세계의 Drug Discovery 분야 인공지능 시장 : 프로세스별, 사용 사례별, 치료 분야별, 진입 기업 유형별, 도구별, 전개별, 최종사용자별, 지역별 - 예측(-2029년)

Artificial Intelligence in Drug Discovery Market by Process, Use Case, Therapy, Tool (ML:DL ), End User & Region - Global Forecast to 2029

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한글목차

세계 Drug Discovery 분야 인공지능(AI) 시장 규모는 2024년 18억 6,000만 달러에서 2029년 68억 9,000만 달러에 달할 것으로 예상되며, 2024년부터 2029년까지 연평균 29.9%의 CAGR을 기록할 것으로 전망됩니다.

Drug Discovery 공급망의 다양한 측면에서 전문 지식, 리소스, 기술을 결합함으로써 업계 간 협업과 파트너십이 증가하고 있으며, 이는 Drug Discovery 분야 인공지능(AI) 시장 성장을 촉진하고 있습니다. 예를 들어, 2024년 3월 Cognizant는 BioNeMo 플랫폼을 통해 생성형 AI를 활용하기 위해 NVIDIA와 협력하여 신약 개발을 혁신하고 생명을 구하는 치료법 개발을 가속화하는 것을 목표로 하고 있습니다. 마찬가지로 2024년 8월, Exscientia Recursion과 Exscientia plc는 신약 개발을 강화하기 위해 양사의 기술을 결합하기로 합의했다고 발표했습니다. 통합 Recursion OS는 환자 중심 타겟 탐색, AI 기반 설계, 양자 역학 모델링, 자동 화학 합성 등의 기능을 통해 신약 개발을 강화할 예정입니다. 통합 회사는 18개월 내에 10건의 임상시험을 완료할 예정이며, Exscientia의 주주들은 Recursion의 주식을 받게 되고 Recursion의 주주들은 통합 회사의 74%를 소유하게 됩니다. 이번 거래는 8억 5,000만 달러 상당의 현금으로 2025년 초까지 완료될 예정입니다.

조사 범위
조사 대상 연도	2022-2029년
기준 연도	2023년
예측 기간	2024-2029년
검토 단위	100만/10억(달러)
부문별	프로세스별, 사용 사례별, 치료 분야별, 진입 기업 유형별, 도구별, 전개별, 최종사용자별, 지역별
대상 지역	북미, 유럽, 아시아태평양, 라틴아메리카, 중동 및 아프리카

치료 분야별로 Drug Discovery 분야 인공지능(AI) 시장은 종양학, 감염학, 신경학, 대사성 질환, 심혈관질환, 면역학, 정신건강, 기타(호흡기질환, 신장학, 피부과 질환, 유전성 질환, 염증성 질환, 소화기 질환)로 구분됩니다. 암의 높은 유병률과 종양 생물학의 복잡한 특성으로 인해 암 분야는 인공지능(AI) 신약 개발 시장에서 가장 큰 시장 점유율을 차지하고 있으며, 2022년까지 전 세계적으로 약 2,000만 명의 신규 암 환자와 970만 명의 암 관련 사망자가 발생했습니다. 마찬가지로 2024년에는 미국에서 200만 명의 신규 암환자와 611,720명의 암 사망자가 발생할 것으로 예측됩니다. 암 연구, 환자 기록, 유전체 연구, 멀티오믹스 데이터셋(유전체, 단백질체학, 트랜스스크립믹스), 임상시험에서 얻은 생물의학 데이터의 가용성이 증가함에 따라 패턴 인식과 약물 상호작용 예측에 AI를 활용할 수 있는 기회를 제공하고 있습니다. 암 분야에서 개인화된 의료 및 표적치료에 대한 높은 수요, 높은 상업적 수익, 면역종양학(특히 체크포인트 억제제 및 T세포 치료)에 대한 새로운 관심, 종합적인 데이터 가용성은 AI 기반 솔루션에 대한 투자를 촉진하고 AI를 신약개발의 최전선으로 끌어올릴 것입니다.

사용 사례별로 Drug Discovery 분야 인공지능(AI) 시장은 질병 이해, 약물 재사용, 새로운 약물 설계, 약물 최적화, 안전성 및 독성 등으로 구분됩니다. 질병 이해는 예측 기간 동안 가장 빠르게 성장하는 사용 사례가 될 것으로 예상됩니다. AI가 복잡한 생물학적 데이터를 평가하고 질병 메커니즘을 식별하는 능력은 초기 단계의 의약품 개발에서 매우 중요하며, AI는 연구자들이 질병 경로, 유전적 요인, 바이오마커를 더 잘 이해할 수 있도록 도와주며, 이는 모두 표적 치료제를 개발하는 데 필요한 것입니다. 질병에 대한 이해는 잠재적인 신약 개발 표적을 식별하는 데 필요하며, 신약 설계 및 시험과 같은 후속 단계의 효율성을 높입니다. 표현형 스크리닝, 이미지 분석, 세포 및 조직 형태 관련 유전자 이상 검출, 바이오마커 식별, 오믹스 데이터 마이닝을 위한 AI의 활용 확대가 시장 성장을 촉진할 것으로 예상됩니다.

이 보고서는 세계 Drug Discovery 분야 인공지능 시장을 조사하여 프로세스별, 사용 사례별, 치료 분야별, 진입 기업 유형별, 도구별, 전개별, 최종사용자별, 지역별 동향, 시장 진입 기업 개요 등을 정리했습니다.

목차

제1장 소개

제2장 조사 방법

제3장 주요 요약

제4장 주요 인사이트

제5장 시장 개요

• 소개
• 시장 역학
• 고객의 비즈니스에 영향을 미치는 동향/혼란
• 업계 동향
• 생태계 분석
• 공급망 분석
• 기술 분석
• 규제 상황
• 가격 분석
• Porter's Five Forces 분석
• 주요 이해관계자와 구입 기준
• 특허 분석
• 미충족 수요와 주요 문제점
• 2024-2025년의 주요 회의와 이벤트
• 사례 연구 분석
• 비즈니스 모델 분석
• 투자와 자금 조달 시나리오
• AI/생성형 AI가 Drug Discovery 분야 인공지능 시장에 미치는 영향
• 인공지능으로 탄생한 임상 자산

제6장 Drug Discovery 분야 인공지능 시장(프로세스별)

• 소개
• 표적 특정과 선택
• 표적 검증
• 히트 식별과 우선순위 지정
• 히트에서 리드로의 식별/리드 생성
• 리드 최적화
• 후보자 선정과 검증

제7장 Drug Discovery 분야 인공지능 시장(사용 사례별)

• 소개
• 질병 이해
• 약물 재이용
• 신규 의약품 설계
• 약제 최적화
• 안전성과 독성

제8장 Drug Discovery 분야 인공지능 시장(치료 분야별)

• 소개
• 종양학
• 감염증
• 신경학
• 대사성 질환
• 심혈관질환
• 면역학
• 정신질환
• 기타

제9장 Drug Discovery 분야 인공지능 시장(진출 기업 유형별)

• 소개
• 엔드 투 엔드 솔루션 프로바이더
• 틈새/포인트 솔루션 프로바이더
• AI 테크놀러지 프로바이더
• 비즈니스 프로세스 서비스 프로바이더

제10장 Drug Discovery 분야 인공지능 시장(툴별)

• 소개
• 머신러닝
• 자연어 처리
• 상황 인식 처리와 컴퓨팅
• 컴퓨터 비전
• 이미지 분석

제11장 Drug Discovery 분야 인공지능 시장(전개별)

• 소개
• 온프레미스 전개
• 클라우드 기반 전개
• SaaS 기반 전개

제12장 Drug Discovery 분야 인공지능 시장(최종사용자별)

• 소개
• 제약·바이오테크놀러지 기업
• CRO
• 연구 센터 및 학술기관·정부기관

제13장 Drug Discovery 분야 인공지능 시장(지역별)

• 소개
• 북미
• 유럽
• 아시아태평양
• 라틴아메리카
• 중동 및 아프리카

제14장 경쟁 상황

• 소개
• 주요 진출 기업 전략/비책
• 매출 분석, 2019-2023년
• 시장 점유율 분석, 2023년
• 기업 평가 매트릭스 : 주요 진출 기업, 2023년
• 기업 평가 매트릭스 : 스타트업/중소기업, 2023년
• 기업 가치 평가와 재무 지표
• 브랜드/제품 비교
• 경쟁 시나리오

제15장 기업 개요

• 주요 진출 기업
  • NVIDIA CORPORATION
  • EXSCIENTIA
  • GOOGLE
  • RECURSION
  • INSILICO MEDICINE
  • SCHRODINGER, INC.
  • BENEVOLENTAI
  • MICROSOFT CORPORATION
  • ATOMWISE INC.
  • ILLUMINA, INC.
  • NUMEDII, INC.
  • XTALPI INC.
  • IKTOS
  • TEMPUS
  • DEEP GENOMICS
  • VERGE GENOMICS
  • BENCHSCI
  • INSITRO
  • VALO HEALTH
  • BPGBIO, INC.
  • MERCK KGAA
• 기타 기업
  • PREDICTIVE ONCOLOGY
  • IQVIA INC.
  • TENCENT HOLDINGS LIMITED
  • CYTOREASON LTD.
  • OWKIN, INC.
  • CLOUD PHARMACEUTICALS
  • EVAXION BIOTECH A/S
  • STANDIGM INC.
  • BIOAGE LABS
  • ENVISAGENICS
  • ABCELLERA
  • CENTELLA

제16장 부록

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영문 목차

영문목차

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.

Scope of the Report
Years Considered for the Study	2022-2029
Base Year	2023
Forecast Period	2024-2029
Units Considered	Million/Billion (USD)
Segments	By Process, use case, therapeutic area, player type, AI tools, deployment, end user, and region
Regions covered	North America, Europe, Asia Pacific, Latin America, and Middle East & Africa.

"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 AI-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 AI. Furthermore, in February 2023, the FDA granted an Orphan Drug Designation to a drug discovered and designed with AI. Insilico Medicine and began a global Phase I trial for the drug.

In-depth interviews have been conducted with chief executive officers (CEOs), Directors, and other executives from various key organizations operating in the authentication and brand protection marketplace.

Breakdown of supply-side primary interviews by company type, designation, and region:

• By Company Type: Tier 1 (31%), Tier 2 (28%), and Tier 3 (41%)
• By Designation - Demand Side: Purchase Managers (45%), Heads of Artificial Intelligence, Machine Learning, Drug Discovery, and Computational Molecular Design (30%), and Research Scientists (25%)
• By Designation - Supply Side: C-level Excecutives & Director level (35%), Managers (40%), and Others (25%)
• By Region: North America (45%), Europe (30%), Asia Pacific (20%), and Rest of the world (5%)

List of Companies Profiled in the Report

• NVIDIA Corporation (US)
• Exscientia (UK)
• Google (US)
• BenevolentAI (UK)
• Recursion (US)
• Insilico Medicine (US)
• Schrodinger, 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)

The study includes an in-depth competitive analysis of these key players in the artificial intelligence (AI) in drug discovery market, with their company profiles, recent developments, and key market strategies.

Research Coverage

This research report categorizes the artificial intelligence (AI) in drug discovery market by process (target identification & selection, target validation, hit identification & prioritization, hit-to-lead identification/lead generation, lead optimization, and candidate selection & validation), by use case (understanding disease, drug repurposing, de novo drug design [small molecule design, vaccines design, antibody & other biologics design], drug optimization [small molecule optimization, vaccines optimization, antibody & other biologics optimization], and safety and toxicity), by therapeutic area (oncology, infectious diseases, neurology, metabolic diseases, cardiovascular diseases, immunology, mental health, others), by player type (end-to-end solution providers, niche/point solutions providers, AI technology providers, business process service providers), by tools (machine learning, natural language processing, context-aware process and computing, computer vision, image analysis (including optical character recognition)), by deployment (on-premise, cloud-based, SaaS-based), by end user (pharmaceutical & biotechnology companies, contract research organizations (CROs), and research centers, academic institutes, & government organizations) and by region (North America, Europe, Asia Pacific, Latin America, and Middle East & Africa). The scope of the report covers detailed information regarding the major factors, such as drivers, restraints, challenges, and opportunities, influencing the growth of the artificial intelligence (AI) in drug discovery market. A detailed analysis of the key industry players has been done to provide insights into their business overview, solutions, and services, key strategies such as product launches and enhancements, investments, partnerships, collaborations, agreements, joint ventures, funding, acquisitions, expansions, conferences, FDA clearances, sales contracts, alliances, and other recent developments associated with the artificial intelligence (AI) in drug discovery market. Competitive analysis of upcoming startups in the artificial intelligence (AI) in drug discovery market ecosystem is covered in this report.

Reasons to buy this report

The report will help the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the artificial intelligence (AI) in drug discovery market and the subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and to plan suitable go-to-market strategies. The report also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.

The report provides insights on the following pointers:

• Analysis of key drivers (growing cross-industry collaborations and partnerships, growing need to reduce time and cost of drug discovery and development, patent expiry of several drugs, AI application in oncology areas, integration of multi-omics data, initiatives for research on rare diseases and orphan drugs), restraints (shortage of AI workforce and ambiguous regulatory guidelines for medical software, interpretability of AI), opportunities (growing biotechnology industry, increasing focus on emerging markets, focus on developing human-aware AI systems, increasing use of AI in single cell analysis, rapid expansion of biomarker, disease types, and subtypes identification, growing demand for precision and personalized medicine), and challenges (limited availability of data sets, lack of required tools and usability, computational limitations of advanced AI models, challenges regarding the accessibility of high-quality data) influencing the growth of the artificial intelligence (AI) in drug discovery market
• Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the artificial intelligence (AI) in drug discovery market
• Market Development: Comprehensive information about lucrative markets - the report analyses the artificial intelligence (AI) in drug discovery market across varied regions.
• Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the artificial intelligence (AI) in drug discovery market
• Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading players such as NVIDIA Corporation (US), Exscientia (UK), Google (US), BenevolentAI (UK), Recursion (US), Insilico Medicine (US), Schrodinger, 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.

TABLE OF CONTENTS

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 SCHRODINGER, 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

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