Summary

The global AI in biotechnology market is projected to reach USD 7.75 billion by 2029 from USD 3.23 billion in 2024, at a high CAGR of 19.1% during the forecast period. The market is expected to grow as a result of the increasing demand for personalized therapies and precision medicines, and the growing applications of AI in epidemiological models for predicting disease outbreaks. It helps public health officials to respond and develop better vaccines which further drives market growth. The increasing demand for personalized therapies and precision medicine has led to an increasing number of clinical trials performed. For instance, as of October 2023, around 1584 clinical trials performed using AI for various diseases were reported to Clincaltrials.gov. However, the limited interpretability of AI algorithms, high implementation cost and data privacy & security concerns are some of the restraining factors for the market growth.
“Based on function, research & development segment dominated the AI in biotechnology market in 2023”
The AI in biotechnology market by function is broadly divided into six segments: research & development, regulatory compliance, manufacturing & supply chain, launch & commercial, and post-market surveillance & patient support. The research & development segment accounted for the largest share of the global AI in biotechnology market in 2023. The large share of this segment can be attributed to the rising demand for personalized medicine, automation in labs, the rise of predictive analytics, and the need for faster drug discovery. an increase in the number of AI-discovered molecules in clinical trials significantly augments market growth. For instance, AI-native Biotechs and their partners in the pharmaceutical industry have entered an increasing number of molecules for AI-driven clinical trials (Source: Elsevier B.V.). In 2023, there were around 67 reported ongoing trials, and this number has increased from 2014 with around 60% year-over-year compound growth.

“In 2023, the pharmaceutical companies held the largest market share among end users.”
Based on end user, pharmaceutical companies hold the largest share of the AI in biotechnology market. There are emerging health problems, notably cognitive decline that led to high healthcare services and medication demands, associated with such demographic shifts. Pharmaceutical companies will experience huge growth opportunities as life expectancy increases, thereby raising the increasing healthcare demands of this aging population. Additionally, massive investments are being made by pharmaceutical companies into research and development, particularly drug discovery and development processes wherein AI is utilized for tasks like target identification, lead optimization, and patient stratification in clinical trials.

“In 2023, Europe was the second largest regional market for AI in the biotechnology market.”

In 2023, Europe held the second-highest share of the AI in biotechnology market. This dominance is attributed to a substantial increase in investment in Europe for AI, with a growing number of patent filings for biotechnology-related medical technology. In the year 2022, the European Patent Office (EPO) published over 10,000 AI patent applications, which highlights an increased focus on AI solutions in biotechnology. In March 2023, the UK government pledged investment in nine promising AI healthcare technologies to speed up research and development. Moreover, in August 2023, the government launched 22 new projects to explore the application of AI in healthcare. All these initiatives represent Europe's determination to spearhead applications of AI in the biotechnology sector.

The break-down of primary participants is as mentioned below:
• By Company Type - Tier 1: 45%, Tier 2: 30%, and Tier 3: 25%
• By Designation - C-level: 42%, Director-level: 31%, and Others: 27%
• By Region - North America: 32%, Europe: 32%, Asia Pacific: 26%, Middle East & Africa: 5%, Latin America: 5%
NVIDIA Corporation (US), Illumina, Inc. (US), Exscientia plc (UK), Schrödinger, Inc. (US), Recursion Pharmaceuticals, Inc. (US), SOPHiA GENETICS (Switzerland), Predictive Oncology. (US), Deep Genomics. (Canada), ), Data4Cure, Inc. (US), Genoox (US), BenevolentAI (US), and DNAnexus, Inc. (US) are some of the key players in the AI in biotechnology market.
The study includes an in-depth competitive analysis of these key players in AI in biotechnology market, with their company profiles, recent developments, and key market strategies.
Research Coverage:
The report analyses the AI in biotechnology market. It aims to estimate the market size and future growth potential of various market segments based on offering (end-to-end solutions, niche solutions, technology providers, and services), function (research & development [R&D], regulatory compliance, manufacturing & supply chain, launch & commercial, post-market surveillance & patient support, and corporate), deployment mode (cloud-based, and on-premise), end-user (pharmaceutical companies, biotechnology companies, research institutes and labs, healthcare providers, and contract research organizations [CRO]), and 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 AI in biotechnology market. A detailed analysis of the key industry players has been done to provide insights into their business overview, solutions, and services; key strategies; partnerships, collaborations, acquisitions, expansion, agreements, investment, and product launches associated with the AI in biotechnology market. Competitive analysis of upcoming startups in the AI in biotechnology market ecosystem is covered in this report.
Reasons to Buy the Report
This report will enrich established firms and new entrants/smaller firms to gauge the market's pulse, 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 below-mentioned strategies to strengthen their positions in the market.
This report provides insights on:
Analysis of key drivers: (growing cross-industry collaborations and partnerships, growing need to reduce the time and cost of drug discovery and development, rising adoption of AI in precision medicine, improving computing power, and declining hardware cost), restraints (high implementation costs of AI limit adoption in biotechnology, especially for SMEs and emerging economies, data privacy risks and compliance challenges for AI in biotechnology), opportunities (integrating AI and big data in precision medicine for biotechnology advancement, surge in biotechnology investments enhances opportunities for AI to accelerate drug discovery innovations, innovation across healthcare, agriculture, and environmental science for global growth), and challenges (data quality and interpretability issues that hinder AI integration and trustworthiness, AI deployment in biotechnology hindered by talent shortages and evolving regulatory challenges) influencing the growth of the AI in biotechnology market.
 Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product launches in the AI in biotechnology market.
 Market Development: Comprehensive information on the lucrative emerging markets, by offering, function, deployment mode, end-user, and region.
 Market Diversification: Exhaustive information about the product portfolios, growing geographies, recent developments, and investments in the AI in biotechnology market.
 Competitive Assessment: In-depth assessment of market shares, growth strategies, product offerings, and capabilities of the leading players in the AI in biotechnology market including NVIDIA Corporation (US), Illumina, Inc. (US), Exscientia (UK), Schrödinger, Inc. (US), Recursion Pharmaceuticals, Inc. (US), SOPHiA GENETICS (Switzerland), Predictive Oncology. (US),Deep Genomics. (Canada), Exscientia (US), and Data4Cure, Inc.

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Table of Contents

1 INTRODUCTION 37
1.1 STUDY OBJECTIVES 37
1.2 MARKET DEFINITION 37
1.3 STUDY SCOPE 38
1.3.1 MARKETS COVERED & REGIONAL SCOPE 38
1.3.2 INCLUSIONS & EXCLUSIONS 39
1.3.3 YEARS CONSIDERED 40
1.4 CURRENCY CONSIDERED 41
1.5 LIMITATIONS 41
1.6 STAKEHOLDERS 42
2 RESEARCH METHODOLOGY 43
2.1 RESEARCH DATA 43
2.1.1 SECONDARY DATA 44
2.1.1.1 Key data from secondary sources 45
2.1.2 PRIMARY DATA 45
2.1.2.1 Key data from primary sources 47
2.1.2.2 Insights from primary experts 48
2.2 MARKET SIZE ESTIMATION 49
2.3 DATA TRIANGULATION 53
2.4 MARKET SHARE ESTIMATION 54
2.5 RESEARCH ASSUMPTIONS 54
2.6 LIMITATIONS 54
2.6.1 METHODOLOGY-RELATED LIMITATIONS 54
2.6.2 SCOPE-RELATED LIMITATIONS 54
2.7 RISK ASSESSMENT 55
3 EXECUTIVE SUMMARY 56
4 PREMIUM INSIGHTS 60
4.1 AI IN BIOTECHNOLOGY MARKET OVERVIEW 60
4.2 AI IN BIOTECHNOLOGY MARKET, BY REGION 61
4.3 NORTH AMERICA: AI IN BIOTECHNOLOGY MARKET, BY END USER & REGION 62
4.4 AI IN BIOTECHNOLOGY MARKET: GEOGRAPHIC SNAPSHOT 63
4.5 AI IN BIOTECHNOLOGY MARKET: DEVELOPED VS. EMERGING ECONOMIES 63
 
5 MARKET OVERVIEW 64
5.1 INTRODUCTION 64
5.2 MARKET DYNAMICS 64
5.2.1 DRIVERS 65
5.2.1.1 Growing cross-industry collaborations and partnerships 65
5.2.1.2 Growing need to reduce time and cost of drug discovery and development 66
5.2.1.3 Rising adoption of AI in precision medicine 66
5.2.1.4 Improving computing power and declining hardware cost 67
5.2.2 RESTRAINTS 68
5.2.2.1 High implementation costs of AI limit adoption in biotechnology, especially for SMEs and emerging economies 68
5.2.2.2 Data privacy risks and compliance challenges for AI in biotechnology 68
5.2.3 OPPORTUNITIES 69
5.2.3.1 Integrating AI and big data in precision medicine for biotechnology advancement 69
5.2.3.2 Surge in biotechnology investments enhances opportunities for AI to accelerate drug discovery innovations 70
5.2.3.3 Innovation across healthcare, agriculture, and environmental science for global growth 70
5.2.4 CHALLENGES 70
5.2.4.1 Data quality and interpretability issues that hinder AI integration and trustworthiness 70
5.2.4.2 AI deployment in biotechnology hindered by talent shortages and evolving regulatory challenges 71
5.3 ECOSYSTEM ANALYSIS 72
5.4 CASE STUDY ANALYSIS 73
5.4.1 LEVERAGED NVIDIA DGX CLOUD FOR RAPID TRAINING OF PROTEIN MODELS 73
5.4.2 IMPLEMENTED END-TO-END NGS WORKFLOW FOR EFFICIENT GENETIC VARIANT DETECTION 73
5.4.3 ACCELERATED DRUG DISCOVERY WITH GENERATIVE AI AND STREAMLINED WORKFLOWS 74
5.5 VALUE CHAIN ANALYSIS 74
5.6 PORTER'S FIVE FORCES ANALYSIS 76
5.6.1 BARGAINING POWER OF SUPPLIERS 77
5.6.2 BARGAINING POWER OF BUYERS 77
5.6.3 THREAT OF SUBSTITUTES 77
5.6.4 THREAT OF NEW ENTRANTS 77
5.6.5 INTENSITY OF COMPETITIVE RIVALRY 77
5.7 REGULATORY ANALYSIS 78
5.7.1 REGULATORY LANDSCAPE 78
5.7.1.1 North America 78
5.7.1.2 Europe 79
5.7.1.3 Asia Pacific 80
5.7.1.4 Latin America 81
5.7.1.5 Middle East & Africa 81
5.7.2 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 81
5.8 PATENT ANALYSIS 84
5.8.1 PATENT PUBLICATION TRENDS FOR AI IN BIOTECHNOLOGY 84
5.8.2 JURISDICTION AND TOP APPLICANT ANALYSIS 85
5.9 TECHNOLOGY ANALYSIS 89
5.9.1 KEY TECHNOLOGIES 89
5.9.1.1 Natural language processing (NLP) 89
5.9.1.2 Predictive analytics 89
5.9.2 COMPLEMENTARY TECHNOLOGIES 89
5.9.2.1 Cloud computing 89
5.9.2.2 Big data analytics 89
5.10 INDUSTRY TRENDS 90
5.10.1 EVOLUTION OF AI IN BIOTECHNOLOGY 90
5.10.2 COMPUTER-AIDED DRUG DESIGN AND AI 91
5.11 PRICING ANALYSIS 91
5.11.1 INDICATIVE PRICING ANALYSIS, BY DRUG DISCOVERY PROCESS 92
5.11.2 AVERAGE SELLING PRICE, BY REGION (QUALITATIVE) 92
5.12 KEY CONFERENCES & EVENTS, 2025–2026 92
5.13 KEY STAKEHOLDERS & BUYING CRITERIA 94
5.13.1 BUYING CRITERIA 95
5.14 TRENDS & DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESSES 96
5.15 END-USER ANALYSIS 97
5.15.1 UNMET NEEDS 97
5.15.2 END-USER EXPECTATIONS 98
5.16 INVESTMENT & FUNDING SCENARIO 98
5.17 IMPACT OF AI/GEN AI ON AI IN BIOTECHNOLOGY MARKET 99
5.17.1 KEY USE CASES 100
5.17.2 CASE STUDIES OF AI/GENERATIVE AI IMPLEMENTATION 100
5.17.2.1 Case study: Accelerated biomarker discovery and clinical trial optimization 100
5.17.3 IMPACT OF AI/GEN AI ON INTERCONNECTED AND ADJACENT ECOSYSTEMS 101
5.17.3.1 Drug discovery and development market 101
5.17.3.2 Genomics and bioinformatics market 101
5.17.3.3 Medical imaging & diagnostics market 102
5.17.4 USER READINESS & IMPACT ASSESSMENT 102
5.17.4.1 User readiness 102
5.17.4.1.1 Pharmaceutical companies 102
5.17.4.1.2 Biotechnology companies 102
 
5.17.4.2 Impact assessment 103
5.17.4.2.1 User A: Pharmaceutical companies 103
5.17.4.2.1.1 Implementation 103
5.17.4.2.1.2 Impact 103
5.17.4.2.2 User B: Biotechnology companies 103
5.17.4.2.2.1 Implementation 103
5.17.4.2.2.2 Impact 103
6 AI IN BIOTECHNOLOGY MARKET, BY OFFERING 104
6.1 INTRODUCTION 105
6.2 END-TO-END SOLUTIONS 105
6.2.1 GROWING USE OF ADVANCED ALGORITHMS TO IMPROVE PRECISION AND EFFICIENCY TO BOOST MARKET GROWTH 105
6.3 NICHE SOLUTIONS 106
6.3.1 ABILITY OF NICHE SOLUTIONS TO ADDRESS SPECIFIC CHALLENGES WITHIN DRUG DISCOVERY TO SUPPORT ADOPTION 106
6.4 TECHNOLOGIES 107
6.4.1 ABILITY OF TECHNOLOGIES TO ENHANCE DRUG DISCOVERY, PERSONALIZED MEDICINE, AND DATA ANALYTICS TO FUEL GROWTH 107
6.5 SERVICES 108
6.5.1 CONSULTING SERVICES 109
6.5.1.1 Increasing efficiency of research processes and cost savings to boost adoption of consulting services 109
6.5.2 IMPLEMENTATION SERVICES & ONGOING IT SUPPORT 110
6.5.2.1 Increasing precision and efficiency in IT support services to boost demand 110
6.5.3 TRAINING & EDUCATION SERVICES 110
6.5.3.1 Need for skilled talent to drive market growth 110
6.5.4 POST-SALES & MAINTENANCE SERVICES 111
6.5.4.1 Complexity of AI systems and need for improvement in AI algorithms to boost market 111
7 AI IN BIOTECHNOLOGY MARKET, BY FUNCTION 113
7.1 INTRODUCTION 114
7.2 RESEARCH & DEVELOPMENT 114
7.2.1 DRUG DISCOVERY 116
7.2.1.1 Molecular design & optimization 117
7.2.1.1.1 Increased efficiency in drug discovery with molecular design & optimization to drive market growth 117
7.2.1.2 Biomarker discovery 118
7.2.1.2.1 Ability to analyze large data sets with AI-enabled biomarker discovery to boost demand for 118
 
7.2.1.3 Structure-activity relationship (SAR) modeling 119
7.2.1.3.1 Improved data analysis, predictive modeling, and compound optimization for drug candidates with SAR to fuel growth 119
7.2.2 CLINICAL DEVELOPMENT 119
7.2.2.1 Trial design 121
7.2.2.1.1 Ability of AI to improve trial design through simulations and patient stratification to favor market 121
7.2.2.2 Site selection 121
7.2.2.2.1 Optimized process of selecting clinical trial sites to fuel growth 121
7.2.2.3 Recruitment 122
7.2.2.3.1 Enhanced process of selecting and enrolling participants for clinical trials to drive demand 122
7.2.2.4 Clinical data assessment 123
7.2.2.4.1 Ability of clinical data assessment to enhance efficiency and accuracy of data interpretation to propel market 123
7.2.2.5 Predictive toxicity & risk monitoring 123
7.2.2.5.1 Ability of data integration and predictive modeling to create comprehensive risk profiles for drug candidates to support market 123
7.2.2.6 Monitoring & drug adherence 124
7.2.2.6.1 Enhanced patient compliance with monitoring & drug adherence to drive market growth 124
7.2.2.7 Real-world evidence (RWE) analysis 125
7.2.2.7.1 Enhanced safety monitoring & economic evaluation with RWE analysis to propel growth 125
7.3 REGULATORY COMPLIANCE 125
7.3.1 ABILITY OF AI TO ENSURE REGULATORY COMPLIANCE IN CLINICAL TRIALS TO SUPPORT GROWTH 125
7.4 MANUFACTURING & SUPPLY CHAIN 126
7.4.1 SUPPLY CHAIN PLANNING 128
7.4.1.1 Increasing demand for real-time data analytics to accelerate market growth 128
7.4.2 INVENTORY MANAGEMENT 128
7.4.2.1 Automating stock tracking and replenishment with advanced analytics to fuel growth 128
7.4.3 LOGISTICS OPTIMIZATION 129
7.4.3.1 Ability of AI to drive collaboration and transparency in biotechnology logistics to aid growth 129
7.4.4 DEMAND FORECASTING 130
7.4.4.1 Ability to integrate data for reliable demand forecasts to fuel growth 130
7.4.5 PREDICTIVE MAINTENANCE 130
7.4.5.1 Boosting equipment reliability with AI-powered predictive maintenance to drive demand 130
7.4.6 OTHER MANUFACTURING & SUPPLY CHAIN FUNCTIONS 131
7.5 LAUNCH & COMMERCIAL 132
7.5.1 LAUNCH COORDINATION 133
7.5.1.1 Growing product launch success rates through predictive analytics to boost adoption 133
7.5.2 PATIENT ENGAGEMENT 133
7.5.2.1 Advantages such as real-time patient feedback for better health outcomes to support growth 133
7.5.3 MARKETING OPERATIONS 134
7.5.3.1 Enhanced marketing performance with AI to boost market 134
7.5.4 PREDICTIVE PRICING 135
7.5.4.1 Ability of AI to enhance pricing accuracy to drive adoption 135
7.6 POST-MARKETING SURVEILLANCE & PATIENT SUPPORT 135
7.6.1 MEDICATION ADHERENCE 136
7.6.1.1 Growing demand for personalized healthcare to drive market 136
7.6.2 ADVERSE EVENT REPORTING 137
7.6.2.1 Advantages such as faster post-market surveillance and enhanced drug safety to drive demand 137
7.6.3 PATIENT MONITORING 138
7.6.3.1 Rise of remote healthcare solutions to boost demand 138
7.6.4 COMPLIANCE MONITORING 138
7.6.4.1 Increasing complexity of regulatory requirements to drive adoption 138
7.6.5 PATIENT SUPPORT PROGRAMS 139
7.6.5.1 Growing interest in patient-centered care to support growth 139
7.7 CORPORATE 140
7.7.1 RISK MANAGEMENT 141
7.7.1.1 Rising expenditure for drug development to support growth 141
7.7.2 COMPLIANCE MONITORING 141
7.7.2.1 Strict guidelines from bodies to aid growth 141
7.7.3 SALES FORCE OPTIMIZATION 142
7.7.3.1 Need for data-driven decision-making to boost adoption of sales force optimization 142
7.7.4 OTHER CORPORATE FUNCTIONS 143
8 AI IN BIOTECHNOLOGY MARKET, BY DEPLOYMENT MODE 144
8.1 INTRODUCTION 145
8.2 CLOUD-BASED SOLUTIONS 145
8.2.1 PUBLIC CLOUD 146
8.2.1.1 Need to reduce dependency on expensive on-premise infrastructure to boost demand 146
8.2.2 PRIVATE CLOUD 147
8.2.2.1 Need for enhanced security and data protection to drive market growth 147
8.2.3 MULTI-CLOUD 148
8.2.3.1 Enhanced flexibility & cost optimization to support market growth 148
8.2.4 HYBRID CLOUD 149
8.2.4.1 Cost efficiency and flexibility of hybrid cloud to fuel growth 149
8.3 ON-PREMISE SOLUTIONS 150
8.3.1 ADVANTAGES SUCH AS DATA SECURITY AND PRIVACY AND COMPLIANCE WITH REGULATIONS TO FAVOR GROWTH 150
9 AI IN BIOTECHNOLOGY MARKET, BY END USER 152
9.1 INTRODUCTION 153
9.2 PHARMACEUTICAL COMPANIES 153
9.2.1 INNOVATION AND EFFICIENCY ASSOCIATED WITH AI INTEGRATION IN DRUG DISCOVERY & DEVELOPMENT TO BOOST ADOPTION 153
9.3 BIOTECHNOLOGY COMPANIES 154
9.3.1 ABILITY OF AI-DRIVEN INNOVATIONS TO ACCELERATE PERSONALIZED MEDICINE AND DRUG DISCOVERY TO SUPPORT GROWTH 154
9.4 RESEARCH INSTITUTES & LABS 155
9.4.1 STRATEGIC INVESTMENTS AND COLLABORATIONS TO PROPEL AI ADVANCEMENTS IN RESEARCH INSTITUTES AND LABS 155
9.5 HEALTHCARE PROVIDERS 156
9.5.1 IMPROVED PATIENT OUTCOMES TO SUPPORT ADOPTION 156
9.6 CONTRACT RESEARCH ORGANIZATIONS (CROS) 157
9.6.1 ABILITY OF AI TECHNOLOGIES TO ACCELERATE CLINICAL TRIALS AND IMPROVE PATIENT RECRUITMENT TO FUEL GROWTH 157
10 AI IN BIOTECHNOLOGY MARKET, BY REGION 159
10.1 INTRODUCTION 160
10.2 NORTH AMERICA 161
10.2.1 MACROECONOMIC OUTLOOK FOR NORTH AMERICA 167
10.2.2 US 167
10.2.2.1 Increasing investments and partnerships to drive market 167
10.2.3 CANADA 173
10.2.3.1 Availability of advanced facilities and shorter approval times for drug candidates to drive market 173
10.3 EUROPE 179
10.3.1 MACROECONOMIC OUTLOOK FOR EUROPE 186
10.3.2 GERMANY 186
10.3.2.1 Increased funding in start-ups to drive uptake of AI in biotechnology 186
10.3.3 UK 192
10.3.3.1 Increasing investments and government fund allocations to drive market 192
10.3.4 FRANCE 197
10.3.4.1 Government initiatives in France to support market growth 197
10.3.5 ITALY 203
10.3.5.1 Growing investments to create opportunities for market growth 203
 
10.3.6 SPAIN 209
10.3.6.1 Increasing need for personalized medicine and data-driven healthcare to increase adoption rate in market 209
10.3.7 REST OF EUROPE 214
10.4 ASIA PACIFIC 220
10.4.1 MACROECONOMIC OUTLOOK FOR ASIA PACIFIC 228
10.4.2 JAPAN 228
10.4.2.1 Accelerating AI-driven drug discovery and biotechnology innovation to drive market in Japan 228
10.4.3 CHINA 234
10.4.3.1 Rising foreign investments to drive market in China 234
10.4.4 INDIA 240
10.4.4.1 Increasing number of start-ups and support from government to propel market 240
10.4.5 SOUTH KOREA 246
10.4.5.1 Significant advances in AI integration for R&D to fuel growth 246
10.4.6 REST OF ASIA PACIFIC 252
10.5 LATIN AMERICA 258
10.5.1 MACROECONOMIC OUTLOOK FOR LATIN AMERICA 264
10.5.2 BRAZIL 264
10.5.2.1 Funding of biotech companies to drive market in Brazil 264
10.5.3 MEXICO 270
10.5.3.1 Investment inflows and strengthening AI-related education to drive market in Mexico 270
10.5.4 REST OF LATIN AMERICA 276
10.6 MIDDLE EAST & AFRICA 282
10.6.1 MACROECONOMIC OUTLOOK FOR MIDDLE EAST & AFRICA 288
10.6.2 GCC COUNTRIES 288
10.6.2.1 Increase in healthcare investments to support market growth 288
10.6.3 REST OF MIDDLE EAST & AFRICA 295
11 COMPETITIVE LANDSCAPE 302
11.1 INTRODUCTION 302
11.2 KEY PLAYER STRATEGY/RIGHT TO WIN 303
11.2.1 OVERVIEW OF STRATEGIES ADOPTED BY PLAYERS IN AI IN BIOTECHNOLOGY MARKET 303
11.3 REVENUE ANALYSIS, 2019–2023 304
11.4 MARKET SHARE ANALYSIS, 2023 305
11.4.1 RANKING OF KEY MARKET PLAYERS 308
11.5 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023 308
11.5.1 STARS 308
11.5.2 EMERGING LEADERS 308
11.5.3 PERVASIVE PLAYERS 309
11.5.4 PARTICIPANTS 309
11.5.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023 310
11.5.5.1 Company footprint 310
11.5.5.2 Component footprint 311
11.5.5.3 Application footprint 312
11.5.5.4 End-user footprint 313
11.5.5.5 Region footprint 314
11.6 COMPANY EVALUATION MATRIX: START-UPS/SMES, 2023 315
11.6.1 PROGRESSIVE COMPANIES 315
11.6.2 RESPONSIVE COMPANIES 315
11.6.3 DYNAMIC COMPANIES 315
11.6.4 STARTING BLOCKS 315
11.6.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023 317
11.7 COMPANY VALUATION & FINANCIAL METRICS 319
11.8 BRAND/PRODUCT COMPARISON 320
11.9 COMPETITIVE SCENARIO 321
11.9.1 PRODUCT LAUNCHES & UPGRADES 321
11.9.2 DEALS 322
11.9.3 EXPANSIONS 324
12 COMPANY PROFILES 325
12.1 KEY PLAYERS 325
12.1.1 NVIDIA CORPORATION 325
12.1.1.1 Business overview 325
12.1.1.2 Products offered 326
12.1.1.3 Recent developments 327
12.1.1.3.1 Product launches 327
12.1.1.3.2 Deals 327
12.1.1.4 MnM view 328
12.1.1.4.1 Right to win 328
12.1.1.4.2 Strategic choices 328
12.1.1.4.3 Weaknesses & competitive threats 328
12.1.2 ILLUMINA, INC. 329
12.1.2.1 Business overview 329
12.1.2.2 Products offered 330
12.1.2.3 Recent developments 331
12.1.2.3.1 Product launches 331
12.1.2.3.2 Deals 332
12.1.2.4 MnM view 333
12.1.2.4.1 Right to win 333
12.1.2.4.2 Strategic choices 334
12.1.2.4.3 Weaknesses & competitive threats 334
 
12.1.3 EXSCIENTIA 335
12.1.3.1 Business overview 335
12.1.3.2 Products offered 336
12.1.3.3 Recent developments 336
12.1.3.3.1 Product launches 336
12.1.3.3.2 Deals 336
12.1.3.3.3 Other developments 339
12.1.3.4 MnM view 340
12.1.3.4.1 Right to win 340
12.1.3.4.2 Strategic choices 340
12.1.3.4.3 Weaknesses & competitive threats 340
12.1.4 SCHRÖDINGER, INC. 341
12.1.4.1 Business overview 341
12.1.4.2 Products offered 342
12.1.4.3 Recent developments 343
12.1.4.3.1 Product upgrades 343
12.1.4.3.2 Deals 343
12.1.5 RECURSION PHARMACEUTICALS, INC. 345
12.1.5.1 Business overview 345
12.1.5.2 Products offered 346
12.1.5.3 Recent developments 346
12.1.5.3.1 Deals 346
12.1.5.3.2 Expansions 347
12.1.5.3.3 Other developments 347
12.1.6 SOPHIA GENETICS 348
12.1.6.1 Business overview 348
12.1.6.2 Products offered 349
12.1.6.3 Recent developments 349
12.1.6.3.1 Product launches 349
12.1.6.3.2 Deals 350
12.1.7 PREDICTIVE ONCOLOGY 352
12.1.7.1 Business overview 352
12.1.7.2 Products offered 353
12.1.7.3 Recent developments 353
12.1.7.3.1 Product launches 353
12.1.7.3.2 Deals 353
12.1.7.3.3 Expansions 354
 
12.1.8 BENEVOLENTAI 355
12.1.8.1 Business overview 355
12.1.8.2 Products offered 356
12.1.8.3 Recent developments 356
12.1.8.3.1 Deals 356
12.1.8.3.2 Other developments 357
12.1.9 EUROFINS DISCOVERY 358
12.1.9.1 Business overview 358
12.1.9.2 Products offered 359
12.1.9.3 Recent developments 359
12.1.9.3.1 Product launches 359
12.1.9.3.2 Deals 360
12.1.9.3.3 Expansions 361
12.1.10 XTALPI INC. 362
12.1.10.1 Business overview 362
12.1.10.2 Products offered 363
12.1.10.3 Recent developments 363
12.1.10.3.1 Deals 363
12.1.11 DNANEXUS, INC. 365
12.1.11.1 Business overview 365
12.1.11.2 Products offered 365
12.1.11.3 Recent developments 366
12.1.11.3.1 Deals 366
12.1.11.3.2 Other developments 368
12.1.12 NUMEDII, INC. 369
12.1.12.1 Business overview 369
12.1.12.2 Products offered 369
12.1.13 BPGBIO, INC. 370
12.1.13.1 Business overview 370
12.1.13.2 Products offered 370
12.1.13.3 Recent developments 371
12.1.13.3.1 Product launches 371
12.1.13.3.2 Deals 371
12.1.13.3.3 Other developments 372
12.1.14 IKTOS 373
12.1.14.1 Business overview 373
12.1.14.2 Products offered 373
12.1.14.3 Recent developments 374
12.1.14.3.1 Deals 374
12.1.14.3.2 Other developments 374
 
12.1.15 INSILICO MEDICINE 375
12.1.15.1 Business overview 375
12.1.15.2 Products offered 375
12.1.15.3 Recent developments 376
12.1.15.3.1 Product approvals 376
12.1.15.3.2 Deals 376
12.1.15.3.3 Other developments 376
12.1.16 LOGICA (CHARLES RIVER + VALO HEALTH) 377
12.1.16.1 Business overview 377
12.1.16.2 Products offered 377
12.1.16.3 Recent developments 378
12.1.16.3.1 Product launches 378
12.1.17 AMERICAN CHEMICAL SOCIETY 379
12.1.17.1 Business overview 379
12.1.17.2 Products offered 379
12.1.18 AGANITHA AI INC. 380
12.1.18.1 Business overview 380
12.1.18.2 Products offered 380
12.1.18.3 Recent developments 381
12.1.18.3.1 Deals 381
12.1.18.3.2 Other developments 381
12.2 START-UP/SME PLAYERS 382
12.2.1 VERISIM LIFE 382
12.2.2 VALO HEALTH 382
12.2.3 TEMPUS AI, INC. 383
12.2.4 LIFEBIT BIOTECH LTD. 384
12.2.5 GENOOX 384
12.2.6 DATA4CURE, INC. 385
12.2.7 DEEP GENOMICS 385
13 APPENDIX 386
13.1 DISCUSSION GUIDE 386
13.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 393
13.3 CUSTOMIZATION OPTIONS 395
13.4 RELATED REPORTS 395
13.5 AUTHOR DETAILS 396