Artificial Intelligence in Drug Discovery Market by Process (Target, Lead), Use Case (Design & Optimisation: Vaccine, Antibody; Disease understanding, PK/PD), Therapy (Cancer, CNS, CVS), Tool (ML:DL (CNN, GAN)), End User & Region - Global Forecast to 2029
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:
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:
“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%)
- NVIDIA Corporation (US)
- Exscientia (UK)
- Google (US)
- BenevolentAI (UK)
- Recursion (US)
- Insilico Medicine (US)
- Schrцdinger, Inc. (US)
- Microsoft (US)
- Atomwise Inc. (US)
- Illumina, Inc. (US)
- Numedii, Inc. (US)
- Xtalpi Inc. (US)
- Iktos (France)
- Tempus (US)
- DEEP GENOMICS (Canada)
- Verge Genomics (US)
- BenchSci (Canada)
- Insitro (US)
- Valo Health (US)
- BPGBio, Inc. (US)
- Merck KGaA (Germany)
- IQVIA (US)
- Tencent Holdings Limited (China)
- Predictive Oncology, Inc. (US)
- CytoReason (Israel)
- Owkin, Inc. (US)
- Cloud Pharmaceuticals (US)
- Evaxion Biotech (Denmark)
- Standigm (South Korea)
- BIOAGE (US)
- Envisagenics (US)
- Abcellera (US)
- Centella (India)
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), Schrцdinger, 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.
1 INTRODUCTION
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.3 STUDY SCOPE
1.3.1 SEGMENTS AND REGIONS CONSIDERED
1.3.2 INCLUSIONS AND EXCLUSIONS
1.3.3 YEARS CONSIDERED
1.3.4 CURRENCY CONSIDERED
1.4 MARKET STAKEHOLDERS
1.5 SUMMARY OF CHANGES
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.1.1 Key secondary sources
2.1.1.2 Key data from secondary sources
2.1.2 PRIMARY DATA
2.1.2.1 Key primary sources
2.1.2.2 Key objectives of primary research
2.1.2.3 Key data from primary sources
2.1.2.4 Key industry insights
2.1.2.5 Breakdown of primaries
2.2 RESEARCH DESIGN
2.3 MARKET SIZE ESTIMATION
2.3.1 SUPPLY-SIDE ANALYSIS (REVENUE SHARE ANALYSIS)
2.3.2 BOTTOM-UP APPROACH: END-USER ADOPTION
2.3.2.1 Top-down assessment of parent market
2.3.2.2 Company presentations and primary interviews
2.4 DATA TRIANGULATION
2.5 STUDY ASSUMPTIONS
2.5.1 MARKET SIZING ASSUMPTIONS
2.5.2 RESEARCH ASSUMPTIONS
2.6 RISK ASSESSMENT
2.7 RESEARCH LIMITATIONS
2.7.1 METHODOLOGY-RELATED LIMITATIONS
2.7.2 SCOPE-RELATED LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET OVERVIEW
4.2 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET,
BY END USER AND COUNTRY (2023)
4.3 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET: GEOGRAPHIC GROWTH OPPORTUNITIES
4.4 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET: REGIONAL MIX
4.5 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET: DEVELOPED VS.
EMERGING MARKETS
5 MARKET OVERVIEW
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Increasing number of cross-industry collaborations and partnerships
5.2.1.2 Rising need to reduce time and cost of drug discovery and development
5.2.1.3 Patent expiry of drugs and need for effective new leads
5.2.1.4 Growing utilization of AI to predict drug-target interactions for cancer therapy
5.2.1.5 Integration of AI-assisted multiomics in drug discovery
5.2.1.6 Growing focus on rare disease treatments for orphan drug development
5.2.2 RESTRAINTS
5.2.2.1 Shortage of AI workforce and ambiguous regulatory guidelines for medical software
5.2.3 OPPORTUNITIES
5.2.3.1 Leveraging AI for accelerated biotech drug discovery
5.2.3.2 Increased focus on drug discovery in emerging economies
5.2.3.3 Focus on developing human-aware AI systems
5.2.3.4 Growing use of AI in single-cell analysis
5.2.3.5 Easy identification of biomarker and disease subtypes from single-cell data
5.2.3.6 High demand for precision and personalized medicines
5.2.4 CHALLENGES
5.2.4.1 Limited availability of quality data sets
5.2.4.2 Lack of advanced AI tools and training data sets
5.2.4.3 Computational constraints of advanced AI models
5.2.4.4 Lack of high-quality data sets for model training
5.3 TRENDS/DISRUPTIONS IMPACTING CUSTOMER’S BUSINESS
5.4 INDUSTRY TRENDS
5.4.1 EVOLUTION OF AI IN DRUG DISCOVERY
5.4.2 COMPUTER-AIDED DRUG DESIGN AND ARTIFICIAL INTELLIGENCE
5.5 ECOSYSTEM ANALYSIS
5.6 SUPPLY CHAIN ANALYSIS
5.7 TECHNOLOGY ANALYSIS
5.7.1 KEY TECHNOLOGIES
5.7.1.1 Dry lab services
5.7.1.2 Wet lab services
5.7.1.2.1 Chemistry software and services
5.7.1.2.2 Biology software and services
5.7.1.2.2.1 Single-cell analysis
5.7.2 COMPLEMENTARY TECHNOLOGIES
5.7.2.1 High-performance computing
5.7.2.2 Next-generation sequencing
5.7.2.3 Real-world evidence/Real-world data
5.7.3 ADJACENT TECHNOLOGIES
5.7.3.1 Cloud computing
5.7.3.2 Blockchain technologies
5.7.3.3 Internet of things
5.8 REGULATORY LANDSCAPE
5.8.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
5.8.2 REGULATORY FRAMEWORK
5.9 PRICING ANALYSIS
5.9.1 INDICATIVE SELLING PRICE FOR DRUG DISCOVERY SOFTWARE AND SERVICES, BY REGION
5.9.2 INDICATIVE PRICING ANALYSIS, BY PROCESS
5.10 PORTER’S FIVE FORCES ANALYSIS
5.10.1 INTENSITY OF COMPETITIVE RIVALRY
5.10.2 BARGAINING POWER OF BUYERS
5.10.3 THREAT OF SUBSTITUTES
5.10.4 THREAT OF NEW ENTRANTS
5.10.5 BARGAINING POWER OF SUPPLIERS
5.11 KEY STAKEHOLDERS AND BUYING CRITERIA
5.11.1 KEY STAKEHOLDERS IN BUYING PROCESS
5.11.2 KEY BUYING CRITERIA
5.12 PATENT ANALYSIS
5.12.1 PATENT PUBLICATION TRENDS
5.12.2 JURISDICTION ANALYSIS: TOP APPLICANT COUNTRIES FOR ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY
5.12.3 MAJOR PATENTS IN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET
5.13 UNMET NEEDS AND KEY PAIN POINTS
5.13.1 UNMET NEEDS
5.13.2 SINGLE-CELL ANALYSIS LANDSCAPE: KEY CHALLENGES AND PAIN POINTS IN DRUG DISCOVERY
5.13.3 END-USER EXPECTATIONS
5.14 KEY CONFERENCES & EVENTS, 2024–2025
5.15 CASE STUDY ANALYSIS
5.16 BUSINESS MODEL ANALYSIS
5.17 INVESTMENT AND FUNDING SCENARIO
5.18 IMPACT OF AI/GENERATIVE AI ON ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET
5.18.1 TOP USE CASES AND MARKET POTENTIAL
5.18.1.1 Key use cases
5.18.2 CASE STUDIES OF AI/GENERATIVE AI IMPLEMENTATION
5.18.2.1 Case study 1: Accelerated drug discovery with generative AI and streamlined workflows
5.18.2.2 Case study 2: Accelerating small-molecule drug discovery with generative AI
5.18.3 IMPACT OF AI/GENERATIVE AI ON INTERCONNECTED AND ADJACENT ECOSYSTEMS
5.18.3.1 AI in drug discovery market
5.18.3.2 Genomics and bioinformatics market
5.18.3.3 Life science analytics market
5.18.4 USER READINESS AND IMPACT ASSESSMENT
5.18.4.1 User readiness
5.18.4.1.1 Pharmaceutical companies
5.18.4.1.2 Biotechnology companies
5.18.4.2 Impact assessment
5.18.4.2.1 User A: Pharmaceutical Companies
5.18.4.2.1.1 Implementation
5.18.4.2.1.2 Impact
5.18.4.2.2 User B: Biotechnology companies
5.18.4.2.2.1 Implementation
5.18.4.2.2.2 Impact
5.19 ARTIFICIAL INTELLIGENCE-DERIVED CLINICAL ASSETS
6 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY PROCESS
6.1 INTRODUCTION
6.2 TARGET IDENTIFICATION & SELECTION
6.2.1 INCREASED DEMAND FOR PERSONALIZED MEDICINES AND HIGH INVESTMENT IN PHARMACEUTICAL R&D TO FUEL MARKET GROWTH
6.3 TARGET VALIDATION
6.3.1 RISING EMPHASIS ON AVOIDING LATE-STAGE FAILURE IN DRUG DISCOVERY TO BOOST MARKET GROWTH
6.4 HIT IDENTIFICATION & PRIORITIZATION
6.4.1 NEED FOR LARGE-SCALE DATA ANALYSIS TO DRIVE ADOPTION
6.5 HIT-TO-LEAD IDENTIFICATION/LEAD GENERATION
6.5.1 HIT-TO-LEAD IDENTIFICATION/LEAD GENERATION TO IMPROVE NEW DRUG POTENCY WITHOUT INCREASING LIPOPHILICITY
6.6 LEAD OPTIMIZATION
6.6.1 NEED FOR TRANSPARENT PRESENTATION AND ANALYSIS TO BOOST MARKET GROWTH
6.7 CANDIDATE SELECTION & VALIDATION
6.7.1 HIGH POSSIBILITY OF CLINICAL DRUG FAILURE TO SPUR ADOPTION OF CANDIDATE VALIDATION SERVICES
7 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY USE CASE
7.1 INTRODUCTION
7.2 UNDERSTANDING DISEASES
7.2.1 INCREASED FOCUS ON UNDERSTANDING DISEASES TO IMPROVE RESEARCH DATA QUALITY AND QUANTITY
7.3 DRUG REPURPOSING
7.3.1 INCREASING NEED FOR COST-EFFECTIVE TREATMENTS AND RISING AVAILABILITY OF BIOMEDICAL DATA TO AID MARKET GROWTH
7.4 DE NOVO DRUG DESIGN
7.4.1 SMALL-MOLECULE DESIGN
7.4.1.1 Increasing use of virtual screening and simulation techniques to drive growth
7.4.2 VACCINE DESIGN
7.4.2.1 Availability of well-validated AI tools to boost market growth
7.4.3 ANTIBODY & OTHER BIOLOGICS DESIGN
7.4.3.1 Advancements in protein modeling to propel segment growth
7.5 DRUG OPTIMIZATION
7.5.1 SMALL-MOLECULE OPTIMIZATION
7.5.1.1 Leveraging generative models for identifying potential modifications in molecular structures to aid market growth
7.5.2 VACCINE OPTIMIZATION
7.5.2.1 Effectively predicting vaccine formulations and adjusting delivery vectors to drive growth
7.5.3 ANTIBODY & OTHER BIOLOGICS OPTIMIZATION
7.5.3.1 Increasing adoption of machine learning for predicting protein structures to augment segment growth
7.6 SAFETY & TOXICITY
7.6.1 FOCUS ON ADVANCED OFF-TARGET EFFECT PREDICTION, PK/PD SIMULATION, AND QSP MODELING TO DRIVE MARKET
8 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET,
BY THERAPEUTIC AREA
8.1 INTRODUCTION
8.2 ONCOLOGY
8.2.1 HIGH PREVALENCE OF CANCER AND SHORTAGE OF EFFECTIVE ONCOLOGY DRUGS TO PROPEL MARKET GROWTH
8.3 INFECTIOUS DISEASES
8.3.1 RISING EPIDEMIC OUTBREAKS TO BOOST DRUG DISCOVERY ACTIVITY
8.4 NEUROLOGY
8.4.1 COMPLEX DISEASE DIAGNOSIS AND TREATMENT TO INCREASE ADOPTION OF ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY
8.5 METABOLIC DISEASES
8.5.1 ROLE OF ARTIFICIAL INTELLIGENCE IN UNCOVERING SMALL-MOLECULE THERAPIES TO DRIVE ADOPTION
8.6 CARDIOVASCULAR DISEASES
8.6.1 SEDENTARY LIFESTYLES AND HIGH PREVALENCE OF OBESITY TO INCREASE NOVEL DRUG DEVELOPMENT FOR CARDIAC DISEASES
8.7 IMMUNOLOGY
8.7.1 GROWING DRUG PIPELINE FOR IMMUNOLOGICAL DISORDERS TO FAVOR MARKET GROWTH
8.8 MENTAL HEALTH DISORDERS
8.8.1 INCREASED OCCURRENCE OF MENTAL HEALTH DISEASES IN DEVELOPED ECONOMIES TO SPUR MARKET GROWTH
8.9 OTHER THERAPEUTIC AREAS
9 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY PLAYER TYPE
9.1 INTRODUCTION
9.2 END-TO-END SOLUTION PROVIDERS
9.2.1 END-TO-END SOLUTION PROVIDERS TO REDUCE NEED FOR MULTIPLE VENDORS AND ACCELERATE WORKFLOWS
9.3 NICHE/POINT SOLUTION PROVIDERS
9.3.1 ACCURATE, COST-EFFECTIVE, AND LESS TIME CONSUMPTION TO PROPEL MARKET GROWTH
9.4 AI TECHNOLOGY PROVIDERS
9.4.1 SPECIALIZED AI CAPABILITIES WITH FULL-SERVICE MANAGEMENT TO SUPPORT MARKET GROWTH
9.5 BUSINESS PROCESS SERVICE PROVIDERS
9.5.1 BETTER ACCESSIBILITY OF HIGH-QUALITY TOOLS AND LOWER DRUG DEVELOPMENT COSTS TO AID MARKET GROWTH
10 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY AI TOOL
10.1 INTRODUCTION
10.2 MACHINE LEARNING
10.2.1 DEEP LEARNING
10.2.1.1 Reduced number of errors and consistent management of data to augment market growth
10.2.1.2 Convolutional neural networks
10.2.1.3 Recurrent neural networks
10.2.1.4 Generative adversarial networks
10.2.1.5 Graph neural networks
10.2.1.6 Other deep learning technologies
10.2.2 SUPERVISED LEARNING
10.2.2.1 Supervised learning to predict drug repositioning and manage high-dimensional datasets
10.2.3 REINFORCEMENT LEARNING
10.2.3.1 Need to accelerate new molecules and maximize performance to augment segment growth
10.2.4 UNSUPERVISED LEARNING
10.2.4.1 Unsupervised learning to perform complex tasks, uncover potential drug candidates, and optimize lead compounds
10.2.5 OTHER MACHINE LEARNING TECHNOLOGIES
10.3 NATURAL LANGUAGE PROCESSING
10.3.1 NATURAL LANGUAGE PROCESSING TO IDENTIFY INFORMATION WITHIN UNSTRUCTURED DATA AND ACCELERATE DRUG DISCOVERY
10.4 CONTEXT-AWARE PROCESSING & COMPUTING
10.4.1 CONTEXT-AWARE COMPUTING TO IMPROVE PREDICTIONS OF PATIENT-SPECIFIC DRUG RESPONSES AND OPTIMIZE THERAPEUTIC INTERVENTIONS
10.5 COMPUTER VISION
10.5.1 COMPUTER VISION TO ENHANCE DRUG DISCOVERY THROUGH ADVANCED IMAGE PROCESSING
10.6 IMAGE ANALYSIS
10.6.1 BETTER DRUG DISCOVERY THROUGH IMAGE PROCESSING TECHNIQUES TO SUPPORT MARKET GROWTH
11 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY DEPLOYMENT
11.1 INTRODUCTION
11.2 ON-PREMISES DEPLOYMENT
11.2.1 PROVISION OF MULTI-VENDOR ARCHITECTURE AND SECURITY BENEFITS TO DRIVE MARKET
11.3 CLOUD-BASED DEPLOYMENT
11.3.1 FOCUS ON RESEARCH COLLABORATIONS AND ELIMINATION OF SOFTWARE AND HARDWARE PURCHASING COSTS TO DRIVE MARKET
11.4 SAAS-BASED DEPLOYMENT
11.4.1 LOWER COSTS, BETTER SECURITY, AND EASIER ACCESS TO AUGMENT MARKET GROWTH
12 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY END USER
12.1 INTRODUCTION
12.2 PHARMACEUTICAL & BIOTECHNOLOGY COMPANIES
12.2.1 RISING DEMAND FOR COST-EFFECTIVE DRUG DEVELOPMENT TO PROPEL MARKET GROWTH
12.3 CONTRACT RESEARCH ORGANIZATIONS
12.3.1 RISING NEED FOR OUTSOURCING IN PHARMACEUTICAL & BIOTECHNOLOGY INDUSTRIES TO AID MARKET GROWTH
12.4 RESEARCH CENTERS AND ACADEMIC & GOVERNMENT INSTITUTES
12.4.1 FOCUS ON DEVELOPING THERAPEUTIC STRATEGIES AND INNOVATIVE APPROACHES IN DRUG DISCOVERY TO AID MARKET GROWTH
13 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY REGION
13.1 INTRODUCTION
13.2 NORTH AMERICA
13.2.1 MACROECONOMIC OUTLOOK FOR NORTH AMERICA
13.2.2 US
13.2.2.1 US to dominate North American market during study period
13.2.3 CANADA
13.2.3.1 Emergence of new AI-based startups and high health expenditure to support market growth
13.3 EUROPE
13.3.1 MACROECONOMIC OUTLOOK FOR EUROPE
13.3.2 UK
13.3.2.1 Favorable government R&D funding to augment market growth
13.3.3 GERMANY
13.3.3.1 Presence of advanced medical infrastructure and high focus on personalized medicines to drive market
13.3.4 FRANCE
13.3.4.1 Strong government support and favorable strategies to propel market growth
13.3.5 ITALY
13.3.5.1 Advanced pharmaceutical industry and increased focus on life science R&D to fuel market growth
13.3.6 SPAIN
13.3.6.1 Favorable government initiatives and high investments by pharmaceutical companies to boost market growth
13.3.7 REST OF EUROPE
13.4 ASIA PACIFIC
13.4.1 MACROECONOMIC OUTLOOK FOR ASIA PACIFIC
13.4.2 JAPAN
13.4.2.1 High geriatric population and advanced pharmaceutical research to boost market growth
13.4.3 CHINA
13.4.3.1 Increasing demand for generics and rising government investments to propel market growth
13.4.4 INDIA
13.4.4.1 Developed IT infrastructure and favorable government initiatives to spur market growth
13.4.5 REST OF ASIA PACIFIC
13.5 LATIN AMERICA
13.5.1 MACROECONOMIC OUTLOOK FOR LATIN AMERICA
13.5.2 BRAZIL
13.5.2.1 Growing biotechnology sector and increasing governmental initiatives to boost market growth
13.5.3 MEXICO
13.5.3.1 Favorable government initiatives and high investments by pharmaceutical companies to support market growth
13.5.4 REST OF LATIN AMERICA
13.6 MIDDLE EAST & AFRICA
13.6.1 MACROECONOMIC OUTLOOK FOR MIDDLE EAST & AFRICA
13.6.2 GCC COUNTRIES
13.6.2.1 Increasing emphasis on personalized medicines and developing healthcare infrastructure to drive market
13.6.3 REST OF MIDDLE EAST & AFRICA
14 COMPETITIVE LANDSCAPE
14.1 INTRODUCTION
14.2 KEY PLAYER STRATEGY/RIGHT TO WIN
14.2.1 OVERVIEW OF STRATEGIES ADOPTED BY KEY PLAYERS IN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET
14.3 REVENUE ANALYSIS, 2019–2023
14.4 MARKET SHARE ANALYSIS, 2023
14.4.1 RANKING OF KEY MARKET PLAYERS
14.5 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
14.5.1 STARS
14.5.2 EMERGING LEADERS
14.5.3 PERVASIVE PLAYERS
14.5.4 PARTICIPANTS
14.5.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023
14.5.5.1 Company footprint
14.5.5.2 Use case footprint
14.5.5.3 Process footprint
14.5.5.4 Therapeutic area footprint
14.5.5.5 Player type footprint
14.5.5.6 Deployment mode footprint
14.5.5.7 Region footprint
14.6 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
14.6.1 PROGRESSIVE COMPANIES
14.6.2 RESPONSIVE COMPANIES
14.6.3 DYNAMIC COMPANIES
14.6.4 STARTING BLOCKS
14.6.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023
14.7 COMPANY VALUATION AND FINANCIAL METRICS
14.7.1 FINANCIAL METRICS
14.7.2 COMPANY VALUATION
14.8 BRAND/PRODUCT COMPARISON
14.9 COMPETITIVE SCENARIO
14.9.1 PRODUCT AND SOLUTION LAUNCHES
14.9.2 DEALS
14.9.3 EXPANSIONS
14.9.4 OTHER DEVELOPMENTS
15 COMPANY PROFILES
15.1 KEY PLAYERS
15.1.1 NVIDIA CORPORATION
15.1.1.1 Business overview
15.1.1.2 Products/Services/Solutions offered
15.1.1.3 Recent developments
15.1.1.3.1 Product and service launches
15.1.1.3.2 Deals
15.1.1.4 MnM view
15.1.1.4.1 Right to win
15.1.1.4.2 Strategic choices
15.1.1.4.3 Weaknesses and competitive threats
15.1.2 EXSCIENTIA
15.1.2.1 Business overview
15.1.2.2 Products/Services/Solutions offered
15.1.2.3 Recent developments
15.1.2.3.1 Solution launches
15.1.2.3.2 Deals
15.1.2.3.3 Expansions
15.1.2.3.4 Other developments
15.1.2.4 MnM view
15.1.2.4.1 Right to win
15.1.2.4.2 Strategic choices
15.1.2.4.3 Weaknesses and competitive threats
15.1.3 GOOGLE
15.1.3.1 Business overview
15.1.3.2 Products/Services/Solutions offered
15.1.3.3 Recent developments
15.1.3.3.1 Solution launches
15.1.3.3.2 Deals
15.1.3.3.3 Expansions
15.1.3.4 MnM view
15.1.3.4.1 Right to win
15.1.3.4.2 Strategic choices
15.1.3.4.3 Weaknesses and competitive threats
15.1.4 RECURSION
15.1.4.1 Business overview
15.1.4.2 Products/Services/Solutions offered
15.1.4.3 Recent developments
15.1.4.3.1 Solution launches
15.1.4.3.2 Deals
15.1.4.3.3 Expansions
15.1.4.4 MnM view
15.1.4.4.1 Right to win
15.1.4.4.2 Strategic choices made
15.1.4.4.3 Weaknesses and competitive threats
15.1.5 INSILICO MEDICINE
15.1.5.1 Business overview
15.1.5.2 Products/Services/Solutions offered
15.1.5.3 Recent developments
15.1.5.3.1 Product and solution launches and developments
15.1.5.3.2 Deals
15.1.5.3.3 Other developments
15.1.5.4 MnM view
15.1.5.4.1 Right to win
15.1.5.4.2 Strategic choices
15.1.5.4.3 Weaknesses and competitive threats
15.1.6 SCHRЦDINGER, INC.
15.1.6.1 Business overview
15.1.6.2 Products/Services/Solutions offered
15.1.6.3 Recent developments
15.1.6.3.1 Deals
15.1.6.3.2 Other developments
15.1.7 BENEVOLENTAI
15.1.7.1 Business overview
15.1.7.2 Products/Services/Solutions offered
15.1.7.3 Recent developments
15.1.7.3.1 Deals
15.1.8 MICROSOFT CORPORATION
15.1.8.1 Business overview
15.1.8.2 Products/Services/Solutions offered
15.1.8.3 Recent developments
15.1.8.3.1 Deals
15.1.9 ATOMWISE INC.
15.1.9.1 Business overview
15.1.9.2 Products/Services/Solutions offered
15.1.9.3 Recent developments
15.1.9.3.1 Deals
15.1.10 ILLUMINA, INC.
15.1.10.1 Business overview
15.1.10.2 Products/Services/Solutions offered
15.1.10.3 Recent developments
15.1.10.3.1 Solution launches
15.1.10.3.2 Deals
15.1.11 NUMEDII, INC.
15.1.11.1 Business overview
15.1.11.2 Products/Services/Solutions offered
15.1.12 XTALPI INC.
15.1.12.1 Business overview
15.1.12.2 Products/Services/Solutions offered
15.1.12.3 Recent developments
15.1.12.3.1 Deals
15.1.13 IKTOS
15.1.13.1 Business overview
15.1.13.2 Products/Services/Solutions offered
15.1.13.3 Recent developments
15.1.13.3.1 Deals
15.1.13.3.2 Other developments
15.1.14 TEMPUS
15.1.14.1 Business overview
15.1.14.2 Products/Services/Solutions offered
15.1.14.3 Recent developments
15.1.14.3.1 Solution launches
15.1.14.3.2 Deals
15.1.14.3.3 Expansions
15.1.14.3.4 Other developments
15.1.15 DEEP GENOMICS
15.1.15.1 Business overview
15.1.15.2 Products/Services/Solutions offered
15.1.15.3 Recent developments
15.1.15.3.1 Solution launches
15.1.15.3.2 Deals
15.1.15.3.3 Other developments
15.1.16 VERGE GENOMICS
15.1.16.1 Business overview
15.1.16.2 Products/Services/Solutions offered
15.1.16.3 Recent developments
15.1.16.3.1 Deals
15.1.17 BENCHSCI
15.1.17.1 Business overview
15.1.17.2 Products/Services/Solutions offered
15.1.17.3 Recent developments
15.1.17.3.1 Solution launches
15.1.17.3.2 Deals
15.1.17.3.3 Other developments
15.1.18 INSITRO
15.1.18.1 Business overview
15.1.18.2 Products/Services/Solutions offered
15.1.18.3 Recent developments
15.1.18.3.1 Deals
15.1.18.3.2 Other developments
15.1.19 VALO HEALTH
15.1.19.1 Business overview
15.1.19.2 Products/Services/Solutions offered
15.1.19.3 Recent developments
15.1.19.3.1 Deals
15.1.19.3.2 Other developments
15.1.20 BPGBIO, INC.
15.1.20.1 Business overview
15.1.20.2 Products/Services/Solutions offered
15.1.20.3 Recent developments
15.1.20.3.1 Deals
15.1.21 MERCK KGAA
15.1.21.1 Business overview
15.1.21.2 Products/Services/Solutions offered
15.1.21.3 Recent developments
15.1.21.3.1 Solution launches
15.1.21.3.2 Deals
15.1.21.3.3 Expansions
15.1.21.3.4 Other developments
15.2 OTHER PLAYERS
15.2.1 PREDICTIVE ONCOLOGY
15.2.2 IQVIA INC.
15.2.3 TENCENT HOLDINGS LIMITED
15.2.4 CYTOREASON LTD.
15.2.5 OWKIN, INC.
15.2.6 CLOUD PHARMACEUTICALS
15.2.7 EVAXION BIOTECH A/S
15.2.8 STANDIGM INC.
15.2.9 BIOAGE LABS
15.2.10 ENVISAGENICS
15.2.11 ABCELLERA
15.2.12 CENTELLA
16 APPENDIX
16.1 DISCUSSION GUIDE
16.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
16.3 CUSTOMIZATION OPTIONS
16.4 RELATED REPORTS
16.5 AUTHOR DETAILS
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.3 STUDY SCOPE
1.3.1 SEGMENTS AND REGIONS CONSIDERED
1.3.2 INCLUSIONS AND EXCLUSIONS
1.3.3 YEARS CONSIDERED
1.3.4 CURRENCY CONSIDERED
1.4 MARKET STAKEHOLDERS
1.5 SUMMARY OF CHANGES
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.1.1 Key secondary sources
2.1.1.2 Key data from secondary sources
2.1.2 PRIMARY DATA
2.1.2.1 Key primary sources
2.1.2.2 Key objectives of primary research
2.1.2.3 Key data from primary sources
2.1.2.4 Key industry insights
2.1.2.5 Breakdown of primaries
2.2 RESEARCH DESIGN
2.3 MARKET SIZE ESTIMATION
2.3.1 SUPPLY-SIDE ANALYSIS (REVENUE SHARE ANALYSIS)
2.3.2 BOTTOM-UP APPROACH: END-USER ADOPTION
2.3.2.1 Top-down assessment of parent market
2.3.2.2 Company presentations and primary interviews
2.4 DATA TRIANGULATION
2.5 STUDY ASSUMPTIONS
2.5.1 MARKET SIZING ASSUMPTIONS
2.5.2 RESEARCH ASSUMPTIONS
2.6 RISK ASSESSMENT
2.7 RESEARCH LIMITATIONS
2.7.1 METHODOLOGY-RELATED LIMITATIONS
2.7.2 SCOPE-RELATED LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET OVERVIEW
4.2 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET,
BY END USER AND COUNTRY (2023)
4.3 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET: GEOGRAPHIC GROWTH OPPORTUNITIES
4.4 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET: REGIONAL MIX
4.5 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET: DEVELOPED VS.
EMERGING MARKETS
5 MARKET OVERVIEW
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Increasing number of cross-industry collaborations and partnerships
5.2.1.2 Rising need to reduce time and cost of drug discovery and development
5.2.1.3 Patent expiry of drugs and need for effective new leads
5.2.1.4 Growing utilization of AI to predict drug-target interactions for cancer therapy
5.2.1.5 Integration of AI-assisted multiomics in drug discovery
5.2.1.6 Growing focus on rare disease treatments for orphan drug development
5.2.2 RESTRAINTS
5.2.2.1 Shortage of AI workforce and ambiguous regulatory guidelines for medical software
5.2.3 OPPORTUNITIES
5.2.3.1 Leveraging AI for accelerated biotech drug discovery
5.2.3.2 Increased focus on drug discovery in emerging economies
5.2.3.3 Focus on developing human-aware AI systems
5.2.3.4 Growing use of AI in single-cell analysis
5.2.3.5 Easy identification of biomarker and disease subtypes from single-cell data
5.2.3.6 High demand for precision and personalized medicines
5.2.4 CHALLENGES
5.2.4.1 Limited availability of quality data sets
5.2.4.2 Lack of advanced AI tools and training data sets
5.2.4.3 Computational constraints of advanced AI models
5.2.4.4 Lack of high-quality data sets for model training
5.3 TRENDS/DISRUPTIONS IMPACTING CUSTOMER’S BUSINESS
5.4 INDUSTRY TRENDS
5.4.1 EVOLUTION OF AI IN DRUG DISCOVERY
5.4.2 COMPUTER-AIDED DRUG DESIGN AND ARTIFICIAL INTELLIGENCE
5.5 ECOSYSTEM ANALYSIS
5.6 SUPPLY CHAIN ANALYSIS
5.7 TECHNOLOGY ANALYSIS
5.7.1 KEY TECHNOLOGIES
5.7.1.1 Dry lab services
5.7.1.2 Wet lab services
5.7.1.2.1 Chemistry software and services
5.7.1.2.2 Biology software and services
5.7.1.2.2.1 Single-cell analysis
5.7.2 COMPLEMENTARY TECHNOLOGIES
5.7.2.1 High-performance computing
5.7.2.2 Next-generation sequencing
5.7.2.3 Real-world evidence/Real-world data
5.7.3 ADJACENT TECHNOLOGIES
5.7.3.1 Cloud computing
5.7.3.2 Blockchain technologies
5.7.3.3 Internet of things
5.8 REGULATORY LANDSCAPE
5.8.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
5.8.2 REGULATORY FRAMEWORK
5.9 PRICING ANALYSIS
5.9.1 INDICATIVE SELLING PRICE FOR DRUG DISCOVERY SOFTWARE AND SERVICES, BY REGION
5.9.2 INDICATIVE PRICING ANALYSIS, BY PROCESS
5.10 PORTER’S FIVE FORCES ANALYSIS
5.10.1 INTENSITY OF COMPETITIVE RIVALRY
5.10.2 BARGAINING POWER OF BUYERS
5.10.3 THREAT OF SUBSTITUTES
5.10.4 THREAT OF NEW ENTRANTS
5.10.5 BARGAINING POWER OF SUPPLIERS
5.11 KEY STAKEHOLDERS AND BUYING CRITERIA
5.11.1 KEY STAKEHOLDERS IN BUYING PROCESS
5.11.2 KEY BUYING CRITERIA
5.12 PATENT ANALYSIS
5.12.1 PATENT PUBLICATION TRENDS
5.12.2 JURISDICTION ANALYSIS: TOP APPLICANT COUNTRIES FOR ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY
5.12.3 MAJOR PATENTS IN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET
5.13 UNMET NEEDS AND KEY PAIN POINTS
5.13.1 UNMET NEEDS
5.13.2 SINGLE-CELL ANALYSIS LANDSCAPE: KEY CHALLENGES AND PAIN POINTS IN DRUG DISCOVERY
5.13.3 END-USER EXPECTATIONS
5.14 KEY CONFERENCES & EVENTS, 2024–2025
5.15 CASE STUDY ANALYSIS
5.16 BUSINESS MODEL ANALYSIS
5.17 INVESTMENT AND FUNDING SCENARIO
5.18 IMPACT OF AI/GENERATIVE AI ON ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET
5.18.1 TOP USE CASES AND MARKET POTENTIAL
5.18.1.1 Key use cases
5.18.2 CASE STUDIES OF AI/GENERATIVE AI IMPLEMENTATION
5.18.2.1 Case study 1: Accelerated drug discovery with generative AI and streamlined workflows
5.18.2.2 Case study 2: Accelerating small-molecule drug discovery with generative AI
5.18.3 IMPACT OF AI/GENERATIVE AI ON INTERCONNECTED AND ADJACENT ECOSYSTEMS
5.18.3.1 AI in drug discovery market
5.18.3.2 Genomics and bioinformatics market
5.18.3.3 Life science analytics market
5.18.4 USER READINESS AND IMPACT ASSESSMENT
5.18.4.1 User readiness
5.18.4.1.1 Pharmaceutical companies
5.18.4.1.2 Biotechnology companies
5.18.4.2 Impact assessment
5.18.4.2.1 User A: Pharmaceutical Companies
5.18.4.2.1.1 Implementation
5.18.4.2.1.2 Impact
5.18.4.2.2 User B: Biotechnology companies
5.18.4.2.2.1 Implementation
5.18.4.2.2.2 Impact
5.19 ARTIFICIAL INTELLIGENCE-DERIVED CLINICAL ASSETS
6 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY PROCESS
6.1 INTRODUCTION
6.2 TARGET IDENTIFICATION & SELECTION
6.2.1 INCREASED DEMAND FOR PERSONALIZED MEDICINES AND HIGH INVESTMENT IN PHARMACEUTICAL R&D TO FUEL MARKET GROWTH
6.3 TARGET VALIDATION
6.3.1 RISING EMPHASIS ON AVOIDING LATE-STAGE FAILURE IN DRUG DISCOVERY TO BOOST MARKET GROWTH
6.4 HIT IDENTIFICATION & PRIORITIZATION
6.4.1 NEED FOR LARGE-SCALE DATA ANALYSIS TO DRIVE ADOPTION
6.5 HIT-TO-LEAD IDENTIFICATION/LEAD GENERATION
6.5.1 HIT-TO-LEAD IDENTIFICATION/LEAD GENERATION TO IMPROVE NEW DRUG POTENCY WITHOUT INCREASING LIPOPHILICITY
6.6 LEAD OPTIMIZATION
6.6.1 NEED FOR TRANSPARENT PRESENTATION AND ANALYSIS TO BOOST MARKET GROWTH
6.7 CANDIDATE SELECTION & VALIDATION
6.7.1 HIGH POSSIBILITY OF CLINICAL DRUG FAILURE TO SPUR ADOPTION OF CANDIDATE VALIDATION SERVICES
7 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY USE CASE
7.1 INTRODUCTION
7.2 UNDERSTANDING DISEASES
7.2.1 INCREASED FOCUS ON UNDERSTANDING DISEASES TO IMPROVE RESEARCH DATA QUALITY AND QUANTITY
7.3 DRUG REPURPOSING
7.3.1 INCREASING NEED FOR COST-EFFECTIVE TREATMENTS AND RISING AVAILABILITY OF BIOMEDICAL DATA TO AID MARKET GROWTH
7.4 DE NOVO DRUG DESIGN
7.4.1 SMALL-MOLECULE DESIGN
7.4.1.1 Increasing use of virtual screening and simulation techniques to drive growth
7.4.2 VACCINE DESIGN
7.4.2.1 Availability of well-validated AI tools to boost market growth
7.4.3 ANTIBODY & OTHER BIOLOGICS DESIGN
7.4.3.1 Advancements in protein modeling to propel segment growth
7.5 DRUG OPTIMIZATION
7.5.1 SMALL-MOLECULE OPTIMIZATION
7.5.1.1 Leveraging generative models for identifying potential modifications in molecular structures to aid market growth
7.5.2 VACCINE OPTIMIZATION
7.5.2.1 Effectively predicting vaccine formulations and adjusting delivery vectors to drive growth
7.5.3 ANTIBODY & OTHER BIOLOGICS OPTIMIZATION
7.5.3.1 Increasing adoption of machine learning for predicting protein structures to augment segment growth
7.6 SAFETY & TOXICITY
7.6.1 FOCUS ON ADVANCED OFF-TARGET EFFECT PREDICTION, PK/PD SIMULATION, AND QSP MODELING TO DRIVE MARKET
8 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET,
BY THERAPEUTIC AREA
8.1 INTRODUCTION
8.2 ONCOLOGY
8.2.1 HIGH PREVALENCE OF CANCER AND SHORTAGE OF EFFECTIVE ONCOLOGY DRUGS TO PROPEL MARKET GROWTH
8.3 INFECTIOUS DISEASES
8.3.1 RISING EPIDEMIC OUTBREAKS TO BOOST DRUG DISCOVERY ACTIVITY
8.4 NEUROLOGY
8.4.1 COMPLEX DISEASE DIAGNOSIS AND TREATMENT TO INCREASE ADOPTION OF ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY
8.5 METABOLIC DISEASES
8.5.1 ROLE OF ARTIFICIAL INTELLIGENCE IN UNCOVERING SMALL-MOLECULE THERAPIES TO DRIVE ADOPTION
8.6 CARDIOVASCULAR DISEASES
8.6.1 SEDENTARY LIFESTYLES AND HIGH PREVALENCE OF OBESITY TO INCREASE NOVEL DRUG DEVELOPMENT FOR CARDIAC DISEASES
8.7 IMMUNOLOGY
8.7.1 GROWING DRUG PIPELINE FOR IMMUNOLOGICAL DISORDERS TO FAVOR MARKET GROWTH
8.8 MENTAL HEALTH DISORDERS
8.8.1 INCREASED OCCURRENCE OF MENTAL HEALTH DISEASES IN DEVELOPED ECONOMIES TO SPUR MARKET GROWTH
8.9 OTHER THERAPEUTIC AREAS
9 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY PLAYER TYPE
9.1 INTRODUCTION
9.2 END-TO-END SOLUTION PROVIDERS
9.2.1 END-TO-END SOLUTION PROVIDERS TO REDUCE NEED FOR MULTIPLE VENDORS AND ACCELERATE WORKFLOWS
9.3 NICHE/POINT SOLUTION PROVIDERS
9.3.1 ACCURATE, COST-EFFECTIVE, AND LESS TIME CONSUMPTION TO PROPEL MARKET GROWTH
9.4 AI TECHNOLOGY PROVIDERS
9.4.1 SPECIALIZED AI CAPABILITIES WITH FULL-SERVICE MANAGEMENT TO SUPPORT MARKET GROWTH
9.5 BUSINESS PROCESS SERVICE PROVIDERS
9.5.1 BETTER ACCESSIBILITY OF HIGH-QUALITY TOOLS AND LOWER DRUG DEVELOPMENT COSTS TO AID MARKET GROWTH
10 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY AI TOOL
10.1 INTRODUCTION
10.2 MACHINE LEARNING
10.2.1 DEEP LEARNING
10.2.1.1 Reduced number of errors and consistent management of data to augment market growth
10.2.1.2 Convolutional neural networks
10.2.1.3 Recurrent neural networks
10.2.1.4 Generative adversarial networks
10.2.1.5 Graph neural networks
10.2.1.6 Other deep learning technologies
10.2.2 SUPERVISED LEARNING
10.2.2.1 Supervised learning to predict drug repositioning and manage high-dimensional datasets
10.2.3 REINFORCEMENT LEARNING
10.2.3.1 Need to accelerate new molecules and maximize performance to augment segment growth
10.2.4 UNSUPERVISED LEARNING
10.2.4.1 Unsupervised learning to perform complex tasks, uncover potential drug candidates, and optimize lead compounds
10.2.5 OTHER MACHINE LEARNING TECHNOLOGIES
10.3 NATURAL LANGUAGE PROCESSING
10.3.1 NATURAL LANGUAGE PROCESSING TO IDENTIFY INFORMATION WITHIN UNSTRUCTURED DATA AND ACCELERATE DRUG DISCOVERY
10.4 CONTEXT-AWARE PROCESSING & COMPUTING
10.4.1 CONTEXT-AWARE COMPUTING TO IMPROVE PREDICTIONS OF PATIENT-SPECIFIC DRUG RESPONSES AND OPTIMIZE THERAPEUTIC INTERVENTIONS
10.5 COMPUTER VISION
10.5.1 COMPUTER VISION TO ENHANCE DRUG DISCOVERY THROUGH ADVANCED IMAGE PROCESSING
10.6 IMAGE ANALYSIS
10.6.1 BETTER DRUG DISCOVERY THROUGH IMAGE PROCESSING TECHNIQUES TO SUPPORT MARKET GROWTH
11 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY DEPLOYMENT
11.1 INTRODUCTION
11.2 ON-PREMISES DEPLOYMENT
11.2.1 PROVISION OF MULTI-VENDOR ARCHITECTURE AND SECURITY BENEFITS TO DRIVE MARKET
11.3 CLOUD-BASED DEPLOYMENT
11.3.1 FOCUS ON RESEARCH COLLABORATIONS AND ELIMINATION OF SOFTWARE AND HARDWARE PURCHASING COSTS TO DRIVE MARKET
11.4 SAAS-BASED DEPLOYMENT
11.4.1 LOWER COSTS, BETTER SECURITY, AND EASIER ACCESS TO AUGMENT MARKET GROWTH
12 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY END USER
12.1 INTRODUCTION
12.2 PHARMACEUTICAL & BIOTECHNOLOGY COMPANIES
12.2.1 RISING DEMAND FOR COST-EFFECTIVE DRUG DEVELOPMENT TO PROPEL MARKET GROWTH
12.3 CONTRACT RESEARCH ORGANIZATIONS
12.3.1 RISING NEED FOR OUTSOURCING IN PHARMACEUTICAL & BIOTECHNOLOGY INDUSTRIES TO AID MARKET GROWTH
12.4 RESEARCH CENTERS AND ACADEMIC & GOVERNMENT INSTITUTES
12.4.1 FOCUS ON DEVELOPING THERAPEUTIC STRATEGIES AND INNOVATIVE APPROACHES IN DRUG DISCOVERY TO AID MARKET GROWTH
13 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY REGION
13.1 INTRODUCTION
13.2 NORTH AMERICA
13.2.1 MACROECONOMIC OUTLOOK FOR NORTH AMERICA
13.2.2 US
13.2.2.1 US to dominate North American market during study period
13.2.3 CANADA
13.2.3.1 Emergence of new AI-based startups and high health expenditure to support market growth
13.3 EUROPE
13.3.1 MACROECONOMIC OUTLOOK FOR EUROPE
13.3.2 UK
13.3.2.1 Favorable government R&D funding to augment market growth
13.3.3 GERMANY
13.3.3.1 Presence of advanced medical infrastructure and high focus on personalized medicines to drive market
13.3.4 FRANCE
13.3.4.1 Strong government support and favorable strategies to propel market growth
13.3.5 ITALY
13.3.5.1 Advanced pharmaceutical industry and increased focus on life science R&D to fuel market growth
13.3.6 SPAIN
13.3.6.1 Favorable government initiatives and high investments by pharmaceutical companies to boost market growth
13.3.7 REST OF EUROPE
13.4 ASIA PACIFIC
13.4.1 MACROECONOMIC OUTLOOK FOR ASIA PACIFIC
13.4.2 JAPAN
13.4.2.1 High geriatric population and advanced pharmaceutical research to boost market growth
13.4.3 CHINA
13.4.3.1 Increasing demand for generics and rising government investments to propel market growth
13.4.4 INDIA
13.4.4.1 Developed IT infrastructure and favorable government initiatives to spur market growth
13.4.5 REST OF ASIA PACIFIC
13.5 LATIN AMERICA
13.5.1 MACROECONOMIC OUTLOOK FOR LATIN AMERICA
13.5.2 BRAZIL
13.5.2.1 Growing biotechnology sector and increasing governmental initiatives to boost market growth
13.5.3 MEXICO
13.5.3.1 Favorable government initiatives and high investments by pharmaceutical companies to support market growth
13.5.4 REST OF LATIN AMERICA
13.6 MIDDLE EAST & AFRICA
13.6.1 MACROECONOMIC OUTLOOK FOR MIDDLE EAST & AFRICA
13.6.2 GCC COUNTRIES
13.6.2.1 Increasing emphasis on personalized medicines and developing healthcare infrastructure to drive market
13.6.3 REST OF MIDDLE EAST & AFRICA
14 COMPETITIVE LANDSCAPE
14.1 INTRODUCTION
14.2 KEY PLAYER STRATEGY/RIGHT TO WIN
14.2.1 OVERVIEW OF STRATEGIES ADOPTED BY KEY PLAYERS IN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET
14.3 REVENUE ANALYSIS, 2019–2023
14.4 MARKET SHARE ANALYSIS, 2023
14.4.1 RANKING OF KEY MARKET PLAYERS
14.5 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
14.5.1 STARS
14.5.2 EMERGING LEADERS
14.5.3 PERVASIVE PLAYERS
14.5.4 PARTICIPANTS
14.5.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023
14.5.5.1 Company footprint
14.5.5.2 Use case footprint
14.5.5.3 Process footprint
14.5.5.4 Therapeutic area footprint
14.5.5.5 Player type footprint
14.5.5.6 Deployment mode footprint
14.5.5.7 Region footprint
14.6 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
14.6.1 PROGRESSIVE COMPANIES
14.6.2 RESPONSIVE COMPANIES
14.6.3 DYNAMIC COMPANIES
14.6.4 STARTING BLOCKS
14.6.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023
14.7 COMPANY VALUATION AND FINANCIAL METRICS
14.7.1 FINANCIAL METRICS
14.7.2 COMPANY VALUATION
14.8 BRAND/PRODUCT COMPARISON
14.9 COMPETITIVE SCENARIO
14.9.1 PRODUCT AND SOLUTION LAUNCHES
14.9.2 DEALS
14.9.3 EXPANSIONS
14.9.4 OTHER DEVELOPMENTS
15 COMPANY PROFILES
15.1 KEY PLAYERS
15.1.1 NVIDIA CORPORATION
15.1.1.1 Business overview
15.1.1.2 Products/Services/Solutions offered
15.1.1.3 Recent developments
15.1.1.3.1 Product and service launches
15.1.1.3.2 Deals
15.1.1.4 MnM view
15.1.1.4.1 Right to win
15.1.1.4.2 Strategic choices
15.1.1.4.3 Weaknesses and competitive threats
15.1.2 EXSCIENTIA
15.1.2.1 Business overview
15.1.2.2 Products/Services/Solutions offered
15.1.2.3 Recent developments
15.1.2.3.1 Solution launches
15.1.2.3.2 Deals
15.1.2.3.3 Expansions
15.1.2.3.4 Other developments
15.1.2.4 MnM view
15.1.2.4.1 Right to win
15.1.2.4.2 Strategic choices
15.1.2.4.3 Weaknesses and competitive threats
15.1.3 GOOGLE
15.1.3.1 Business overview
15.1.3.2 Products/Services/Solutions offered
15.1.3.3 Recent developments
15.1.3.3.1 Solution launches
15.1.3.3.2 Deals
15.1.3.3.3 Expansions
15.1.3.4 MnM view
15.1.3.4.1 Right to win
15.1.3.4.2 Strategic choices
15.1.3.4.3 Weaknesses and competitive threats
15.1.4 RECURSION
15.1.4.1 Business overview
15.1.4.2 Products/Services/Solutions offered
15.1.4.3 Recent developments
15.1.4.3.1 Solution launches
15.1.4.3.2 Deals
15.1.4.3.3 Expansions
15.1.4.4 MnM view
15.1.4.4.1 Right to win
15.1.4.4.2 Strategic choices made
15.1.4.4.3 Weaknesses and competitive threats
15.1.5 INSILICO MEDICINE
15.1.5.1 Business overview
15.1.5.2 Products/Services/Solutions offered
15.1.5.3 Recent developments
15.1.5.3.1 Product and solution launches and developments
15.1.5.3.2 Deals
15.1.5.3.3 Other developments
15.1.5.4 MnM view
15.1.5.4.1 Right to win
15.1.5.4.2 Strategic choices
15.1.5.4.3 Weaknesses and competitive threats
15.1.6 SCHRЦDINGER, INC.
15.1.6.1 Business overview
15.1.6.2 Products/Services/Solutions offered
15.1.6.3 Recent developments
15.1.6.3.1 Deals
15.1.6.3.2 Other developments
15.1.7 BENEVOLENTAI
15.1.7.1 Business overview
15.1.7.2 Products/Services/Solutions offered
15.1.7.3 Recent developments
15.1.7.3.1 Deals
15.1.8 MICROSOFT CORPORATION
15.1.8.1 Business overview
15.1.8.2 Products/Services/Solutions offered
15.1.8.3 Recent developments
15.1.8.3.1 Deals
15.1.9 ATOMWISE INC.
15.1.9.1 Business overview
15.1.9.2 Products/Services/Solutions offered
15.1.9.3 Recent developments
15.1.9.3.1 Deals
15.1.10 ILLUMINA, INC.
15.1.10.1 Business overview
15.1.10.2 Products/Services/Solutions offered
15.1.10.3 Recent developments
15.1.10.3.1 Solution launches
15.1.10.3.2 Deals
15.1.11 NUMEDII, INC.
15.1.11.1 Business overview
15.1.11.2 Products/Services/Solutions offered
15.1.12 XTALPI INC.
15.1.12.1 Business overview
15.1.12.2 Products/Services/Solutions offered
15.1.12.3 Recent developments
15.1.12.3.1 Deals
15.1.13 IKTOS
15.1.13.1 Business overview
15.1.13.2 Products/Services/Solutions offered
15.1.13.3 Recent developments
15.1.13.3.1 Deals
15.1.13.3.2 Other developments
15.1.14 TEMPUS
15.1.14.1 Business overview
15.1.14.2 Products/Services/Solutions offered
15.1.14.3 Recent developments
15.1.14.3.1 Solution launches
15.1.14.3.2 Deals
15.1.14.3.3 Expansions
15.1.14.3.4 Other developments
15.1.15 DEEP GENOMICS
15.1.15.1 Business overview
15.1.15.2 Products/Services/Solutions offered
15.1.15.3 Recent developments
15.1.15.3.1 Solution launches
15.1.15.3.2 Deals
15.1.15.3.3 Other developments
15.1.16 VERGE GENOMICS
15.1.16.1 Business overview
15.1.16.2 Products/Services/Solutions offered
15.1.16.3 Recent developments
15.1.16.3.1 Deals
15.1.17 BENCHSCI
15.1.17.1 Business overview
15.1.17.2 Products/Services/Solutions offered
15.1.17.3 Recent developments
15.1.17.3.1 Solution launches
15.1.17.3.2 Deals
15.1.17.3.3 Other developments
15.1.18 INSITRO
15.1.18.1 Business overview
15.1.18.2 Products/Services/Solutions offered
15.1.18.3 Recent developments
15.1.18.3.1 Deals
15.1.18.3.2 Other developments
15.1.19 VALO HEALTH
15.1.19.1 Business overview
15.1.19.2 Products/Services/Solutions offered
15.1.19.3 Recent developments
15.1.19.3.1 Deals
15.1.19.3.2 Other developments
15.1.20 BPGBIO, INC.
15.1.20.1 Business overview
15.1.20.2 Products/Services/Solutions offered
15.1.20.3 Recent developments
15.1.20.3.1 Deals
15.1.21 MERCK KGAA
15.1.21.1 Business overview
15.1.21.2 Products/Services/Solutions offered
15.1.21.3 Recent developments
15.1.21.3.1 Solution launches
15.1.21.3.2 Deals
15.1.21.3.3 Expansions
15.1.21.3.4 Other developments
15.2 OTHER PLAYERS
15.2.1 PREDICTIVE ONCOLOGY
15.2.2 IQVIA INC.
15.2.3 TENCENT HOLDINGS LIMITED
15.2.4 CYTOREASON LTD.
15.2.5 OWKIN, INC.
15.2.6 CLOUD PHARMACEUTICALS
15.2.7 EVAXION BIOTECH A/S
15.2.8 STANDIGM INC.
15.2.9 BIOAGE LABS
15.2.10 ENVISAGENICS
15.2.11 ABCELLERA
15.2.12 CENTELLA
16 APPENDIX
16.1 DISCUSSION GUIDE
16.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
16.3 CUSTOMIZATION OPTIONS
16.4 RELATED REPORTS
16.5 AUTHOR DETAILS
