Drug Discovery용 AI 시장 예측(-2031년) : 프로세스별, AI 툴별, 최종사용자별, 사용 사례별, 배포별, 치료 분야별, 참여 조직 유형별, 지역별

The AI in drug discovery market is anticipated to grow from USD 5.09 billion in 2026 to USD 17.56 billion by 2031, at a CAGR of 28.1% during the forecast period. The market is driven by the increasing adoption of AI to improve R&D productivity, expanding use of multimodal biological datasets, and growing investments in precision medicine and computational drug discovery. According to a 2025 review published in Drug Discovery Today, declining pharmaceutical R&D productivity continues to reshape innovation strategies across the biopharmaceutical industry, prompting companies to increasingly adopt AI-driven platforms to improve research efficiency and accelerate drug discovery.

However, challenges related to data quality, model validation, regulatory uncertainty, and integration with existing pharmaceutical R&D workflows continue to influence the pace of market adoption.

Machine learning to be the fastest-growing AI tool segment between 2026 and 2031

By AI tool, the machine learning segment is expected to register the fastest growth during the forecast period as pharmaceutical companies increasingly leverage predictive algorithms to improve decision-making across the drug discovery workflow. Machine learning enables rapid analysis of complex biological, chemical, and clinical datasets, significantly enhancing target identification, hit prioritization, molecular property prediction, and lead optimization. Continuous advancements in deep learning, graph neural networks, and generative AI have further expanded the application of machine learning across small molecule discovery, biologics development, and precision medicine research. Reflecting this trend, in March 2026, NVIDIA expanded its BioNeMo platform with next-generation foundation models and agentic AI capabilities to accelerate biomolecular research and molecular design. As pharmaceutical companies continue to prioritize faster drug development and improved R&D efficiency, machine learning is expected to remain the fastest-growing technology segment in the AI in drug discovery market.

Oncology segment accounted for the largest share of the AI in drug discovery market in 2025

By therapeutic area, the oncology segment accounted for the largest share of the AI in drug discovery market in 2025 due to the high global cancer burden, extensive oncology research pipelines, and increasing demand for precision therapeutics. According to the International Agency for Research on Cancer (IARC), global cancer incidence is projected to increase from 20.6 million new cases in 2024 to 34.4 million by 2050, highlighting the growing need for innovative technologies that can accelerate oncology drug discovery. AI is widely adopted to identify novel therapeutic targets, predict biomarkers, optimize patient stratification, and accelerate the discovery of targeted therapies and immuno-oncology drugs. The availability of large genomic, transcriptomic, proteomic, and clinical datasets has made oncology one of the most data-rich therapeutic areas, enabling AI models to generate more accurate and clinically relevant insights. Pharmaceutical companies continue to prioritize oncology within their R&D portfolios due to its significant commercial potential and the growing demand for personalized cancer therapies. Increasing collaborations between AI companies and oncology-focused biopharmaceutical organizations are further accelerating innovation and reinforcing oncology's leading position in the AI in drug discovery market.

Europe to exhibit the second-highest CAGR during the forecast period

Europe is expected to register the second-highest growth rate during the forecast period, driven by increasing investments in pharmaceutical innovation, expanding adoption of AI across biomedical research, and a strong regulatory framework supporting trustworthy AI. The region is home to leading pharmaceutical companies, research institutions, and AI-native biotechnology firms that are accelerating the integration of AI into target discovery, molecular design, and precision medicine. The implementation of the European Health Data Space (EHDS) and the EU AI Act is expected to facilitate secure cross-border access to health data while establishing harmonized governance for AI applications in life sciences. In parallel, the proposed Cloud and AI Development Act (CADA) aims to strengthen Europe's cloud and AI infrastructure by expanding sovereign computing capacity, improving access to high-performance computing resources, and supporting the development of AI innovation across strategic sectors, including healthcare and life sciences. In addition, collaborative programs such as the Innovative Health Initiative (IHI) continue to fund AI-enabled drug discovery projects, including LIGAND-AI, to accelerate therapeutic research through high-quality biological datasets and advanced AI models. Collectively, these initiatives are expected to reinforce its position as the second-fastest-growing regional market for AI in drug discovery.

The breakdown of primary participants is as mentioned below:

• By Company Type - Tier 1: 32%, Tier 2: 44%, and Tier 3: 24%
• By Designation - Directors: 30%, Managers: 34%, and Others: 36%
• By Region - North America: 40%, Europe: 28%, Asia Pacific: 20%, Latin America: 7%, and Middle East & Africa: 5%

Key Players

The key players operating in the AI in drug discovery market include NVIDIA Corporation (US), Schrodinger, Inc. (US), Recursion (US), Insilico Medicine (US), Google (US), Microsoft Corporation (US), Tempus AI, Inc. (US), Illumina, Inc. (US), XtalPi Inc. (China), and Iktos (France). These companies have adopted strategies such as strategic partnerships, collaborations, product launches, platform enhancements, investments in foundation models and generative AI, mergers and acquisitions, and geographic expansion to strengthen their market presence in the market.

Research Coverage

The report analyzes the AI in drug discovery market. It aims to estimate the market size and future growth potential of various market segments based on process, use case, therapeutic area, player type, AI tool, deployment model, end user, and region. The report also analyzes factors such as drivers, restraints, opportunities, and challenges influencing market growth. It evaluates opportunities across the AI-driven drug discovery ecosystem and assesses the competitive landscape for key stakeholders. The report further analyzes micro markets with respect to their growth trends, prospects, and contributions to the overall AI in drug discovery market. It forecasts market revenue across major regions and provides a comprehensive competitive analysis of leading market participants, including their company profiles, product portfolios, recent developments, and key growth strategies.

Reasons to Buy the Report

This report will enrich established firms as well as new entrants/smaller firms to gauge the pulse of the market, which, in turn, would help them garner a greater share of the market. Firms purchasing the report could use one or a combination of the following strategies to strengthen their positions in the market.

This report provides insights on:

• Analysis of key drivers (Rising need to reduce time and cost of drug discovery and development, Growing utilization of AI to predict drug-target interactions for cancer therapy), restraints (Shortage of AI workforce and regulatory frameworks for AI-enabled drug discovery, Increasing AI compute infrastructure costs amid global HBM/DRAM/NAND shortage), opportunities (Accelerating biotech drug discovery, AI-driven single-cell analysis for biomarker and disease-subtype identification), and challenges (Limited availability of high-quality training datasets, Limited real-world clinical validation of AI-derived drug candidates) are factors contributing the growth of the AI in drug discovery market
• Product Development/Innovation: Detailed insights on upcoming trends, research & development activities, and software launches in the AI in drug discovery market
• Market Development: Comprehensive information on high-growth market segments across process, use case, therapeutic area, player type, AI tool, deployment model, end user, and region
• Market Diversification: Exhaustive information on product portfolios, technology advancements, expanding geographic presence, strategic collaborations, investments, and recent developments in the AI in drug discovery market
• Competitive Assessment: In-depth assessment of market shares, growth strategies, product offerings, company evaluation quadrant, technology capabilities, and competitive strengths of leading players in the global AI in drug discovery market, including NVIDIA Corporation (US), Schrodinger, Inc. (US), Recursion (US), Insilico Medicine (US), Google (US), Microsoft Corporation (US), Tempus AI, Inc. (US), Illumina, Inc. (US), XtalPi Inc. (China), and other major market participants

TABLE OF CONTENTS

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 drives market
    • 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
    • 18.1.1.1 Business overview
    • 18.1.1.2 Products/Services offered
    • 18.1.1.3 Recent developments
      • 18.1.1.3.1 Product/Service launches & approvals
      • 18.1.1.3.2 Deals
      • 18.1.1.3.3 Other developments
    • 18.1.1.4 MnM view
      • 18.1.1.4.1 Right to win
      • 18.1.1.4.2 Strategic choices
      • 18.1.1.4.3 Weaknesses & competitive threats
  • 18.1.2 ALPHABET INC.
    • 18.1.2.1 Business overview
    • 18.1.2.2 Products/Services offered
    • 18.1.2.3 Recent developments
      • 18.1.2.3.1 Product launches & approvals
      • 18.1.2.3.2 Deals
      • 18.1.2.3.3 Other developments
    • 18.1.2.4 MnM view
      • 18.1.2.4.1 Right to win
      • 18.1.2.4.2 Strategic choices
      • 18.1.2.4.3 Weaknesses & competitive threats
  • 18.1.3 RECURSION
    • 18.1.3.1 Business overview
    • 18.1.3.2 Products/Services offered
    • 18.1.3.3 Recent developments
      • 18.1.3.3.1 Product/Service launches
      • 18.1.3.3.2 Deals
      • 18.1.3.3.3 Expansions
    • 18.1.3.4 MnM view
      • 18.1.3.4.1 Right to win
      • 18.1.3.4.2 Strategic choices
      • 18.1.3.4.3 Weaknesses & competitive threats
  • 18.1.4 INSILICO MEDICINE
    • 18.1.4.1 Business overview
    • 18.1.4.2 Products/Services offered
    • 18.1.4.3 Recent developments
      • 18.1.4.3.1 Product/Service launches
      • 18.1.4.3.2 Deals
      • 18.1.4.3.3 Other developments
      • 18.1.4.3.4 expansions
    • 18.1.4.4 MnM view
      • 18.1.4.4.1 Right to win
      • 18.1.4.4.2 Strategic choices
      • 18.1.4.4.3 Weaknesses & competitive threats
  • 18.1.5 SCHRODINGER, INC.
    • 18.1.5.1 Business overview
    • 18.1.5.2 Products/Services offered
    • 18.1.5.3 Recent developments
      • 18.1.5.3.1 Deals
      • 18.1.5.3.2 Other developments
    • 18.1.5.4 MnM view
      • 18.1.5.4.1 Right to win
      • 18.1.5.4.2 Strategic choices
      • 18.1.5.4.3 Weaknesses & competitive threats
  • 18.1.6 BENEVOLENTAI
    • 18.1.6.1 Business overview
    • 18.1.6.2 Products/Services offered
    • 18.1.6.3 Recent developments
      • 18.1.6.3.1 Deals
  • 18.1.7 MICROSOFT CORPORATION
    • 18.1.7.1 Business overview
    • 18.1.7.2 Products /Services offered
    • 18.1.7.3 Recent developments
      • 18.1.7.3.1 Product/Service launches & approvals
      • 18.1.7.3.2 Deals
  • 18.1.8 NUMERION LABS
    • 18.1.8.1 Business overview
    • 18.1.8.2 Products/Services offered
  • 18.1.9 ILLUMINA, INC.
    • 18.1.9.1 Business overview
    • 18.1.9.2 Products/Services offered
    • 18.1.9.3 Recent developments
      • 18.1.9.3.1 Product/Service launches
      • 18.1.9.3.2 Deals
  • 18.1.10 XTALPI INC.
    • 18.1.10.1 Business overview
    • 18.1.10.2 Products/Services offered
    • 18.1.10.3 Recent developments
      • 18.1.10.3.1 Deals
  • 18.1.11 IKTOS
    • 18.1.11.1 Business overview
    • 18.1.11.2 Products/Services offered
    • 18.1.11.3 Recent developments
      • 18.1.11.3.1 Deals
      • 18.1.11.3.2 Other developments
  • 18.1.12 TEMPUS AI, INC.
    • 18.1.12.1 Business overview
    • 18.1.12.2 Products/Services offered
    • 18.1.12.3 Recent developments
      • 18.1.12.3.1 Product/Service launches
      • 18.1.12.3.2 Deals
  • 18.1.13 DEEP GENOMICS, INC.
    • 18.1.13.1 Business overview
    • 18.1.13.2 Products/Services offered
    • 18.1.13.3 Recent developments
      • 18.1.13.3.1 Product/Service launches & approvals
  • 18.1.14 VERGE LABS
    • 18.1.14.1 Business overview
    • 18.1.14.2 Products /Services offered
    • 18.1.14.3 Recent developments
      • 18.1.14.3.1 Deals
  • 18.1.15 BENCHSCI
    • 18.1.15.1 Business overview
    • 18.1.15.2 Products/Services offered
    • 18.1.15.3 Recent developments
      • 18.1.15.3.1 Product/Service launches & approvals
      • 18.1.15.3.2 Deals
      • 18.1.15.3.3 Other developments
  • 18.1.16 INSITRO
    • 18.1.16.1 Business overview
    • 18.1.16.2 Products/Services offered
    • 18.1.16.3 Recent developments
      • 18.1.16.3.1 Deals
      • 18.1.16.3.2 Other developments
  • 18.1.17 VALO HEALTH
    • 18.1.17.1 Business overview
    • 18.1.17.2 Products/Services offered
    • 18.1.17.3 Recent developments
      • 18.1.17.3.1 Deals
      • 18.1.17.3.2 Other developments
  • 18.1.18 BPGBIO, INC.
    • 18.1.18.1 Business overview
    • 18.1.18.2 Products/Services offered
    • 18.1.18.3 Recent developments
      • 18.1.18.3.1 Deals
  • 18.1.19 GENERATE:BIOMEDICINES
    • 18.1.19.1 Business overview
    • 18.1.19.2 Products/Services offered
    • 18.1.19.3 Recent developments
      • 18.1.19.3.1 Product/Service launches
      • 18.1.19.3.2 Deals
      • 18.1.19.3.3 Expansions
  • 18.1.20 CERTARA, INC.
    • 18.1.20.1 Business overview
    • 18.1.20.2 Products/Services offered
    • 18.1.20.3 Recent developments
      • 18.1.20.3.1 Product/Service launches & approvals
      • 18.1.20.3.2 Deals
• 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: MARKETSANDMARKETS' SUBSCRIPTION PORTAL
• 20.3 CUSTOMIZATION OPTIONS
• 20.4 RELATED REPORTS
• 20.5 AUTHOR DETAILS