity
- 11.19 Microsoft
- 11.20 NVIDIA Corporation
- 11.21 Orakl Oncology
