Global AI in Drug Discovery Market Forecast to 2031 | Market to Reach USD 17.56 Billion at 28.1% CAGR as Generative AI and Precision Medicine Transform Pharma R&D
Dublin, Oct. 12, 2026 (GLOBE NEWSWIRE) -- "AI in Drug Discovery Market: Global Forecast to 2031" has been added to ResearchAndMarkets.com's offering.
The global AI in drug discovery market is projected to grow from USD 5.09 billion in 2026 to USD 17.56 billion by 2031, registering a CAGR of 28.1% during the forecast period. Growth is supported by the increasing adoption of artificial intelligence to improve pharmaceutical R&D productivity, the expanding availability of multimodal biological datasets, and rising investment in precision medicine and computational drug discovery.
A 2025 review published in Drug Discovery Today highlighted the continued decline in pharmaceutical R&D productivity, which is prompting biopharmaceutical companies to adopt AI-driven platforms to improve research efficiency and accelerate drug discovery. Market expansion may be moderated by inconsistent data quality, model validation requirements, regulatory uncertainty, limited clinical validation, and challenges associated with integrating AI into established pharmaceutical R&D workflows.
Machine learning expected to record the fastest growth
By AI tool, machine learning is expected to be the fastest-growing segment between 2026 and 2031. Pharmaceutical and biotechnology companies are using predictive algorithms to support target identification, hit prioritization, molecular property prediction, and lead optimization. Advances in deep learning, graph neural networks, and generative AI are broadening machine learning applications across small molecule discovery, biologics development, and precision medicine.
In March 2026, NVIDIA expanded its BioNeMo platform with next-generation foundation models and agentic AI capabilities designed to advance biomolecular research and molecular design. Continued demand for shorter development timelines, lower discovery costs, and better R&D decision-making is expected to sustain adoption of machine learning across the AI in drug discovery market.
Oncology held the largest market share in 2025
Oncology accounted for the largest share of the market by therapeutic area in 2025, driven by the global cancer burden, extensive research pipelines, and strong demand for precision therapeutics. The International Agency for Research on Cancer projects that worldwide cancer incidence will increase from 20.6 million new cases in 2024 to 34.4 million by 2050.
AI is increasingly used in oncology to identify therapeutic targets, predict biomarkers, improve patient stratification, and accelerate targeted therapy and immuno-oncology research. Access to genomic, transcriptomic, proteomic, and clinical data also supports the development of clinically relevant AI models. Pharmaceutical companies continue to prioritize oncology because of its commercial potential and the growing need for personalized cancer treatments. Partnerships between AI developers and oncology-focused biopharmaceutical companies are further strengthening this segment.
Europe projected to register the second-highest CAGR
Europe is forecast to achieve the second-highest regional growth rate through 2031. Key factors include investment in pharmaceutical innovation, wider AI adoption in biomedical research, and regulatory initiatives supporting trustworthy AI. The region benefits from an established network of pharmaceutical companies, research institutions, and AI-native biotechnology firms working across target discovery, molecular design, and precision medicine.
The European Health Data Space and the EU AI Act are expected to support secure cross-border health data access and harmonized governance for life sciences applications. The proposed Cloud and AI Development Act also seeks to expand sovereign computing capacity and access to high-performance computing resources. Collaborative programs such as the Innovative Health Initiative are funding AI-enabled drug discovery projects, including LIGAND-AI, to advance therapeutic research using high-quality biological datasets and sophisticated AI models.
Primary research participant breakdown
Competitive landscape
Leading companies in the AI in drug discovery market include NVIDIA Corporation, Schrodinger, Inc., Recursion, Insilico Medicine, Google, Microsoft Corporation, Tempus AI, Inc., Illumina, Inc., XtalPi Inc., and Iktos. These market participants are pursuing strategic partnerships, product launches, platform enhancements, foundation model investments, generative AI development, mergers and acquisitions, and geographic expansion to strengthen their competitive positions.
Research coverage and market insights
The report evaluates the market by process, use case, therapeutic area, player type, AI tool, deployment model, end user, and region. It examines market drivers, restraints, opportunities, and challenges while assessing growth prospects across the AI-driven drug discovery ecosystem. Regional revenue forecasts, micro market trends, company profiles, product portfolios, recent developments, and competitive strategies are also included.
Key growth drivers include the need to reduce drug discovery time and cost and the expanding use of AI to predict drug-target interactions in cancer research. Opportunities include AI-enabled biotechnology research and single-cell analysis for biomarker and disease-subtype identification. Market constraints include shortages of skilled AI professionals, evolving regulatory frameworks, rising computing infrastructure costs amid global HBM/DRAM/NAND shortages, limited high-quality training data, and insufficient real-world clinical validation of AI-derived drug candidates.
The report can help established companies, new entrants, and smaller firms identify high-growth segments, evaluate product development opportunities, assess technology trends, plan market diversification, and benchmark competitive capabilities. It also provides insights into R&D activity, software launches, geographic expansion, strategic collaborations, market shares, company evaluation, and the strengths of leading AI in drug discovery companies.
Key Attributes:
Key Topics Covered:
1 INTRODUCTION
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.3 MARKET SCOPE
1.3.1 MARKET SEGMENTATION
1.3.2 REGIONAL SCOPE
1.3.3 INCLUSIONS & EXCLUSIONS
1.3.4 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 LIMITATIONS
1.6 STAKEHOLDERS
1.7 SUMMARY OF CHANGES
2 EXECUTIVE SUMMARY
2.1 KEY INSIGHTS AND MARKET HIGHLIGHTS
2.2 KEY MARKET PARTICIPANTS: SHARE INSIGHTS AND STRATEGIC DEVELOPMENTS
2.3 DISRUPTIVE TRENDS SHAPING THE MARKET
2.4 HIGH-GROWTH SEGMENTS & EMERGING FRONTIERS
2.5 SNAPSHOT: GLOBAL MARKET SIZE, GROWTH RATE, AND FORECAST
3 PREMIUM INSIGHTS
3.1 AI IN DRUG DISCOVERY MARKET OVERVIEW
3.2 AI IN DRUG DISCOVERY MARKET, BY DEPLOYMENT & COUNTRY
3.3 AI IN DRUG DISCOVERY MARKET: GEOGRAPHIC SNAPSHOT
4 MARKET OVERVIEW
4.1 INTRODUCTION
4.2 MARKET DYNAMICS
4.2.1 DRIVERS
4.2.1.1 Increasing number of cross-industry collaborations and partnerships
4.2.1.2 Rising need to reduce time and cost of drug discovery and development
4.2.1.3 Patent expiry of drugs and need for effective new leads
4.2.1.4 Growing utilization of AI to predict drug-target interactions for cancer therapy
4.2.1.5 Integration of AI-assisted multiomics in drug discovery
4.2.1.6 Growing focus on rare disease treatments for orphan drug development
4.2.2 RESTRAINTS
4.2.2.1 Shortage of AI workforce and ambiguous regulatory guidelines for medical software
4.2.3 OPPORTUNITIES
4.2.3.1 Leveraging AI for accelerated biotech drug discovery
4.2.3.2 Increased focus on drug discovery in emerging economies
4.2.3.3 Focus on developing human-aware AI systems
4.2.3.4 Growing use of AI in single-cell analysis
4.2.3.5 Easy identification of biomarker and disease subtypes from single-cell data
4.2.3.6 High demand for precision and personalized medicines
4.2.4 CHALLENGES
4.2.4.1 Limited availability of quality data sets
4.2.4.2 Lack of advanced AI tools and training data sets
4.2.4.3 Computational constraints of advanced AI models
4.2.4.4 Lack of high-quality data sets for model training
4.3 UNMET NEEDS AND WHITE SPACES
4.4 INTERCONNECTED MARKETS AND CROSS-SECTOR OPPORTUNITIES
4.5 STRATEGIC MOVES BY TIER-1/2/3 PLAYERS
5 INDUSTRY TRENDS
5.1 PORTER'S FIVE FORCES ANALYSIS
5.1.1 BARGAINING POWER OF SUPPLIERS
5.1.2 BARGAINING POWER OF BUYERS
5.1.3 THREAT OF SUBSTITUTES
5.1.4 THREAT OF NEW ENTRANTS
5.1.5 INTENSITY OF COMPETITIVE RIVALRY
5.2 MACROECONOMIC INDICATORS
5.2.1 INTRODUCTION
5.2.2 GDP TRENDS AND FORECAST
5.2.3 GLOBAL PHARMACEUTICAL R&D EXPENDITURE AND PRODUCTIVITY TRENDS
5.3 VALUE CHAIN ANALYSIS
5.3.1 RESEARCH, DATA GENERATION, AND SCIENTIFIC INPUTS
5.3.2 DATA ENGINEERING & AI INFRASTRUCTURE
5.3.3 AI MODEL & PLATFORM DEVELOPMENT
5.3.4 AI WORKFLOW INTEGRATION & DEPLOYMENT
5.3.5 DRUG DISCOVERY SERVICES & EXPERIMENTAL EXECUTION
5.3.6 VALIDATION, LAB-IN-THE-LOOP, AND CONTINUOUS OPTIMIZATION
5.4 ECOSYSTEM ANALYSIS
5.5 PRICING ANALYSIS
5.5.1 INDICATIVE PRICE FOR AI IN DRUG DISCOVERY PLATFORMS, BY KEY PLAYERS (2025)
5.5.2 INDICATIVE PRICE FOR AI IN DRUG DISCOVERY SOFTWARE AND SERVICES, BY REGION (2025)
5.6 KEY CONFERENCES AND EVENTS, 2026-2027
5.7 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
5.8 INVESTMENT AND FUNDING SCENARIO
5.9 CASE STUDY ANALYSIS
5.10 IMPACT OF 2025 US TARIFF - AI IN DRUG DISCOVERY MARKET
5.10.1 INTRODUCTION
5.10.2 KEY TARIFF RATES
5.10.3 PRICE IMPACT ANALYSIS
5.10.4 IMPACT ON COUNTRY/REGION
5.10.4.1 US
5.10.4.2 Europe
5.10.4.3 Asia-Pacific
5.10.5 IMPACT ON END-USE INDUSTRIES
5.10.5.1 Pharmaceutical companies
5.10.5.2 Biotechnology companies
5.10.5.3 CROs and CDMOs
5.10.5.4 Academic and research users
5.10.5.5 Other end users
6 STRATEGIC DISRUPTION THROUGH TECHNOLOGY, PATENTS, DIGITAL, AND AI ADOPTION
6.1 KEY EMERGING TECHNOLOGIES
6.1.1 GENERATIVE AND AGENTIC AI FOR DRUG DISCOVERY
6.1.2 MULTIMODAL AI AND FOUNDATION MODELS
6.1.3 AI-ENABLED AUTONOMOUS LABORATORIES
6.2 COMPLEMENTARY TECHNOLOGIES
6.2.1 CLOUD COMPUTING AND HIGH-PERFORMANCE COMPUTING
6.2.2 HIGH-THROUGHPUT SCREENING AND LABORATORY AUTOMATION
6.3 ADJACENT TECHNOLOGIES
6.3.1 MULTIOMICS AND SINGLE-CELL TECHNOLOGIES
6.3.2 DIGITAL TWINS AND IN-SILICO CLINICAL TECHNOLOGIES
6.4 TECHNOLOGY/PRODUCT ROADMAP
6.5 PATENT ANALYSIS
6.5.1 PATENT PUBLICATION TRENDS FOR AI IN DRUG DISCOVERY MARKET
6.5.2 INSIGHTS: JURISDICTION AND TOP APPLICANT ANALYSIS
6.6 FUTURE APPLICATIONS
6.6.1 AUTONOMOUS END-TO-END DRUG DISCOVERY
6.6.2 AI-DRIVEN PRECISION DRUG DISCOVERY AND PATIENT-SPECIFIC THERAPEUTICS
6.6.3 AI-ENABLED DIGITAL AND VIRTUAL DRUG DEVELOPMENT
7 REGULATORY LANDSCAPE
7.1 REGIONAL REGULATIONS AND COMPLIANCE
7.1.1 REGULATORY BODIES, GOVERNMENT AGENCIES, & OTHER ORGANIZATIONS
7.1.2 REGULATORY FRAMEWORK
7.1.2.1 North America
7.1.2.2 Europe
7.1.2.3 Asia Pacific
7.1.2.4 Latin America
7.1.2.5 Middle East & Africa
7.1.3 INDUSTRY STANDARDS
8 CUSTOMER LANDSCAPE & BUYER BEHAVIOR
8.1 INTRODUCTION
8.2 DECISION-MAKING PROCESS
8.3 BUYER STAKEHOLDERS AND BUYING EVALUATION CRITERIA
8.3.1 KEY STAKEHOLDERS IN BUYING PROCESS
8.3.2 BUYING CRITERIA
8.4 ADOPTION BARRIERS & INTERNAL CHALLENGES
8.5 UNMET NEEDS FROM VARIOUS END USERS
8.5.1 UNMET NEEDS
8.5.2 END USER EXPECTATIONS
8.6 MARKET PROFITABILITY
9 AI IN DRUG DISCOVERY MARKET, BY PROCESS
9.1 INTRODUCTION
9.2 TARGET IDENTIFICATION & SELECTION
9.2.1 INCREASED DEMAND FOR PERSONALIZED MEDICINES AND HIGH INVESTMENT IN PHARMACEUTICAL R&D TO FUEL MARKET GROWTH
9.3 TARGET VALIDATION
9.3.1 RISING EMPHASIS ON AVOIDING LATE-STAGE FAILURE IN DRUG DISCOVERY TO BOOST MARKET GROWTH
9.4 HIT IDENTIFICATION & PRIORITIZATION
9.4.1 NEED FOR LARGE-SCALE DATA ANALYSIS TO DRIVE ADOPTION
9.5 HIT-TO-LEAD IDENTIFICATION/LEAD GENERATION
9.5.1 HIT-TO-LEAD IDENTIFICATION/LEAD GENERATION TO IMPROVE NEW DRUG POTENCY WITHOUT INCREASING LIPOPHILICITY
9.6 LEAD OPTIMIZATION
9.6.1 NEED FOR TRANSPARENT PRESENTATION AND ANALYSIS TO BOOST MARKET GROWTH
9.7 CANDIDATE SELECTION & VALIDATION
9.7.1 HIGH POSSIBILITY OF CLINICAL DRUG FAILURE TO SPUR ADOPTION OF CANDIDATE VALIDATION SERVICES
10 AI IN DRUG DISCOVERY MARKET, BY AI TOOL
10.1 INTRODUCTION
10.2 MACHINE LEARNING
10.2.1 INCREASING AVAILABILITY OF COMPLEX BIOLOGICAL AND CHEMICAL DATASETS DRIVES ADOPTION
10.2.2 DEEP LEARNING
10.2.2.1 Ability to capture complex nonlinear relationships across multimodal biological and molecular data - key driver
10.2.2.2 Transformer-based architectures
10.2.2.2.1 Growing volumes of sequential, multimodal, and unstructured scientific data drive market growth
10.2.2.3 Graph neural networks (GNN)
10.2.2.3.1 Need to understand molecular structure and complex biological relationships supports GNN adoption
10.2.2.4 Convolutional neural networks (CNN)
10.2.2.4.1 Expansion of image-based and 3D structural analysis boosts adoption
10.2.2.5 Diffusion and flow-based models
10.2.2.5.1 Increasing demand for controlled generation of novel molecules and proteins accelerates segment growth
10.2.2.6 Generative adversarial networks (GAN) & variational autoencoders (VAE)
10.2.2.6.1 Growing demand for computational exploration of chemical space supports growth
10.2.2.7 Recurrent & Sequence Models (RNN, LSTM)
10.2.2.7.1 Continued need to model sequential chemical and biological information sustains demand
10.2.2.8 Other deep learning technologies
10.2.2.8.1 Emergence of specialized neural architectures expands applications across complex discovery workflows
10.2.3 SUPERVISED LEARNING
10.2.3.1 Growing availability of experimentally validated datasets supports segment
10.2.4 SELF-SUPERVISED & REPRESENTATION LEARNING
10.2.4.1 Shortage of labeled biological data and growing availability of unlabeled datasets drive adoption
10.2.5 REINFORCEMENT LEARNING
10.2.5.1 Need to optimize molecules and experimental decisions across multiple objectives drives adoption
10.2.6 UNSUPERVISED LEARNING
10.2.6.1 Increasing complexity of biological datasets to boost segment
10.2.7 OTHER MACHINE LEARNING TECHNOLOGIES
10.2.7.1 Need to extract value from partially labeled and heterogeneous datasets supports segment
10.3 GENERATIVE AI
10.3.1 ABILITY TO DESIGN NOVEL MOLECULES AND BIOLOGICAL STRUCTURES ACCELERATES ADOPTION
10.3.2 MOLECULAR GENERATIVE MODELS
10.3.2.1 Need to explore larger chemical spaces and simultaneously optimize multiple molecular properties - segment driver
10.3.3 PROTEIN & BIOLOGICS GENERATIVE MODELS
10.3.3.1 Growing demand for engineered proteins, antibodies, and other biologics expands applications
10.3.4 LARGE LANGUAGE MODELS (LLMS)
10.3.4.1 Rapid growth of scientific literature and unstructured research data increases demand
10.4 FOUNDATION MODELS
10.4.1 NEED FOR REUSABLE AI MODELS TO LEARN FROM LARGE-SCALE, MULTIMODAL BIOLOGICAL DATASETS DRIVES ADOPTION
10.4.2 CHEMISTRY FOUNDATION MODELS
10.4.2.1 Increasing need for reusable molecular representation and scalable chemical intelligence drives segment
10.4.3 PROTEIN LANGUAGE MODELS
10.4.3.1 Growing demand for protein engineering and functional prediction accelerates adoption
10.4.4 BIOLOGY AND MULTIOMICS FOUNDATION MODELS
10.4.4.1 Increasing integration of genomic, transcriptomic, proteomic, and cellular data - key driver
10.5 AGENTIC AI & AI CO-SCIENTISTS
10.5.1 GROWTH DRIVEN BY DEMAND FOR AUTONOMOUS RESEARCH WORKFLOWS AND IMPROVED SCIENTIST PRODUCTIVITY
10.5.2 AUTONOMOUS RESEARCH AGENTS & WORKFLOW ORCHESTRATION
10.5.2.1 Need to automate complex multi-step research processes drives adoption
10.5.3 MULTI-AGENT REASONING SYSTEMS
10.5.3.1 Increasing complexity of scientific problems creates demand
10.5.4 LAB-IN-THE-LOOP & CLOSED-LOOP EXPERIMENTATION
10.5.4.1 Integration of AI with automated laboratories enables continuous design-build-test-learn workflows
10.6 KNOWLEDGE GRAPHS AND SEMANTIC REASONING
10.6.1 NEED TO CONNECT FRAGMENTED SCIENTIFIC INFORMATION AND IDENTIFY HIDDEN RELATIONSHIPS DRIVES SEGMENT
10.7 NATURAL LANGUAGE PROCESSING (NLP)
10.7.1 EXPLOSION OF SCIENTIFIC LITERATURE, PATENTS, AND UNSTRUCTURED R&D INFORMATION DRIVES NLP ADOPTION
10.8 COMPUTER VISION & IMAGE ANALYSIS
10.8.1 INCREASING USE OF HIGH-CONTENT SCREENING, MICROSCOPY, AND PHENOTYPIC ASSAYS EXPANDS APPLICATIONS
10.9 PHYSICS-BASED & HYBRID AI METHODS
10.9.1 NEED FOR MECHANISTIC UNDERSTANDING AND IMPROVED ACCURACY IN MOLECULAR MODELING DRIVES SEGMENT
11 AI IN DRUG DISCOVERY MARKET, BY END USER
11.1 INTRODUCTION
11.2 PHARMACEUTICAL & BIOTECHNOLOGY COMPANIES
11.2.1 GROWING PRESSURE TO ACCELERATE R&D PRODUCTIVITY AND REDUCE DRUG DISCOVERY COSTS DRIVES SEGMENT
11.2.2 LARGE PHARMACEUTICAL COMPANIES
11.2.2.1 High R&D expenditure and increasing need to improve pipeline productivity accelerate AI adoption
11.2.3 SMALL & MID-SIZED BIOTECHNOLOGY COMPANIES
11.2.3.1 Limited internal resources and need for differentiated pipelines increase reliance on AI-enabled discovery
11.3 CONTRACT RESEARCH ORGANIZATIONS (CROS) & CDMOS
11.3.1 INCREASING OUTSOURCING OF DRUG DISCOVERY ACTIVITIES DRIVES GROWTH
11.4 RESEARCH CENTERS, ACADEMIC INSTITUTES, AND GOVERNMENT ORGANIZATIONS
11.4.1 GROWING AVAILABILITY OF RESEARCH FUNDING AND ADVANCED AI CAPABILITIES EXPANDS GROWTH
12 AI IN DRUG DISCOVERY MARKET, BY USE CASE
12.1 INTRODUCTION
12.2 UNDERSTANDING DISEASE BIOLOGY
12.2.1 INCREASED FOCUS ON UNDERSTANDING DISEASES TO IMPROVE RESEARCH DATA QUALITY AND QUANTITY
12.2.2 EVIDENCE SYNTHESIS AND SCIENTIFIC LITERATURE INTELLIGENCE
12.2.2.1 Rapid data analysis - key demand driver
12.2.3 MULTIOMICS INTEGRATION & TARGET-DISEASE ASSOCIATION
12.2.3.1 Growing integration of genomic, transcriptomic, proteomic, metabolomic, and other biological datasets to drive market
12.2.4 EXPERIMENT DESIGN & REAGENT/MODEL SELECTION
12.2.4.1 Growing complexity of disease biology and experiment design boosts demand
12.3 PROTEIN STRUCTURE & INTERACTION PREDICTION
12.3.1 ADVANCES IN AI-POWERED PROTEIN INTERACTION PREDICTION ACCELERATE STRUCTURE-BASED DRUG DISCOVERY
12.4 DRUG REPURPOSING
12.4.1 INCREASING ADOPTION OF AI PLATFORMS FOR SYSTEMATIC DRUG REPURPOSING
12.5 DE NOVO DRUG DESIGN
12.5.1 SMALL MOLECULE DESIGN
12.5.1.1 Increasing use of virtual screening and simulation techniques to drive growth
12.5.2 VACCINE DESIGN
12.5.2.1 Availability of well-validated AI tools to boost market growth
12.5.3 ANTIBODY & OTHER BIOLOGICS DESIGN
12.5.3.1 Advancements in protein modeling to propel segment growth
12.5.4 PROTEIN & ENZYME DESIGN
12.5.4.1 Protein designing to propel segment growth
12.6 DRUG OPTIMIZATION
12.6.1 SMALL MOLECULE OPTIMIZATION
12.6.1.1 Generative models for potential modifications in molecular structures to aid market growth
12.6.2 VACCINE OPTIMIZATION
12.6.2.1 Effective prediction of vaccine formulations and adjustment of delivery vectors to drive growth
12.6.3 ANTIBODY & OTHER BIOLOGICS OPTIMIZATION
12.6.3.1 Increasing adoption of machine learning to predict protein structures to augment segment growth
12.7 SAFETY & TOXICITY
12.7.1 FOCUS ON ADVANCED OFF-TARGET EFFECT PREDICTION, PK/PD SIMULATION, AND QSP MODELING TO DRIVE MARKET
13 AI IN DRUG DISCOVERY MARKET, BY DEPLOYMENT
13.1 INTRODUCTION
13.2 ON-PREMISE SOLUTIONS
13.2.1 GROWING DEMAND FOR DATA SOVEREIGNTY, IP PROTECTION, AND CONTROL OVER PROPRIETARY R&D DATA SUPPORTS GROWTH DEPLOYMENT
13.3 CLOUD-BASED SOLUTIONS
13.3.1 NEED FOR SCALABLE COMPUTING, RAPID AI DEPLOYMENT, AND COST-EFFICIENT ACCESS TO ADVANCED INFRASTRUCTURE - KEY DRIVERS
13.3.2 PUBLIC CLOUD
13.3.2.1 Elastic computing capacity and access to advanced AI infrastructure drive adoption
13.3.3 PRIVATE CLOUD/VPC/SOVEREIGN DEPLOYMENT
13.3.3.1 Increasing data-security, IP-protection, and data-residency requirements strengthen demand
13.4 HYBRID SOLUTIONS
13.4.1 NEED TO BALANCE COMPUTATIONAL SCALABILITY WITH CONTROL OVER SENSITIVE R&D DATA ACCELERATES ADOPTION
14 AI IN DRUG DISCOVERY MARKET, BY THERAPEUTIC AREA
14.1 INTRODUCTION
14.2 ONCOLOGY
14.2.1 HIGH PREVALENCE OF ONCOLOGY AND SHORTAGE OF EFFECTIVE ONCOLOGY DRUGS TO DRIVE MARKET GROWTH
14.3 INFECTIOUS DISEASES
14.3.1 RISING ANTIMICROBIAL RESISTANCE AND NEED FOR NOVEL ANTI-INFECTIVE THERAPIES TO DRIVE GROWTH
14.4 NEUROLOGY
14.4.1 HIGH DISEASE COMPLEXITY AND LIMITED TREATMENT OPTIONS DRIVE DEMAND FOR AI-ENABLED DISCOVERY
14.5 CARDIOVASCULAR DISEASES
14.5.1 GROWING CARDIOVASCULAR DISEASE BURDEN AND NEED FOR NOVEL THERAPEUTIC TARGETS BOOST GROWTH
14.6 METABOLIC DISEASES
14.6.1 RISING PREVALENCE OF OBESITY AND METABOLIC DISORDERS ACCELERATES DEMAND FOR INNOVATIVE THERAPIES
14.7 IMMUNOLOGY
14.7.1 COMPLEX IMMUNE MECHANISMS AND GROWING DEMAND FOR TARGETED THERAPIES DRIVE AI ADOPTION
14.8 RARE & GENETIC DISORDERS
14.8.1 LIMITED TREATMENT OPTIONS AND FRAGMENTED DISEASE DATA DRIVE AI-ENABLED DRUG DISCOVERY
14.9 MENTAL HEALTH DISORDERS
14.9.1 HIGH UNMET NEED AND LIMITED UNDERSTANDING OF DISEASE MECHANISMS DRIVE SEGMENT GROWTH
14.10 OTHER THERAPEUTIC AREAS
14.10.1 EXPANDING APPLICATION OF AI ACROSS UNDEREXPLORED DISEASE AREAS DRIVES MARKET GROWTH
15 AI IN DRUG DISCOVERY MARKET, BY PLAYER TYPE
15.1 INTRODUCTION
15.2 END-TO-END SOLUTION PROVIDERS
15.2.1 INCREASING ADOPTION OF INTEGRATED AI PLATFORMS ACROSS DRUG DISCOVERY WORKFLOW DRIVES GROWTH
15.3 NICHE/POINT SOLUTION PROVIDERS
15.3.1 GROWING DEMAND FOR SPECIALIZED AI CAPABILITIES TARGETING COMPLEX DISCOVERY CHALLENGES TO DRIVE GROWTH
15.4 AI TECHNOLOGY PROVIDERS
15.4.1 RAPID ADVANCES IN GENERATIVE AI, FOUNDATION MODELS, AND AGENTIC AI ACCELERATE DEMAND
15.5 COMPUTE & INFRASTRUCTURE PROVIDERS
15.5.1 GROWING COMPUTATIONAL REQUIREMENTS OF ADVANCED AI MODELS DRIVE DEMAND
15.6 BUSINESS PROCESS SERVICE PROVIDERS
15.6.1 INCREASING OUTSOURCING OF SPECIALIZED AI-ENABLED DISCOVERY ACTIVITIES DRIVES GROWTH
16 AI IN DRUG DISCOVERY MARKET, BY REGION
16.1 INTRODUCTION
16.2 NORTH AMERICA
16.2.1 MACROECONOMIC OUTLOOK FOR NORTH AMERICA
16.2.2 US
16.2.2.1 Strong pharma-AI ecosystem and high R&D investment drives demand
16.2.3 CANADA
16.2.3.1 Strong AI research base and growing biopharma innovation - key drivers
16.3 EUROPE
16.3.1 MACROECONOMIC OUTLOOK FOR EUROPE
16.3.2 GERMANY
16.3.2.1 Availability of advanced computing and research infrastructure - key market driver
16.3.3 UK
16.3.3.1 Increasing investment in AI-based molecular design drives demand
16.3.4 SWITZERLAND
16.3.4.1 Strong pharmaceutical and biotechnology ecosystem to boost market
16.3.5 FRANCE
16.3.5.1 Large-scale AI investment by pharmaceutical companies - key driver
16.3.6 ITALY
16.3.6.1 Market driven by country's strong pharmaceutical and life sciences ecosystem
16.3.7 SPAIN
16.3.7.1 Expansion of AI-enabled biotechnology research drives demand
16.3.8 REST OF EUROPE
16.3.8.1 Growing pharmaceutical R&D and public-sector AI adoption to drive market
16.4 ASIA PACIFIC
16.4.1 MACROECONOMIC OUTLOOK FOR ASIA PACIFIC
16.4.2 JAPAN
16.4.2.1 Increasing pharmaceutical investment in AI and robotics drives demand
16.4.3 CHINA
16.4.3.1 Growth of AI-native biotech companies boosts demand
16.4.4 INDIA
16.4.4.1 Large pharmaceutical and biotechnology ecosystem to propel market
16.4.5 SOUTH KOREA
16.4.5.1 Government support for AI-based drug discovery to boost market
16.4.6 AUSTRALIA
16.4.6.1 Market driven by strong government support for AI and medical research
16.4.7 REST OF ASIA PACIFIC
16.4.7.1 Growth of regional pharmaceutical and biotechnology capabilities to boost market
16.5 LATIN AMERICA
16.5.1 MACROECONOMIC OUTLOOK FOR LATIN AMERICA
16.5.2 BRAZIL
16.5.2.1 Expanding pharmaceutical R&D and emerging AI-biotech ecosystem act as major drivers
16.5.3 MEXICO
16.5.3.1 Multinational pharmaceutical activity and government-backed research to drive market
16.5.4 REST OF LATIN AMERICA
16.5.4.1 Growing research base for AI to drive market
16.6 MIDDLE EAST & AFRICA
16.6.1 MACROECONOMIC OUTLOOK FOR MIDDLE EAST & AFRICA
16.6.2 GCC COUNTRIES
16.6.2.1 National AI strategies and growing technology partnerships act as major drivers
16.6.3 SAUDI ARABIA
16.6.3.1 Government investment in biotechnology and life sciences propels market
16.6.4 UAE
16.6.4.1 Government-led AI and biotechnology investment boosts market
16.6.5 REST OF GCC
16.6.5.1 International AI-drug discovery partnerships drive demand
16.6.6 SOUTH AFRICA
16.6.6.1 Strong domestic drug-discovery research base drives market
16.6.7 REST OF MIDDLE EAST & AFRICA
16.6.7.1 Expansion of local pharmaceutical and biomanufacturing capabilities drives demand
17 COMPETITIVE LANDSCAPE
17.1 OVERVIEW
17.2 KEY PLAYER STRATEGIES/RIGHT TO WIN
17.2.1 OVERVIEW OF STRATEGIES ADOPTED BY KEY PLAYERS IN AI IN DRUG DISCOVERY MARKET
17.3 REVENUE SHARE ANALYSIS OF TOP MARKET PLAYERS
17.4 MARKET SHARE ANALYSIS, 2025
17.5 BRAND COMPARISON
17.6 VALUATION & FINANCIAL METRICS
17.6.1 FINANCIAL METRICS
17.6.2 COMPANY VALUATION
17.7 COMPANY EVALUATION MATRIX
17.7.1 STARS
17.7.2 EMERGING LEADERS
17.7.3 PERVASIVE PLAYERS
17.7.4 PARTICIPANTS
17.7.5 COMPANY FOOTPRINT: KEY PLAYERS, 2025
17.7.5.1 Company footprint
17.7.5.2 Region footprint
17.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2025
17.8.1 PROGRESSIVE COMPANIES
17.8.2 RESPONSIVE COMPANIES
17.8.3 DYNAMIC COMPANIES
17.8.4 STARTING BLOCKS
17.8.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2025
17.8.5.1 Detailed list of key startups/SMEs
17.8.5.2 Competitive benchmarking of startups/SMEs
17.9 COMPETITIVE SCENARIO
17.9.1 PRODUCT/SERVICE LAUNCHES
17.9.2 DEALS
17.9.3 EXPANSIONS
17.9.4 OTHER DEVELOPMENTS
18 COMPANY PROFILES
18.1 KEY PLAYERS
18.1.1 NVIDIA CORPORATION
18.1.2 ALPHABET INC.
18.1.3 RECURSION
18.1.4 INSILICO MEDICINE
18.1.5 SCHRODINGER, INC.
18.1.6 BENEVOLENTAI
18.1.7 MICROSOFT CORPORATION
18.1.8 NUMERION LABS
18.1.9 ILLUMINA, INC.
18.1.10 XTALPI INC.
18.1.11 IKTOS
18.1.12 TEMPUS AI, INC.
18.1.13 DEEP GENOMICS, INC.
18.1.14 VERGE LABS
18.1.15 BENCHSCI
18.1.16 INSITRO
18.1.17 VALO HEALTH
18.1.18 BPGBIO, INC.
18.1.19 GENERATE:BIOMEDICINES
18.1.20 CERTARA, INC.
18.1.21 KEY PLAYER PROFILE COVERAGE
18.1.21.1 Business overview
18.1.21.2 Products/Services offered
18.1.21.3 Recent developments
18.1.21.3.1 Product/Service launches & approvals
18.1.21.3.2 Deals
18.1.21.3.3 Expansions
18.1.21.3.4 Other developments
18.1.21.4 Analyst's view
18.1.21.4.1 Right to win
18.1.21.4.2 Strategic choices
18.1.21.4.3 Weaknesses & competitive threats
18.2 OTHER EMERGING PLAYERS
18.2.1 XAIRA THERAPEUTICS
18.2.2 IAMBIC THERAPEUTICS, INC.
18.2.3 GENESIS MOLECULAR AI
18.2.4 CRADLE BIO
18.2.5 ISOMORPHIC LABS
19 RESEARCH METHODOLOGY
19.1 RESEARCH APPROACH
19.1.1 SECONDARY RESEARCH
19.1.1.1 Key data from secondary sources
19.1.2 PRIMARY RESEARCH
19.1.2.1 Primary sources
19.1.2.2 Key data from primary sources
19.1.2.3 Breakdown of primaries
19.1.2.4 Insights from primary experts
19.2 RESEARCH METHODOLOGY DESIGN
19.3 MARKET SIZE ESTIMATION
19.4 MARKET BREAKDOWN DATA TRIANGULATION
19.5 MARKET SHARE ESTIMATION
19.6 STUDY ASSUMPTIONS
19.7 RESEARCH LIMITATIONS
19.7.1 METHODOLOGY-RELATED LIMITATIONS
19.8 RISK ASSESSMENT
20 APPENDIX
20.1 DISCUSSION GUIDE
20.2 KNOWLEDGESTORE: THE SUBSCRIPTION PORTAL
20.3 CUSTOMIZATION OPTIONS
20.4 RELATED REPORTS
20.5 AUTHOR DETAILS
LIST OF TABLES [716]
LIST OF FIGURES [50]
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