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 40
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 68
5.2.3.1 Integrating AI and big data in precision medicine for biotechnology advancement 68
5.2.3.2 Surge in biotechnology investments enhances opportunities for AI to accelerate drug discovery innovations 69
5.2.3.3 Innovation across healthcare, agriculture, and environmental science for global growth 69
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 70
5.3 ECOSYSTEM ANALYSIS 71
5.4 CASE STUDY ANALYSIS 72
5.4.1 LEVERAGED NVIDIA DGX CLOUD FOR RAPID TRAINING OF PROTEIN MODELS 72
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 73
5.5 VALUE CHAIN ANALYSIS 74
5.6 PORTER'S FIVE FORCES ANALYSIS 75
5.6.1 BARGAINING POWER OF SUPPLIERS 76
5.6.2 BARGAINING POWER OF BUYERS 76
5.6.3 THREAT OF SUBSTITUTES 76
5.6.4 THREAT OF NEW ENTRANTS 76
5.6.5 INTENSITY OF COMPETITIVE RIVALRY 76
5.7 REGULATORY ANALYSIS 77
5.7.1 REGULATORY LANDSCAPE 77
5.7.1.1 North America 77
5.7.1.2 Europe 78
5.7.1.3 Asia Pacific 79
5.7.1.4 Latin America 80
5.7.1.5 Middle East & Africa 80
5.7.2 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 80
5.8 PATENT ANALYSIS 83
5.8.1 PATENT PUBLICATION TRENDS FOR AI IN BIOTECHNOLOGY 83
5.8.2 JURISDICTION AND TOP APPLICANT ANALYSIS 84
5.9 TECHNOLOGY ANALYSIS 87
5.9.1 KEY TECHNOLOGIES 87
5.9.1.1 Natural Language Processing (NLP) 87
5.9.1.2 Predictive analytics 87
5.9.2 COMPLEMENTARY TECHNOLOGIES 87
5.9.2.1 Cloud computing 87
5.9.2.2 Big data analytics 87
5.10 INDUSTRY TRENDS 88
5.10.1 EVOLUTION OF AI IN BIOTECHNOLOGY 88
5.10.2 COMPUTER-AIDED DRUG DESIGN AND AI 89
5.11 PRICING ANALYSIS 89
5.11.1 INDICATIVE PRICING ANALYSIS, BY DRUG DISCOVERY PROCESS 90
5.11.2 AVERAGE SELLING PRICE TREND, BY REGION 90
5.12 KEY CONFERENCES & EVENTS, 2024–2025 91
5.13 KEY STAKEHOLDERS & BUYING CRITERIA 92
5.13.1 BUYING CRITERIA 93
5.14 TRENDS & DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESSES 94
5.15 END-USER ANALYSIS 95
5.15.1 UNMET NEEDS 95
5.15.2 END-USER EXPECTATIONS 96
5.16 INVESTMENT & FUNDING SCENARIO 96
5.17 IMPACT OF AI/GEN AI ON AI IN BIOTECHNOLOGY MARKET 97
5.17.1 KEY USE CASES 98
5.17.2 CASE STUDIES OF AI/GENERATIVE AI IMPLEMENTATION 98
5.17.2.1 Case study: Accelerated biomarker discovery and clinical trial optimization 98
5.17.3 IMPACT OF AI/GEN AI ON INTERCONNECTED AND ADJACENT ECOSYSTEMS 99
5.17.3.1 Drug discovery and development market 99
5.17.3.2 Genomics and bioinformatics market 99
5.17.3.3 Medical imaging & diagnostics market 100
5.17.4 USER READINESS & IMPACT ASSESSMENT 100
5.17.4.1 User readiness 100
5.17.4.1.1 Pharmaceutical companies 100
5.17.4.1.2 Biotechnology companies 100
 
5.17.4.2 Impact assessment 101
5.17.4.2.1 User A: Pharmaceutical companies 101
5.17.4.2.1.1 Implementation 101
5.17.4.2.1.2 Impact 101
5.17.4.2.2 User B: Biotechnology companies 101
5.17.4.2.2.1 Implementation 101
5.17.4.2.2.2 Impact 101
6 AI IN BIOTECHNOLOGY MARKET, BY OFFERING 102
6.1 INTRODUCTION 103
6.2 END-TO-END SOLUTIONS 103
6.2.1 GROWING USE OF ADVANCED ALGORITHMS TO IMPROVE PRECISION AND EFFICIENCY TO BOOST MARKET GROWTH 103
6.3 NICHE SOLUTIONS 104
6.3.1 ABILITY OF NICHE SOLUTIONS TO ADDRESS SPECIFIC CHALLENGES WITHIN DRUG DISCOVERY TO SUPPORT ADOPTION 104
6.4 TECHNOLOGIES 105
6.4.1 ABILITY OF TECHNOLOGIES TO ENHANCE DRUG DISCOVERY, PERSONALIZED MEDICINE, AND DATA ANALYTICS TO FUEL GROWTH 105
6.5 SERVICES 106
6.5.1 CONSULTING SERVICES 107
6.5.1.1 Increasing efficiency of research processes and cost savings to boost adoption of consulting services 107
6.5.2 IMPLEMENTATION SERVICES & ONGOING IT SUPPORT 108
6.5.2.1 Increasing precision and efficiency in IT support services to boost demand 108
6.5.3 TRAINING & EDUCATION SERVICES 108
6.5.3.1 Need for skilled talent to drive market growth 108
6.5.4 POST-SALES & MAINTENANCE SERVICES 109
6.5.4.1 Complexity of AI systems and need for improvement in AI algorithms to boost market 109
7 AI IN BIOTECHNOLOGY MARKET, BY FUNCTION 111
7.1 INTRODUCTION 112
7.2 RESEARCH & DEVELOPMENT 112
7.2.1 DRUG DISCOVERY 114
7.2.1.1 Molecular design & optimization 115
7.2.1.1.1 Increased efficiency in drug discovery with molecular design & optimization to drive market growth 115
7.2.1.2 Biomarker discovery 116
7.2.1.2.1 Ability to analyze large data sets with AI-enabled biomarker discovery to boost demand for 116
 
7.2.1.3 Structure-activity relationship (SAR) modeling 117
7.2.1.3.1 Improved data analysis, predictive modeling, and compound optimization for drug candidates with SAR to fuel growth 117
7.2.2 CLINICAL DEVELOPMENT 117
7.2.2.1 Trial design 119
7.2.2.1.1 Ability of AI to improve trial design through simulations and patient stratification to favor market 119
7.2.2.2 Site selection 119
7.2.2.2.1 Optimized process of selecting clinical trial sites to fuel growth 119
7.2.2.3 Recruitment 120
7.2.2.3.1 Enhanced process of selecting and enrolling participants for clinical trials to drive demand 120
7.2.2.4 Clinical data assessment 121
7.2.2.4.1 Ability of clinical data assessment to enhance efficiency and accuracy of data interpretation to propel market 121
7.2.2.5 Predictive toxicity & risk monitoring 121
7.2.2.5.1 Ability of data integration and predictive modeling to create comprehensive risk profiles for drug candidates to support market 121
7.2.2.6 Monitoring & drug adherence 122
7.2.2.6.1 Enhanced patient compliance with monitoring & drug adherence to drive market growth 122
7.2.2.7 Real-world evidence (RWE) analysis 123
7.2.2.7.1 Enhanced safety monitoring & economic evaluation with RWE analysis to propel growth 123
7.3 REGULATORY COMPLIANCE 123
7.3.1 ABILITY OF AI TO ENSURE REGULATORY COMPLIANCE IN CLINICAL TRIALS TO SUPPORT GROWTH 123
7.4 MANUFACTURING & SUPPLY CHAIN 124
7.4.1 SUPPLY CHAIN PLANNING 126
7.4.1.1 Increasing demand for real-time data analytics to accelerate market growth 126
7.4.2 INVENTORY MANAGEMENT 126
7.4.2.1 Automating stock tracking and replenishment with advanced analytics to fuel growth 126
7.4.3 LOGISTICS OPTIMIZATION 127
7.4.3.1 Ability of AI to drive collaboration and transparency in biotechnology logistics to aid growth 127
7.4.4 DEMAND FORECASTING 128
7.4.4.1 Ability to integrate data for reliable demand forecasts to fuel growth 128
7.4.5 PREDICTIVE MAINTENANCE 128
7.4.5.1 Boosting equipment reliability with AI-powered predictive maintenance to drive demand 128
7.4.6 OTHER MANUFACTURING & SUPPLY CHAIN FUNCTIONS 129
7.5 LAUNCH & COMMERCIAL 130
7.5.1 LAUNCH COORDINATION 131
7.5.1.1 Growing product launch success rates through predictive analytics to boost adoption 131
7.5.2 PATIENT ENGAGEMENT 131
7.5.2.1 Advantages such as real-time patient feedback for better health outcomes to support growth 131
7.5.3 MARKETING OPERATIONS 132
7.5.3.1 Enhanced marketing performance with AI to boost market 132
7.5.4 PREDICTIVE PRICING 133
7.5.4.1 Ability of AI to enhance pricing accuracy to drive adoption 133
7.6 POST-MARKETING SURVEILLANCE & PATIENT SUPPORT 133
7.6.1 MEDICATION ADHERENCE 134
7.6.1.1 Growing demand for personalized healthcare to drive market 134
7.6.2 ADVERSE EVENT REPORTING 135
7.6.2.1 Advantages such as faster post-market surveillance and enhanced drug safety to drive demand 135
7.6.3 PATIENT MONITORING 136
7.6.3.1 Rise of remote healthcare solutions to boost demand 136
7.6.4 COMPLIANCE MONITORING 136
7.6.4.1 Increasing complexity of regulatory requirements to drive adoption 136
7.6.5 PATIENT SUPPORT PROGRAMS 137
7.6.5.1 Growing interest in patient-centered care to support growth 137
7.7 CORPORATE 138
7.7.1 RISK MANAGEMENT 139
7.7.1.1 Rising expenditure for drug development to support growth 139
7.7.2 COMPLIANCE MONITORING 139
7.7.2.1 Strict guidelines from bodies to aid growth 139
7.7.3 SALES FORCE OPTIMIZATION 140
7.7.3.1 Need for data-driven decision-making to boost adoption of sales force optimization 140
7.7.4 OTHER CORPORATE FUNCTIONS 141
8 AI IN BIOTECHNOLOGY MARKET, BY DEPLOYMENT MODE 142
8.1 INTRODUCTION 143
8.2 CLOUD-BASED SOLUTIONS 143
8.2.1 PUBLIC CLOUD 144
8.2.1.1 Need to reduce dependency on expensive on-premise infrastructure to boost demand 144
8.2.2 PRIVATE CLOUD 145
8.2.2.1 Need for enhanced security and data protection to drive market growth 145
8.2.3 MULTI-CLOUD 146
8.2.3.1 Enhanced flexibility & cost optimization to support market growth 146
8.2.4 HYBRID CLOUD 147
8.2.4.1 Cost efficiency and flexibility of hybrid cloud to fuel growth 147
8.3 ON-PREMISE SOLUTIONS 148
8.3.1 ADVANTAGES SUCH AS DATA SECURITY AND PRIVACY AND COMPLIANCE WITH REGULATIONS TO FAVOR GROWTH 148
9 AI IN BIOTECHNOLOGY MARKET, BY END USER 150
9.1 INTRODUCTION 151
9.2 PHARMACEUTICAL COMPANIES 151
9.2.1 INNOVATION AND EFFICIENCY ASSOCIATED WITH AI INTEGRATION IN DRUG DISCOVERY & DEVELOPMENT TO BOOST ADOPTION 151
9.3 BIOTECHNOLOGY COMPANIES 152
9.3.1 ABILITY OF AI-DRIVEN INNOVATIONS TO ACCELERATE PERSONALIZED MEDICINE AND DRUG DISCOVERY TO SUPPORT GROWTH 152
9.4 RESEARCH INSTITUTES & LABS 153
9.4.1 STRATEGIC INVESTMENTS AND COLLABORATIONS TO PROPEL AI ADVANCEMENTS IN RESEARCH INSTITUTES AND LABS 153
9.5 HEALTHCARE PROVIDERS 154
9.5.1 IMPROVED PATIENT OUTCOMES TO SUPPORT ADOPTION 154
9.6 CONTRACT RESEARCH ORGANIZATIONS (CROS) 155
9.6.1 ABILITY OF AI TECHNOLOGIES TO ACCELERATE CLINICAL TRIALS AND IMPROVE PATIENT RECRUITMENT TO FUEL GROWTH 155
10 AI IN BIOTECHNOLOGY MARKET, BY REGION 157
10.1 INTRODUCTION 158
10.2 NORTH AMERICA 159
10.2.1 MACROECONOMIC OUTLOOK FOR NORTH AMERICA 165
10.2.2 US 165
10.2.2.1 Increasing investments and partnerships to drive market 165
10.2.3 CANADA 171
10.2.3.1 Availability of advanced facilities and shorter approval times for drug candidates to drive market 171
10.3 EUROPE 177
10.3.1 MACROECONOMIC OUTLOOK FOR EUROPE 184
10.3.2 GERMANY 184
10.3.2.1 Increased funding in start-ups to drive uptake of AI in biotechnology 184
10.3.3 UK 190
10.3.3.1 Increasing investments and government fund allocations to drive market 190
10.3.4 FRANCE 195
10.3.4.1 Government initiatives in France to support market growth 195
10.3.5 ITALY 201
10.3.5.1 Growing investments to create opportunities for market growth 201
 
10.3.6 SPAIN 207
10.3.6.1 Increasing need for personalized medicine and data-driven healthcare to increase adoption rate in market 207
10.3.7 REST OF EUROPE 212
10.4 ASIA PACIFIC 218
10.4.1 MACROECONOMIC OUTLOOK FOR ASIA PACIFIC 226
10.4.2 JAPAN 226
10.4.2.1 Accelerating AI-driven drug discovery and biotechnology innovation to drive market in Japan 226
10.4.3 CHINA 232
10.4.3.1 Rising foreign investments to drive market in China 232
10.4.4 INDIA 238
10.4.4.1 Increasing number of start-ups and support from government to propel market 238
10.4.5 SOUTH KOREA 244
10.4.5.1 Significant advances in AI integration for R&D to fuel growth 244
10.4.6 REST OF ASIA PACIFIC 250
10.5 LATIN AMERICA 256
10.5.1 MACROECONOMIC OUTLOOK FOR LATIN AMERICA 262
10.5.2 BRAZIL 262
10.5.2.1 Funding of biotech companies to drive market in Brazil 262
10.5.3 MEXICO 268
10.5.3.1 Investment inflows and strengthening AI-related education to drive market in Mexico 268
10.5.4 REST OF LATIN AMERICA 274
10.6 MIDDLE EAST & AFRICA 280
10.6.1 MACROECONOMIC OUTLOOK FOR MIDDLE EAST & AFRICA 286
10.6.2 GCC COUNTRIES 286
10.6.2.1 Increase in healthcare investments to support market growth 286
10.6.3 REST OF MIDDLE EAST & AFRICA 293
11 COMPETITIVE LANDSCAPE 300
11.1 INTRODUCTION 300
11.2 KEY PLAYER STRATEGY/RIGHT TO WIN 300
11.3 REVENUE ANALYSIS, 2019–2023 302
11.4 MARKET SHARE ANALYSIS, 2023 303
11.4.1 RANKING OF KEY MARKET PLAYERS 306
11.5 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023 306
11.5.1 STARS 306
11.5.2 EMERGING LEADERS 306
11.5.3 PERVASIVE PLAYERS 307
11.5.4 PARTICIPANTS 307
 
11.5.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023 308
11.5.5.1 Company footprint 308
11.5.5.2 Component footprint 309
11.5.5.3 Application footprint 310
11.5.5.4 End-user footprint 311
11.5.5.5 Region footprint 312
11.6 COMPANY EVALUATION MATRIX: START-UPS/SMES, 2023 313
11.6.1 PROGRESSIVE COMPANIES 313
11.6.2 RESPONSIVE COMPANIES 313
11.6.3 DYNAMIC COMPANIES 313
11.6.4 STARTING BLOCKS 313
11.6.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023 315
11.7 COMPANY VALUATION & FINANCIAL METRICS 317
11.8 BRAND/PRODUCT COMPARISON 318
11.9 COMPETITIVE SCENARIO 319
11.9.1 PRODUCT LAUNCHES & UPGRADES 319
11.9.2 DEALS 320
11.9.3 EXPANSIONS 321
12 COMPANY PROFILES 322
12.1 KEY PLAYERS 322
12.1.1 NVIDIA CORPORATION 322
12.1.1.1 Business overview 322
12.1.1.2 Products offered 323
12.1.1.3 Recent developments 324
12.1.1.3.1 Product launches 324
12.1.1.3.2 Deals 324
12.1.1.4 MnM view 324
12.1.1.4.1 Right to win 324
12.1.1.4.2 Strategic choices 325
12.1.1.4.3 Weaknesses & competitive threats 325
12.1.2 ILLUMINA, INC. 326
12.1.2.1 Business overview 326
12.1.2.2 Products offered 327
12.1.2.3 Recent developments 328
12.1.2.3.1 Product launches 328
12.1.2.3.2 Deals 329
12.1.2.4 MnM view 330
12.1.2.4.1 Right to win 330
12.1.2.4.2 Strategic choices 330
12.1.2.4.3 Weaknesses & competitive threats 330
 
12.1.3 EXSCIENTIA 331
12.1.3.1 Business overview 331
12.1.3.2 Products offered 332
12.1.3.3 Recent developments 332
12.1.3.3.1 Product launches 332
12.1.3.3.2 Deals 332
12.1.3.3.3 Other developments 334
12.1.3.4 MnM view 335
12.1.3.4.1 Right to win 335
12.1.3.4.2 Strategic choices 335
12.1.3.4.3 Weaknesses & competitive threats 335
12.1.4 SCHRÖDINGER, INC. 336
12.1.4.1 Business overview 336
12.1.4.2 Products offered 337
12.1.4.3 Recent developments 338
12.1.4.3.1 Product upgrades 338
12.1.4.3.2 Deals 338
12.1.5 RECURSION PHARMACEUTICALS, INC. 340
12.1.5.1 Business overview 340
12.1.5.2 Products offered 341
12.1.5.3 Recent developments 341
12.1.5.3.1 Deals 341
12.1.5.3.2 Expansions 342
12.1.6 SOPHIA GENETICS 343
12.1.6.1 Business overview 343
12.1.6.2 Products offered 344
12.1.6.3 Recent developments 344
12.1.6.3.1 Product launches 344
12.1.6.3.2 Deals 345
12.1.7 PREDICTIVE ONCOLOGY 347
12.1.7.1 Business overview 347
12.1.7.2 Products offered 348
12.1.7.3 Recent developments 348
12.1.7.3.1 Product launches 348
12.1.7.3.2 Deals 348
12.1.8 BENEVOLENTAI 349
12.1.8.1 Business overview 349
12.1.8.2 Products offered 350
12.1.8.3 Recent developments 350
12.1.8.3.1 Deals 350
 
12.1.9 EUROFINS DISCOVERY 351
12.1.9.1 Business overview 351
12.1.9.2 Products offered 352
12.1.9.3 Recent developments 352
12.1.9.3.1 Product launches 352
12.1.9.3.2 Deals 352
12.1.9.3.3 Expansions 354
12.1.10 XTALPI INC. 355
12.1.10.1 Business overview 355
12.1.10.2 Products offered 356
12.1.10.3 Recent developments 356
12.1.10.3.1 Deals 356
12.1.11 DNANEXUS, INC. 358
12.1.11.1 Business overview 358
12.1.11.2 Products offered 358
12.1.11.3 Recent developments 359
12.1.11.3.1 Deals 359
12.1.11.3.2 Other developments 361
12.1.12 NUMEDII, INC. 362
12.1.12.1 Business overview 362
12.1.12.2 Products offered 362
12.1.13 BPGBIO, INC. 363
12.1.13.1 Business overview 363
12.1.13.2 Products offered 363
12.1.13.3 Recent developments 364
12.1.13.3.1 Deals 364
12.1.14 IKTOS. 365
12.1.14.1 Business overview 365
12.1.14.2 Products offered 365
12.1.14.3 Recent developments 365
12.1.14.3.1 Deals 365
12.1.15 INSILICO MEDICINE 366
12.1.15.1 Business overview 366
12.1.15.2 Products offered 366
12.1.16 LOGICA 367
12.1.16.1 Business overview 367
12.1.16.2 Products offered 367
12.1.17 AMERICAN CHEMICAL SOCIETY 368
12.1.17.1 Business overview 368
12.1.17.2 Products offered 368
 
12.1.18 AGANITHA AI INC. 369
12.1.18.1 Business overview 369
12.1.18.2 Products offered 369
12.1.18.3 Recent developments 370
12.1.18.3.1 Deals 370
12.2 START-UP/SME PLAYERS 371
12.2.1 VERISIM LIFE 371
12.2.2 VALO HEALTH 371
12.2.3 TEMPUS AI, INC. 372
12.2.4 LIFEBIT BIOTECH LTD. 373
12.2.5 GENOOX 373
12.2.6 DATA4CURE, INC. 374
12.2.7 DEEP GENOMICS 374
13 APPENDIX 375
13.1 DISCUSSION GUIDE 375
13.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 382
13.3 CUSTOMIZATION OPTIONS 384
13.4 RELATED REPORTS 384
13.5 AUTHOR DETAILS 385