Summary

The global artificial intelligence (AI) in drug discovery market is projected to reach 6.89 billion by 2029 from 1.86 billion in 2024, at a CAGR of 29.9% from 2024 to 2029. Increasing cross-industry collaborations and partnerships drive the growth of the artificial intelligence (AI) in drug discovery market by combining expertise, resources, and technology from various aspects of the drug discovery supply chain. For instance, in March 2024, Cognizant collaborated with NVIDIA to use generative AI through the BioNeMo platform, with the goal of transforming drug discovery and accelerating the development of life-saving therapies. Similarly, in August 2024, Exscientia Recursion and Exscientia plc announced a agreement, combining their technologies to enhance drug discovery. The integrated Recursion OS will enhance drug discovery through patient-centric target discovery, AI-driven design, quantum mechanics modeling, automated chemical synthesis, and other features. The combined company plans to complete 10 clinical trials within 18 months. Exscientia shareholders will receive Recursion stock, with Recursion shareholders owning 74% of the combined company. The deal is worth USD 850M in cash and is expected to close by early 2025.
“Oncology held the largest market share in the artificial intelligence (AI) in drug discovery market, by therapeutic area in 2023.”
Based on therapeutic areas, the artificial intelligence (AI) in drug discovery market is segmented into oncology, infectious diseases, neurology, metabolic diseases, cardiovascular diseases, immunology, mental health, and others (respiratory diseases, nephrology, dermatological diseases, genetic disorders, inflammatory diseases, and gastrointestinal). The oncology segment held the largest market share in the artificial intelligence (AI) in drug discovery market due to high prevalence of cancer and the complex nature of tumor biology, which necessitates innovative approaches for drug development. There were approximately 20 million new cancer cases and 9.7 million cancer-related deaths worldwide in 2022. Similarly, in 2024, 2.0 million new cancer cases and 611,720 cancer deaths are projected to occur in the US. The growing availability of biomedical data from cancer research, patient records, genomic studies, multi-omics datasets (genomics, proteomics, transcriptomics), and clinical trials provides an opportunity to leverage AI for pattern recognition and predicting drug interactions. The high demand for personalized medicine and targeted therapies in oncology, large commercial returns, emerging focus on immuno-oncology (especially checkpoint inhibitors and T-cell therapies), and exhaustive data availability drive investment in Al-driven solutions, elevating it to the forefront of the drug discovery landscape.

“Understanding disease use case to witness the fastest growth during the forecast period.”
Based on the use case, artificial intelligence (AI) in drug discovery market is segmented into understanding the disease, drug repurposing, de novo drug design, drug optimization, and safety & toxicity. The understanding disease is poised to be the fastest-growing use case over the forecast period. AI’s capacity to assess complex biological data and identify disease mechanisms is critical in early-stage drug development. AI helps researchers better understand disease pathways, genetic factors, and biomarkers, all of which are necessary for developing targeted therapies. Understanding diseases is required to identify potential drug targets, which enhances the efficiency of subsequent stages such as drug design and testing. The growth use of AI for phenotypic screening, image analysis, detecting anomalies in genetic perturbations on cellular or tissue morphology, biomarker identification, (-omics) data mining is expected to fuel the market growth.
“North America to dominate the market over the forecast period.”
Based on the region, the artificial intelligence (AI) in drug discovery market is segmented into five major regional segments: North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. The North American region dominated the artificial intelligence (AI) in drug discovery market in 2023. Several factors contribute to this dominance, including significant investment in healthcare technology, strong cross-sector collaborations, the presence of large pharmaceutical and biotechnology companies, and a favorable regulatory environment. The total investments in AI in Drug Development companies are USD 60.2 billion as of March 2023. A large wave of proof-of-concept studies and substantial advances in democratizing AI technology are also propelling the growth of the market. For example, in January 2023, AbSci created and validated de novo antibodies in silico with generative Al. Furthermore, in February 2023, the FDA granted an Orphan Drug Designation to a drug discovered and designed with Al. Insilico Medicine and began a global Phase I trial for the drug.

In-depth interviews have been conducted with chief executive officers (CEOs), Directors, and other executives from various key organizations operating in the authentication and brand protection marketplace.
Breakdown of supply-side primary interviews by company type, designation, and region:
• By Company Type: Tier 1 (31%), Tier 2 (28%), and Tier 3 (41%)
• By Designation – Demand Side: Purchase Managers (45%), Heads of Artificial Intelligence, Machine Learning, Drug Discovery, and Computational Molecular Design (30%), and Research Scientists (25%)
• By Designation – Supply Side: C-level Excecutives & Director level (35%), Managers (40%), and Others (25%)
• By Region: North America (45%), Europe (30%), Asia Pacific (20%), and Rest of the world (5%)
List of Companies Profiled in the Report
o NVIDIA Corporation (US)
o Exscientia (UK)
o Google (US)
o BenevolentAI (UK)
o Recursion (US)
o Insilico Medicine (US)
o Schrödinger, Inc. (US)
o Microsoft (US)
o Atomwise Inc. (US)
o Illumina, Inc. (US)
o Numedii, Inc. (US)
o Xtalpi Inc. (US)
o Iktos (France)
o Tempus (US)
o DEEP GENOMICS (Canada)
o Verge Genomics (US)
o BenchSci (Canada)
o Insitro (US)
o Valo Health (US)
o BPGBio, Inc. (US)
o Merck KGaA (Germany)
o IQVIA (US)
o Tencent Holdings Limited (China)
o Predictive Oncology, Inc. (US)
o CytoReason (Israel)
o Owkin, Inc. (US)
o Cloud Pharmaceuticals (US)
o Evaxion Biotech (Denmark)
o Standigm (South Korea)
o BIOAGE (US)
o Envisagenics (US)
o Abcellera (US)
o Centella (India)
The study includes an in-depth competitive analysis of these key players in the artificial intelligence (AI) in drug discovery market, with their company profiles, recent developments, and key market strategies.

Research Coverage
This research report categorizes the artificial intelligence (AI) in drug discovery market by process (target identification & selection, target validation, hit identification & prioritization, hit-to-lead identification/lead generation, lead optimization, and candidate selection & validation), by use case (understanding disease, drug repurposing, de novo drug design [small molecule design, vaccines design, antibody & other biologics design], drug optimization [small molecule optimization, vaccines optimization, antibody & other biologics optimization], and safety and toxicity), by therapeutic area (oncology, infectious diseases, neurology, metabolic diseases, cardiovascular diseases, immunology, mental health, others), by player type (end-to-end solution providers, niche/point solutions providers, AI technology providers, business process service providers), by tools (machine learning, natural language processing, context-aware process and computing, computer vision, image analysis (including optical character recognition)), by deployment (on-premise, cloud-based, SaaS-based), by end user (pharmaceutical & biotechnology companies, contract research organizations (CROs), and research centers, academic institutes, & government organizations) and by region (North America, Europe, Asia Pacific, Latin America, and Middle East & Africa). The scope of the report covers detailed information regarding the major factors, such as drivers, restraints, challenges, and opportunities, influencing the growth of the artificial intelligence (AI) in drug discovery market. A detailed analysis of the key industry players has been done to provide insights into their business overview, solutions, and services, key strategies such as product launches and enhancements, investments, partnerships, collaborations, agreements, joint ventures, funding, acquisitions, expansions, conferences, FDA clearances, sales contracts, alliances, and other recent developments associated with the artificial intelligence (AI) in drug discovery market. Competitive analysis of upcoming startups in the artificial intelligence (AI) in drug discovery market ecosystem is covered in this report.
Reasons to buy this report
The report will help the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the artificial intelligence (AI) in drug discovery market and the subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and to plan suitable go-to-market strategies. The report also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
• Analysis of key drivers (growing cross-industry collaborations and partnerships, growing need to reduce time and cost of drug discovery and development, patent expiry of several drugs, AI application in oncology areas, integration of multi-omics data, initiatives for research on rare diseases and orphan drugs), restraints (shortage of AI workforce and ambiguous regulatory guidelines for medical software, interpretability of AI), opportunities (growing biotechnology industry, increasing focus on emerging markets, focus on developing human-aware AI systems, increasing use of AI in single cell analysis, rapid expansion of biomarker, disease types, and subtypes identification, growing demand for precision and personalized medicine), and challenges (limited availability of data sets, lack of required tools and usability, computational limitations of advanced AI models, challenges regarding the accessibility of high-quality data) influencing the growth of the artificial intelligence (AI) in drug discovery market
• Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the artificial intelligence (AI) in drug discovery market
• Market Development: Comprehensive information about lucrative markets – the report analyses the artificial intelligence (AI) in drug discovery market across varied regions.
• Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the artificial intelligence (AI) in drug discovery market
• Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading players such as NVIDIA Corporation (US), Exscientia (UK), Google (US), BenevolentAI (UK), Recursion (US), Insilico Medicine (US), Schrödinger, Inc. (US), Microsoft (US), Atomwise Inc. (US), Illumina, Inc. (US), Numedii, Inc. (US), Xtalpi Inc. (US), Iktos (France), Valo Health (US), and Merck KGaA (Germany), among others in artificial intelligence (AI) in drug discovery market.

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Table of Contents

1 INTRODUCTION 39
1.1 STUDY OBJECTIVES 39
1.2 MARKET DEFINITION 39
1.3 STUDY SCOPE 40
1.3.1 SEGMENTS AND REGIONS CONSIDERED 40
1.3.2 INCLUSIONS AND EXCLUSIONS 41
1.3.3 YEARS CONSIDERED 42
1.3.4 CURRENCY CONSIDERED 42
1.4 MARKET STAKEHOLDERS 43
1.5 SUMMARY OF CHANGES 44
2 RESEARCH METHODOLOGY 45
2.1 RESEARCH DATA 45
2.1.1 SECONDARY DATA 46
2.1.1.1 Key secondary sources 46
2.1.1.2 Key data from secondary sources 47
2.1.2 PRIMARY DATA 47
2.1.2.1 Key primary sources 48
2.1.2.2 Key objectives of primary research 48
2.1.2.3 Key data from primary sources 49
2.1.2.4 Key industry insights 50
2.1.2.5 Breakdown of primaries 50
2.2 RESEARCH DESIGN 51
2.3 MARKET SIZE ESTIMATION 51
2.3.1 SUPPLY-SIDE ANALYSIS (REVENUE SHARE ANALYSIS) 52
2.3.2 BOTTOM-UP APPROACH: END-USER ADOPTION 54
2.3.2.1 Top-down assessment of parent market 55
2.3.2.2 Company presentations and primary interviews 55
2.4 DATA TRIANGULATION 59
2.5 STUDY ASSUMPTIONS 60
2.5.1 MARKET SIZING ASSUMPTIONS 60
2.5.2 RESEARCH ASSUMPTIONS 60
2.6 RISK ASSESSMENT 61
2.7 RESEARCH LIMITATIONS 61
2.7.1 METHODOLOGY-RELATED LIMITATIONS 61
2.7.2 SCOPE-RELATED LIMITATIONS 61
 
3 EXECUTIVE SUMMARY 62
4 PREMIUM INSIGHTS 68
4.1 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET OVERVIEW 68
4.2 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET,
BY END USER AND COUNTRY (2023) 69
4.3 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET: GEOGRAPHIC GROWTH OPPORTUNITIES 70
4.4 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET: REGIONAL MIX 71
4.5 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET: DEVELOPED VS.
EMERGING MARKETS 71
5 MARKET OVERVIEW 72
5.1 INTRODUCTION 72
5.2 MARKET DYNAMICS 72
5.2.1 DRIVERS 74
5.2.1.1 Increasing number of cross-industry collaborations and partnerships 74
5.2.1.2 Rising need to reduce time and cost of drug discovery and development 76
5.2.1.3 Patent expiry of drugs and need for effective new leads 76
5.2.1.4 Growing utilization of AI to predict drug-target interactions for cancer therapy 77
5.2.1.5 Integration of AI-assisted multiomics in drug discovery 78
5.2.1.6 Growing focus on rare disease treatments for orphan drug development 79
5.2.2 RESTRAINTS 80
5.2.2.1 Shortage of AI workforce and ambiguous regulatory guidelines for medical software 80
5.2.3 OPPORTUNITIES 81
5.2.3.1 Leveraging AI for accelerated biotech drug discovery 81
5.2.3.2 Increased focus on drug discovery in emerging economies 81
5.2.3.3 Focus on developing human-aware AI systems 82
5.2.3.4 Growing use of AI in single-cell analysis 82
5.2.3.5 Easy identification of biomarker and disease subtypes from single-cell data 83
5.2.3.6 High demand for precision and personalized medicines 84
5.2.4 CHALLENGES 85
5.2.4.1 Limited availability of quality data sets 85
5.2.4.2 Lack of advanced AI tools and training data sets 85
5.2.4.3 Computational constraints of advanced AI models 86
5.2.4.4 Lack of high-quality data sets for model training 86
5.3 TRENDS/DISRUPTIONS IMPACTING CUSTOMER’S BUSINESS 87
 
5.4 INDUSTRY TRENDS 87
5.4.1 EVOLUTION OF AI IN DRUG DISCOVERY 87
5.4.2 COMPUTER-AIDED DRUG DESIGN AND ARTIFICIAL INTELLIGENCE 89
5.5 ECOSYSTEM ANALYSIS 91
5.6 SUPPLY CHAIN ANALYSIS 93
5.7 TECHNOLOGY ANALYSIS 94
5.7.1 KEY TECHNOLOGIES 94
5.7.1.1 Dry lab services 94
5.7.1.2 Wet lab services 97
5.7.1.2.1 Chemistry software and services 97
5.7.1.2.2 Biology software and services 98
5.7.1.2.2.1 Single-cell analysis 99
5.7.2 COMPLEMENTARY TECHNOLOGIES 102
5.7.2.1 High-performance computing 102
5.7.2.2 Next-generation sequencing 102
5.7.2.3 Real-world evidence/Real-world data 102
5.7.3 ADJACENT TECHNOLOGIES 103
5.7.3.1 Cloud computing 103
5.7.3.2 Blockchain technologies 103
5.7.3.3 Internet of things 103
5.8 REGULATORY LANDSCAPE 104
5.8.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 104
5.8.2 REGULATORY FRAMEWORK 107
5.9 PRICING ANALYSIS 111
5.9.1 INDICATIVE SELLING PRICE FOR DRUG DISCOVERY SOFTWARE AND SERVICES, BY REGION 111
5.9.2 INDICATIVE PRICING ANALYSIS, BY PROCESS 112
5.10 PORTER’S FIVE FORCES ANALYSIS 112
5.10.1 INTENSITY OF COMPETITIVE RIVALRY 114
5.10.2 BARGAINING POWER OF BUYERS 114
5.10.3 THREAT OF SUBSTITUTES 114
5.10.4 THREAT OF NEW ENTRANTS 114
5.10.5 BARGAINING POWER OF SUPPLIERS 115
5.11 KEY STAKEHOLDERS AND BUYING CRITERIA 115
5.11.1 KEY STAKEHOLDERS IN BUYING PROCESS 115
5.11.2 KEY BUYING CRITERIA 116
5.12 PATENT ANALYSIS 117
5.12.1 PATENT PUBLICATION TRENDS 117
5.12.2 JURISDICTION ANALYSIS: TOP APPLICANT COUNTRIES FOR ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY 117
5.12.3 MAJOR PATENTS IN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET 119
 
5.13 UNMET NEEDS AND KEY PAIN POINTS 121
5.13.1 UNMET NEEDS 121
5.13.2 SINGLE-CELL ANALYSIS LANDSCAPE: KEY CHALLENGES AND PAIN POINTS IN DRUG DISCOVERY 122
5.13.3 END-USER EXPECTATIONS 123
5.14 KEY CONFERENCES & EVENTS, 2024–2025 124
5.15 CASE STUDY ANALYSIS 125
5.16 BUSINESS MODEL ANALYSIS 130
5.17 INVESTMENT AND FUNDING SCENARIO 132
5.18 IMPACT OF AI/GENERATIVE AI ON ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET 133
5.18.1 TOP USE CASES AND MARKET POTENTIAL 133
5.18.1.1 Key use cases 134
5.18.2 CASE STUDIES OF AI/GENERATIVE AI IMPLEMENTATION 135
5.18.2.1 Case study 1: Accelerated drug discovery with generative AI and streamlined workflows 135
5.18.2.2 Case study 2: Accelerating small-molecule drug discovery with generative AI 135
5.18.3 IMPACT OF AI/GENERATIVE AI ON INTERCONNECTED AND ADJACENT ECOSYSTEMS 136
5.18.3.1 AI in drug discovery market 136
5.18.3.2 Genomics and bioinformatics market 137
5.18.3.3 Life science analytics market 137
5.18.4 USER READINESS AND IMPACT ASSESSMENT 138
5.18.4.1 User readiness 138
5.18.4.1.1 Pharmaceutical companies 138
5.18.4.1.2 Biotechnology companies 138
5.18.4.2 Impact assessment 138
5.18.4.2.1 User A: Pharmaceutical Companies 138
5.18.4.2.1.1 Implementation 138
5.18.4.2.1.2 Impact 139
5.18.4.2.2 User B: Biotechnology companies 139
5.18.4.2.2.1 Implementation 139
5.18.4.2.2.2 Impact 139
5.19 ARTIFICIAL INTELLIGENCE-DERIVED CLINICAL ASSETS 140
6 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY PROCESS 150
6.1 INTRODUCTION 151
6.2 TARGET IDENTIFICATION & SELECTION 152
6.2.1 INCREASED DEMAND FOR PERSONALIZED MEDICINES AND HIGH INVESTMENT IN PHARMACEUTICAL R&D TO FUEL MARKET GROWTH 152
6.3 TARGET VALIDATION 153
6.3.1 RISING EMPHASIS ON AVOIDING LATE-STAGE FAILURE IN DRUG DISCOVERY TO BOOST MARKET GROWTH 153
6.4 HIT IDENTIFICATION & PRIORITIZATION 154
6.4.1 NEED FOR LARGE-SCALE DATA ANALYSIS TO DRIVE ADOPTION 154
6.5 HIT-TO-LEAD IDENTIFICATION/LEAD GENERATION 155
6.5.1 HIT-TO-LEAD IDENTIFICATION/LEAD GENERATION TO IMPROVE NEW DRUG POTENCY WITHOUT INCREASING LIPOPHILICITY 155
6.6 LEAD OPTIMIZATION 156
6.6.1 NEED FOR TRANSPARENT PRESENTATION AND ANALYSIS TO BOOST MARKET GROWTH 156
6.7 CANDIDATE SELECTION & VALIDATION 157
6.7.1 HIGH POSSIBILITY OF CLINICAL DRUG FAILURE TO SPUR ADOPTION OF CANDIDATE VALIDATION SERVICES 157
7 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY USE CASE 159
7.1 INTRODUCTION 160
7.2 UNDERSTANDING DISEASES 161
7.2.1 INCREASED FOCUS ON UNDERSTANDING DISEASES TO IMPROVE RESEARCH DATA QUALITY AND QUANTITY 161
7.3 DRUG REPURPOSING 162
7.3.1 INCREASING NEED FOR COST-EFFECTIVE TREATMENTS AND RISING AVAILABILITY OF BIOMEDICAL DATA TO AID MARKET GROWTH 162
7.4 DE NOVO DRUG DESIGN 163
7.4.1 SMALL-MOLECULE DESIGN 164
7.4.1.1 Increasing use of virtual screening and simulation techniques to drive growth 164
7.4.2 VACCINE DESIGN 165
7.4.2.1 Availability of well-validated AI tools to boost market growth 165
7.4.3 ANTIBODY & OTHER BIOLOGICS DESIGN 165
7.4.3.1 Advancements in protein modeling to propel segment growth 165
7.5 DRUG OPTIMIZATION 166
7.5.1 SMALL-MOLECULE OPTIMIZATION 167
7.5.1.1 Leveraging generative models for identifying potential modifications in molecular structures to aid market growth 167
7.5.2 VACCINE OPTIMIZATION 168
7.5.2.1 Effectively predicting vaccine formulations and adjusting delivery vectors to drive growth 168
7.5.3 ANTIBODY & OTHER BIOLOGICS OPTIMIZATION 169
7.5.3.1 Increasing adoption of machine learning for predicting protein structures to augment segment growth 169
7.6 SAFETY & TOXICITY 170
7.6.1 FOCUS ON ADVANCED OFF-TARGET EFFECT PREDICTION, PK/PD SIMULATION, AND QSP MODELING TO DRIVE MARKET 170
 
8 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET,
BY THERAPEUTIC AREA 172
8.1 INTRODUCTION 173
8.2 ONCOLOGY 173
8.2.1 HIGH PREVALENCE OF CANCER AND SHORTAGE OF EFFECTIVE ONCOLOGY DRUGS TO PROPEL MARKET GROWTH 173
8.3 INFECTIOUS DISEASES 175
8.3.1 RISING EPIDEMIC OUTBREAKS TO BOOST DRUG DISCOVERY ACTIVITY 175
8.4 NEUROLOGY 177
8.4.1 COMPLEX DISEASE DIAGNOSIS AND TREATMENT TO INCREASE ADOPTION OF ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY 177
8.5 METABOLIC DISEASES 178
8.5.1 ROLE OF ARTIFICIAL INTELLIGENCE IN UNCOVERING SMALL-MOLECULE THERAPIES TO DRIVE ADOPTION 178
8.6 CARDIOVASCULAR DISEASES 179
8.6.1 SEDENTARY LIFESTYLES AND HIGH PREVALENCE OF OBESITY TO INCREASE NOVEL DRUG DEVELOPMENT FOR CARDIAC DISEASES 179
8.7 IMMUNOLOGY 180
8.7.1 GROWING DRUG PIPELINE FOR IMMUNOLOGICAL DISORDERS TO FAVOR MARKET GROWTH 180
8.8 MENTAL HEALTH DISORDERS 180
8.8.1 INCREASED OCCURRENCE OF MENTAL HEALTH DISEASES IN DEVELOPED ECONOMIES TO SPUR MARKET GROWTH 180
8.9 OTHER THERAPEUTIC AREAS 181
9 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY PLAYER TYPE 182
9.1 INTRODUCTION 183
9.2 END-TO-END SOLUTION PROVIDERS 183
9.2.1 END-TO-END SOLUTION PROVIDERS TO REDUCE NEED FOR MULTIPLE VENDORS AND ACCELERATE WORKFLOWS 183
9.3 NICHE/POINT SOLUTION PROVIDERS 184
9.3.1 ACCURATE, COST-EFFECTIVE, AND LESS TIME CONSUMPTION TO PROPEL MARKET GROWTH 184
9.4 AI TECHNOLOGY PROVIDERS 185
9.4.1 SPECIALIZED AI CAPABILITIES WITH FULL-SERVICE MANAGEMENT TO SUPPORT MARKET GROWTH 185
9.5 BUSINESS PROCESS SERVICE PROVIDERS 186
9.5.1 BETTER ACCESSIBILITY OF HIGH-QUALITY TOOLS AND LOWER DRUG DEVELOPMENT COSTS TO AID MARKET GROWTH 186
 
10 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY AI TOOL 187
10.1 INTRODUCTION 188
10.2 MACHINE LEARNING 188
10.2.1 DEEP LEARNING 190
10.2.1.1 Reduced number of errors and consistent management of data to augment market growth 190
10.2.1.2 Convolutional neural networks 191
10.2.1.3 Recurrent neural networks 191
10.2.1.4 Generative adversarial networks 191
10.2.1.5 Graph neural networks 191
10.2.1.6 Other deep learning technologies 192
10.2.2 SUPERVISED LEARNING 192
10.2.2.1 Supervised learning to predict drug repositioning and manage high-dimensional datasets 192
10.2.3 REINFORCEMENT LEARNING 193
10.2.3.1 Need to accelerate new molecules and maximize performance to augment segment growth 193
10.2.4 UNSUPERVISED LEARNING 194
10.2.4.1 Unsupervised learning to perform complex tasks, uncover potential drug candidates, and optimize lead compounds 194
10.2.5 OTHER MACHINE LEARNING TECHNOLOGIES 195
10.3 NATURAL LANGUAGE PROCESSING 196
10.3.1 NATURAL LANGUAGE PROCESSING TO IDENTIFY INFORMATION WITHIN UNSTRUCTURED DATA AND ACCELERATE DRUG DISCOVERY 196
10.4 CONTEXT-AWARE PROCESSING & COMPUTING 197
10.4.1 CONTEXT-AWARE COMPUTING TO IMPROVE PREDICTIONS OF PATIENT-SPECIFIC DRUG RESPONSES AND OPTIMIZE THERAPEUTIC INTERVENTIONS 197
10.5 COMPUTER VISION 198
10.5.1 COMPUTER VISION TO ENHANCE DRUG DISCOVERY THROUGH ADVANCED IMAGE PROCESSING 198
10.6 IMAGE ANALYSIS 198
10.6.1 BETTER DRUG DISCOVERY THROUGH IMAGE PROCESSING TECHNIQUES TO SUPPORT MARKET GROWTH 198
11 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY DEPLOYMENT 200
11.1 INTRODUCTION 201
11.2 ON-PREMISES DEPLOYMENT 201
11.2.1 PROVISION OF MULTI-VENDOR ARCHITECTURE AND SECURITY BENEFITS TO DRIVE MARKET 201
11.3 CLOUD-BASED DEPLOYMENT 202
11.3.1 FOCUS ON RESEARCH COLLABORATIONS AND ELIMINATION OF SOFTWARE AND HARDWARE PURCHASING COSTS TO DRIVE MARKET 202
11.4 SAAS-BASED DEPLOYMENT 204
11.4.1 LOWER COSTS, BETTER SECURITY, AND EASIER ACCESS TO AUGMENT MARKET GROWTH 204
12 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY END USER 206
12.1 INTRODUCTION 207
12.2 PHARMACEUTICAL & BIOTECHNOLOGY COMPANIES 207
12.2.1 RISING DEMAND FOR COST-EFFECTIVE DRUG DEVELOPMENT TO PROPEL MARKET GROWTH 207
12.3 CONTRACT RESEARCH ORGANIZATIONS 210
12.3.1 RISING NEED FOR OUTSOURCING IN PHARMACEUTICAL & BIOTECHNOLOGY INDUSTRIES TO AID MARKET GROWTH 210
12.4 RESEARCH CENTERS AND ACADEMIC & GOVERNMENT INSTITUTES 211
12.4.1 FOCUS ON DEVELOPING THERAPEUTIC STRATEGIES AND INNOVATIVE APPROACHES IN DRUG DISCOVERY TO AID MARKET GROWTH 211
13 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY REGION 213
13.1 INTRODUCTION 214
13.2 NORTH AMERICA 214
13.2.1 MACROECONOMIC OUTLOOK FOR NORTH AMERICA 214
13.2.2 US 220
13.2.2.1 US to dominate North American market during study period 220
13.2.3 CANADA 226
13.2.3.1 Emergence of new AI-based startups and high health expenditure to support market growth 226
13.3 EUROPE 231
13.3.1 MACROECONOMIC OUTLOOK FOR EUROPE 232
13.3.2 UK 237
13.3.2.1 Favorable government R&D funding to augment market growth 237
13.3.3 GERMANY 243
13.3.3.1 Presence of advanced medical infrastructure and high focus on personalized medicines to drive market 243
13.3.4 FRANCE 248
13.3.4.1 Strong government support and favorable strategies to propel market growth 248
13.3.5 ITALY 253
13.3.5.1 Advanced pharmaceutical industry and increased focus on life science R&D to fuel market growth 253
13.3.6 SPAIN 258
13.3.6.1 Favorable government initiatives and high investments by pharmaceutical companies to boost market growth 258
13.3.7 REST OF EUROPE 263
13.4 ASIA PACIFIC 269
13.4.1 MACROECONOMIC OUTLOOK FOR ASIA PACIFIC 269
13.4.2 JAPAN 275
13.4.2.1 High geriatric population and advanced pharmaceutical research to boost market growth 275
 
13.4.3 CHINA 281
13.4.3.1 Increasing demand for generics and rising government investments to propel market growth 281
13.4.4 INDIA 286
13.4.4.1 Developed IT infrastructure and favorable government initiatives to spur market growth 286
13.4.5 REST OF ASIA PACIFIC 291
13.5 LATIN AMERICA 296
13.5.1 MACROECONOMIC OUTLOOK FOR LATIN AMERICA 297
13.5.2 BRAZIL 302
13.5.2.1 Growing biotechnology sector and increasing governmental initiatives to boost market growth 302
13.5.3 MEXICO 307
13.5.3.1 Favorable government initiatives and high investments by pharmaceutical companies to support market growth 307
13.5.4 REST OF LATIN AMERICA 312
13.6 MIDDLE EAST & AFRICA 317
13.6.1 MACROECONOMIC OUTLOOK FOR MIDDLE EAST & AFRICA 318
13.6.2 GCC COUNTRIES 323
13.6.2.1 Increasing emphasis on personalized medicines and developing healthcare infrastructure to drive market 323
13.6.3 REST OF MIDDLE EAST & AFRICA 328
14 COMPETITIVE LANDSCAPE 334
14.1 INTRODUCTION 334
14.2 KEY PLAYER STRATEGY/RIGHT TO WIN 334
14.2.1 OVERVIEW OF STRATEGIES ADOPTED BY KEY PLAYERS IN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET 334
14.3 REVENUE ANALYSIS, 2019–2023 336
14.4 MARKET SHARE ANALYSIS, 2023 337
14.4.1 RANKING OF KEY MARKET PLAYERS 339
14.5 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023 340
14.5.1 STARS 340
14.5.2 EMERGING LEADERS 340
14.5.3 PERVASIVE PLAYERS 340
14.5.4 PARTICIPANTS 340
14.5.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023 342
14.5.5.1 Company footprint 342
14.5.5.2 Use case footprint 343
14.5.5.3 Process footprint 344
14.5.5.4 Therapeutic area footprint 345
14.5.5.5 Player type footprint 346
14.5.5.6 Deployment mode footprint 347
14.5.5.7 Region footprint 348
14.6 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023 349
14.6.1 PROGRESSIVE COMPANIES 349
14.6.2 RESPONSIVE COMPANIES 349
14.6.3 DYNAMIC COMPANIES 349
14.6.4 STARTING BLOCKS 349
14.6.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023 351
14.7 COMPANY VALUATION AND FINANCIAL METRICS 352
14.7.1 FINANCIAL METRICS 352
14.7.2 COMPANY VALUATION 353
14.8 BRAND/PRODUCT COMPARISON 354
14.9 COMPETITIVE SCENARIO 355
14.9.1 PRODUCT AND SOLUTION LAUNCHES 355
14.9.2 DEALS 356
14.9.3 EXPANSIONS 357
14.9.4 OTHER DEVELOPMENTS 358
15 COMPANY PROFILES 359
15.1 KEY PLAYERS 359
15.1.1 NVIDIA CORPORATION 359
15.1.1.1 Business overview 359
15.1.1.2 Products/Services/Solutions offered 360
15.1.1.3 Recent developments 361
15.1.1.3.1 Product and service launches 361
15.1.1.3.2 Deals 363
15.1.1.4 MnM view 367
15.1.1.4.1 Right to win 367
15.1.1.4.2 Strategic choices 367
15.1.1.4.3 Weaknesses and competitive threats 367
15.1.2 EXSCIENTIA 368
15.1.2.1 Business overview 368
15.1.2.2 Products/Services/Solutions offered 369
15.1.2.3 Recent developments 371
15.1.2.3.1 Solution launches 371
15.1.2.3.2 Deals 371
15.1.2.3.3 Expansions 377
15.1.2.3.4 Other developments 378
15.1.2.4 MnM view 379
15.1.2.4.1 Right to win 379
15.1.2.4.2 Strategic choices 379
15.1.2.4.3 Weaknesses and competitive threats 379
 
15.1.3 GOOGLE 380
15.1.3.1 Business overview 380
15.1.3.2 Products/Services/Solutions offered 381
15.1.3.3 Recent developments 382
15.1.3.3.1 Solution launches 382
15.1.3.3.2 Deals 383
15.1.3.3.3 Expansions 384
15.1.3.4 MnM view 384
15.1.3.4.1 Right to win 384
15.1.3.4.2 Strategic choices 384
15.1.3.4.3 Weaknesses and competitive threats 384
15.1.4 RECURSION 385
15.1.4.1 Business overview 385
15.1.4.2 Products/Services/Solutions offered 386
15.1.4.3 Recent developments 386
15.1.4.3.1 Solution launches 386
15.1.4.3.2 Deals 387
15.1.4.3.3 Expansions 388
15.1.4.4 MnM view 389
15.1.4.4.1 Right to win 389
15.1.4.4.2 Strategic choices made 389
15.1.4.4.3 Weaknesses and competitive threats 389
15.1.5 INSILICO MEDICINE 390
15.1.5.1 Business overview 390
15.1.5.2 Products/Services/Solutions offered 390
15.1.5.3 Recent developments 392
15.1.5.3.1 Product and solution launches and developments 392
15.1.5.3.2 Deals 393
15.1.5.3.3 Other developments 398
15.1.5.4 MnM view 399
15.1.5.4.1 Right to win 399
15.1.5.4.2 Strategic choices 399
15.1.5.4.3 Weaknesses and competitive threats 399
15.1.6 SCHRÖDINGER, INC. 400
15.1.6.1 Business overview 400
15.1.6.2 Products/Services/Solutions offered 401
15.1.6.3 Recent developments 402
15.1.6.3.1 Deals 402
15.1.6.3.2 Other developments 405
 
15.1.7 BENEVOLENTAI 406
15.1.7.1 Business overview 406
15.1.7.2 Products/Services/Solutions offered 407
15.1.7.3 Recent developments 407
15.1.7.3.1 Deals 407
15.1.8 MICROSOFT CORPORATION 409
15.1.8.1 Business overview 409
15.1.8.2 Products/Services/Solutions offered 410
15.1.8.3 Recent developments 411
15.1.8.3.1 Deals 411
15.1.9 ATOMWISE INC. 413
15.1.9.1 Business overview 413
15.1.9.2 Products/Services/Solutions offered 413
15.1.9.3 Recent developments 414
15.1.9.3.1 Deals 414
15.1.10 ILLUMINA, INC. 415
15.1.10.1 Business overview 415
15.1.10.2 Products/Services/Solutions offered 416
15.1.10.3 Recent developments 417
15.1.10.3.1 Solution launches 417
15.1.10.3.2 Deals 418
15.1.11 NUMEDII, INC. 420
15.1.11.1 Business overview 420
15.1.11.2 Products/Services/Solutions offered 420
15.1.12 XTALPI INC. 421
15.1.12.1 Business overview 421
15.1.12.2 Products/Services/Solutions offered 421
15.1.12.3 Recent developments 422
15.1.12.3.1 Deals 422
15.1.13 IKTOS 424
15.1.13.1 Business overview 424
15.1.13.2 Products/Services/Solutions offered 425
15.1.13.3 Recent developments 426
15.1.13.3.1 Deals 426
15.1.13.3.2 Other developments 429
15.1.14 TEMPUS 430
15.1.14.1 Business overview 430
15.1.14.2 Products/Services/Solutions offered 430
15.1.14.3 Recent developments 431
15.1.14.3.1 Solution launches 431
15.1.14.3.2 Deals 432
15.1.14.3.3 Expansions 435
15.1.14.3.4 Other developments 435
15.1.15 DEEP GENOMICS 436
15.1.15.1 Business overview 436
15.1.15.2 Products/Services/Solutions offered 436
15.1.15.3 Recent developments 437
15.1.15.3.1 Solution launches 437
15.1.15.3.2 Deals 437
15.1.15.3.3 Other developments 437
15.1.16 VERGE GENOMICS 438
15.1.16.1 Business overview 438
15.1.16.2 Products/Services/Solutions offered 438
15.1.16.3 Recent developments 439
15.1.16.3.1 Deals 439
15.1.17 BENCHSCI 440
15.1.17.1 Business overview 440
15.1.17.2 Products/Services/Solutions offered 440
15.1.17.3 Recent developments 441
15.1.17.3.1 Solution launches 441
15.1.17.3.2 Deals 441
15.1.17.3.3 Other developments 441
15.1.18 INSITRO 442
15.1.18.1 Business overview 442
15.1.18.2 Products/Services/Solutions offered 442
15.1.18.3 Recent developments 443
15.1.18.3.1 Deals 443
15.1.18.3.2 Other developments 443
15.1.19 VALO HEALTH 444
15.1.19.1 Business overview 444
15.1.19.2 Products/Services/Solutions offered 444
15.1.19.3 Recent developments 445
15.1.19.3.1 Deals 445
15.1.19.3.2 Other developments 446
15.1.20 BPGBIO, INC. 447
15.1.20.1 Business overview 447
15.1.20.2 Products/Services/Solutions offered 447
15.1.20.3 Recent developments 448
15.1.20.3.1 Deals 448
15.1.21 MERCK KGAA 449
15.1.21.1 Business overview 449
15.1.21.2 Products/Services/Solutions offered 450
15.1.21.3 Recent developments 451
15.1.21.3.1 Solution launches 451
15.1.21.3.2 Deals 451
15.1.21.3.3 Expansions 452
15.1.21.3.4 Other developments 453
15.2 OTHER PLAYERS 454
15.2.1 PREDICTIVE ONCOLOGY 454
15.2.2 IQVIA INC. 455
15.2.3 TENCENT HOLDINGS LIMITED 456
15.2.4 CYTOREASON LTD. 457
15.2.5 OWKIN, INC. 458
15.2.6 CLOUD PHARMACEUTICALS 459
15.2.7 EVAXION BIOTECH A/S 460
15.2.8 STANDIGM INC. 461
15.2.9 BIOAGE LABS 462
15.2.10 ENVISAGENICS 463
15.2.11 ABCELLERA 464
15.2.12 CENTELLA 465
16 APPENDIX 466
16.1 DISCUSSION GUIDE 466
16.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 474
16.3 CUSTOMIZATION OPTIONS 476
16.4 RELATED REPORTS 476
16.5 AUTHOR DETAILS 477