인공지능(AI) 신약 개발 시장 : 스텝별, 치료 영역별, 주요 지역별

AI in Drug Discovery Market Distribution by Drug Discovery Steps, Therapeutic Area and Key Geographies

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한글목차

세계 인공지능(AI) 신약 개발 시장 규모는 2035년까지 예측 기간 동안 16.5%의 연평균 복합 성장률(CAGR)로 성장할 것으로 예상되며, 18억 달러에서 2035년까지 134억 달러로 성장할 것으로 예측되고 있습니다.

시장 세분화 및 기회 분석은 다음 매개변수로 세분화됩니다.

신약 단계

• 타겟 식별/검증
• 히트 화합물의 창출/리드 화합물의 특정
• 리드 최적화

치료 영역

• 종양 질환
• 중추신경질환
• 감염증
• 호흡기 질환
• 심혈관 장애
• 내분비 질환
• 소화기 장애
• 근골격계 장애
• 면역학적 장애
• 피부과학적 장애
• 기타

주요 지역

• 북미
• 유럽
• 아시아태평양
• 라틴아메리카
• 중동 및 북아프리카
• 기타 지역

인공지능(AI) 신약 개발 시장 : 성장과 동향

의약품은 복잡한 과정이며 시간과 자원의 엄청난 사용을 수반합니다. 컨셉부터 상업상시까지의 의약품 개발에는 40억-100억 달러 상당의 자본 투자가 필요합니다. 의약품의 성공에 중요한 역할을 합니다. 기술의 진보나 생물학적 시스템의 이해의 향상에도 불구하고, 신약 프로세스는 여전히 비효율적이라고 생각되고 있습니다. 상품개발 프로젝트를 최적화하기 위해 이해관계자는 신약 개발의 다양한 단계에서 보다 나은 의사결정을 촉진하기 위한 선진기술 솔루션(인공지능(AI) 신약 개발 등)을 모색하고 있습니다.

빅데이터 분석, 딥러닝, 머신러닝과 같은 인공지능 기반 도구는 실시간으로 데이터를 완벽하게 정확하게 수집하는 데 도움이 됩니다. 패배를 줄이고 안전성을 확보하는 데 도움이 됩니다. Pfizer, Sanofi, Genentech와 같은 업계 대수사는 이미 사내 신약 활동에 다양한 AI 대응 플랫폼을 사용하고 있습니다.

인공지능(AI) 신약 개발시장 : 주요 인사이트력

이 보고서는 의약품에서 AI 시장의 현재 상태를 파악하고 업계 내 잠재적 성장 기회를 확인합니다.

• 현재 세계 약 210개 기업이 신약 개발 프로세스의 다양한 단계에서 AI 기반 기술을 사용하고 있다고 주장하고 있습니다.
• 이해관계자의 대부분은 다양한 치료 영역을 타겟으로 하는 의약품 후보의 리드 동정과 최적화에 필요한 AI 관련 전문 지식을 가지고 있습니다.
• 유리한 가능성을 예견해, 다수의 기업이 복수의 자금 조달 사례에 걸쳐 업계 관계자의 이니셔티브를 추진하기 위해 다액의 투자를 실시했습니다.

• AI 기반 기술을 이용한 의약품의 연구개발에 초점을 맞춘 파트너십 증가는 이 시장에서의 이해관계자의 관심의 고조를 뒷받침하고 있습니다.
• 일부 참가 기업은 강력한 경쟁력을 확립하는 데 성공하고 있습니다.
• AI를 활용한 신약에 관한 특허는 최근 260건 이상 출원/부여되어 있어, 이 영역에 있어서의 기술 혁신의 페이스가 높아지고 있는 것을 나타내고 있습니다.
• 신약 및 개발의 다양한 단계에서 AI를 활용한 툴이나 업무 어프로치가 채용되면, 연구개발비에 영향을 주어, 세계에서 대폭적인 코스트 삭감이 가능해질 것으로 보입니다.
• 이 시장은 향후 수년간 CAGR 16.5%로 성장할 것으로 예측되며, 그 기회는 다양한 신약 단계, 치료 영역, 지역에 잘 분산될 것으로 예측됩니다.

시장 세분화 : 주요 부문

신약 단계별로 시장은 타겟 식별/검증, 히트 생성/리드 식별, 리드 최적화로 구분됩니다.

시장은 치료영역별로 종양질환, 중추신경계질환, 감염증, 호흡기질환, 심혈관질환, 내분비질환, 소화기질환, 근골격계질환, 면역질환, 피부질환 등으로 구분됩니다. 종양성 질환 부문의 성장은 표적 치료제와 바이오마커를 확인하기 위해 종양학에서 인공지능이 널리 채용되고 있기 때문입니다.

주요 지역별로 볼 때 시장은 북미, 유럽, 아시아태평양, 라틴아메리카, 중동, 북아프리카, 기타 지역으로 구분됩니다.

인공지능(AI) 신약 개발시장에서의 진출기업 예

• Aiforia Technologies
• Atomwise
• BioSyntagma
• Chemalive
• Collaborations Pharmaceuticals
• Cyclica
• DeepMatter
• Recursion
• InveniAI
• MAbSilico
• Optibrium
• Recursion Pharmaceuticals
• Sensyne Health
• Valo Health

본 보고서에서는 세계의 인공지능(AI) 신약 개발 시장에 대해 조사했으며, 시장 개요와 함께, 신약 단계별, 치료 영역별, 주요 지역별 동향, 시장 진출기업프로파일 등의 정보를 제공합니다.

목차

제1장 서문

제2장 주요 요약

제3장 소개

• 장의 개요
• 인공지능
• AI의 서브세트
• 데이터 사이언스
• 헬스케어에 있어서의 AI의 응용
• 인공지능(AI) 신약 개발I
• 인공지능(AI) 신약 개발 활용의 이점
• AI 도입에 따른 과제
• 결론

제4장 경쟁 구도

• 장의 개요
• AI를 활용한 창약: 시장 상황

제5장 기업 프로파일 : 북미의 인공지능(AI) 신약 개발 제공업체

• 장의 개요
• Atomwise
• BioSyntagma
• Collaborations Pharmaceuticals
• Cyclica
• InveniAI
• Recursion Pharmaceuticals
• Valo Health

제6장 기업 프로파일 : 유럽의 인공지능(AI) 신약 개발 서비스 제공업체

• 장의 개요
• iforia Technologies
• Chemalive
• DeepMatter
• Exscientia
• MAbSilico
• Optibrium
• Sensyne Health

제7장 기업 프로파일 : 아시아태평양의 인공지능(AI) 신약 개발 서비스 제공업체

• 장의 개요
• 3BIGS
• Gero
• Insilico Medicine
• KeenEye

제8장 파트너십 및 협업

• 장의 개요
• 파트너십 모델
• 인공지능(AI) 신약 개발 : 파트너십과 콜라보레이션

제9장 자금 조달과 투자 분석

• 장의 개요
• 자금 조달의 유형
• 인공지능(AI) 신약 개발 : 자금조달과 투자

제10장 특허 분석

제11장 Porter's Five Forces 분석

제12장 기업 평가 분석

제13장 테크놀로지 대기업의 AI 헬스케어에 대한 대처

• 장의 개요
  • Amazon Web Services
  • Microsoft
  • Intel
  • Alibaba Cloud
  • Siemens
  • Google
  • IBM

제14장 비용 절감 분석

제15장 시장 예측

제16장 결론

제17장 주요 인사이트

제18장 부록 I: 표 형식 데이터

제19장 부록 II: 기업 및 조직 목록

SHW

영문 목차

영문목차

AI IN DRUG DISCOVERY MARKET: OVERVIEW

As per Roots Analysis, the global AI in drug discovery market is estimated to grow from USD 1.8 billion in the current year to USD 13.4 billion by 2035, at a CAGR of 16.5% during the forecast period, till 2035.

The market sizing and opportunity analysis has been segmented across the following parameters:

Drug Discovery Steps

• Target identification / validation
• Hit generation / lead identification
• Lead optimization

Therapeutic Area

• Oncological disorders
• CNS disorders
• Infectious diseases
• Respiratory disorders
• Cardiovascular disorders
• Endocrine disorders
• Gastrointestinal disorders
• Musculoskeletal disorders
• Immunological disorders
• Dermatological disorders
• Others

Key Geographical Regions

• North America
• Europe
• Asia-Pacific
• Latin America
• Middle East and North Africa
• Rest of the World

AI IN DRUG DISCOVERY MARKET: GROWTH AND TRENDS

Drug discovery is a complex process that involves significant utilization of time and resources. As per several sources, on average, the entire drug development process (from initial proof-of-concept to commercial launch) takes around 10-15 years and capital investments worth USD 4-10 billion to develop a drug from concept to commercial launch. The early stages, including target discovery and lead molecule identification, play an important role in the success of the drug in both preclinical and clinical studies. Despite the advances in technology and improved understanding of biological systems, the drug discovery process is still considered to be inefficient. In order to optimize the ongoing and future drug development projects, stakeholders are exploring advanced technology solutions (such as artificial intelligence in drug discovery) in order to facilitate better decision-making during the various stages of drug discovery and development.

Artificial intelligence-based tools, such as big data analytics, deep learning, and machine learning help to collect data in real-time with seamless accuracy. This capability aids in reducing clinical failures and ensuring safety during the early stages of development. Notably, over 50% of investments have been made in this domain over the last two years, reflecting a rapid inclination towards AI-based tools for drug discovery and development. In fact, several industry players, namely Pfizer, Sanofi and Genentech, are already using different AI-enabled platforms for internal drug discovery efforts. Considering the ongoing interest and rising adoption of AI in drug discovery, it is anticipated that the market will expand at a steady rate during the forecast period.

AI IN DRUG DISCOVERY MARKET: KEY INSIGHTS

The report delves into the current state of the AI in drug discovery market and identifies potential growth opportunities within the industry. Some key findings from the report include:

• Presently, around 210 players across the globe claim to use AI-based technologies across various steps of the drug discovery and development process.
• Majority of the stakeholders have the required AI-related expertise for lead identification and optimization of drug candidates targeting a range of therapeutic areas.
• Foreseeing the lucrative potential, a large number of players have made significant investments to advance the initiatives of industry stakeholders across multiple funding instances.

• A rise in partnerships focused on research and development of drugs using AI-based technologies validate the growing interest of stakeholders in this market.
• Some players have managed to establish strong competitive positions; in future, we expect multiple acquisitions to take place wherein the relative valuation of a firm is likely to be a key determinant.
• Over 260 patents related to AI-based drug discovery have recently been filed / granted, indicating the growing pace of innovation in this domain.
• The adoption of AI-enabled tools and operational approaches, across different stages of drug discovery and development, is likely to have an impact on R&D expenditure, enabling significant cost savings worldwide.
• The market is expected to grow at a CAGR of 16.5% in the coming years; the opportunity is anticipated to be well distributed across various drug discovery steps, therapeutic areas and regions.

AI IN DRUG DISCOVERY MARKET: KEY SEGMENTS

Lead Optimization Segment Occupies the Largest Share of AI in drug discovery market

Based on the drug discovery steps, the market is segmented into target identification / validation, hit generation / lead identification and lead optimization. At present, the lead optimization segment holds the maximum share of global AI in drug discovery market. Further, the lead optimization segment is likely to grow at a faster pace compared to the other segments.

By Therapeutic Area, Infectious Diseases is the Fastest Growing Segment of the Global AI in Drug Discovery Market During the Forecast Period

Based on the therapeutic area, the market is segmented into oncological disorders, CNS disorders, infectious diseases, respiratory disorders, cardiovascular disorders, endocrine disorders, gastrointestinal disorders, musculoskeletal disorders, immunological disorders, dermatological disorders, and others. Currently, the oncological disorders segment captures the highest proportion of the global AI in drug discovery market. The growth of the oncological disorders segment stems from the widespread adoption of artificial intelligence in oncology to identify targeted therapeutics and biomarkers. Further, it is worth highlighting that the AI in drug discovery market for the infectious diseases segment is likely to grow at a relatively higher CAGR.

North America Accounts for the Largest Share of the Market

Based on key geographical regions, the market is segmented into North America, Europe, and Asia-Pacific, Latin America, Middle East and North Africa and Rest of the World. Currently, North America dominates the AI in drug discovery market and accounts for the largest revenue share. Additionally, the market in Asia-Pacific is likely to grow at a higher CAGR in the future.

Example Players in the AI in Drug Discovery Market

• Aiforia Technologies
• Atomwise
• BioSyntagma
• Chemalive
• Collaborations Pharmaceuticals
• Cyclica
• DeepMatter
• Recursion
• InveniAI
• MAbSilico
• Optibrium
• Recursion Pharmaceuticals
• Sensyne Health
• Valo Health

AI IN DRUG DISCOVERY MARKET: RESEARCH COVERAGE

• Market Sizing and Opportunity Analysis: The report features an in-depth analysis of the global AI in drug discovery market, focusing on key market segments, including [A] drug discovery steps, [B] therapeutic area, and [C] key geographical regions.
• Market Landscape: A comprehensive evaluation of AI drug discovery companies, based on several relevant parameters, such as [A] year of establishment, [B] company size, [C] location of headquarters, [D] type of company, [E] type of AI technology, [F] type of drug molecule and [G] target therapeutic area.
• Company Profiles: In-depth profiles of key players engaged in the domain of AI in drug discovery, focusing on [A] overview of the company, [B] technology portfolio, and [C] recent developments and an informed future outlook.
• Partnerships and Collaborations: An insightful analysis of the deals inked by stakeholders in the AI in drug discovery market, based on several parameters, such as [A] year of partnership, [B] type of partnership, [C] target therapeutic area, [D] focus area, [E] type of partner company, [F] most active players (in terms of the number of partnerships signed) and [G] geographical distribution of partnership activity.
• Funding and Investments: An in-depth analysis of the fundings raised by AI in drug discovery companies, based on relevant parameters, such as [A] year of funding, [B] amount invested by year, [C] type of funding, [D] amount invested by company size, [E] type of investor, [F] amount invested by type of investor, [G] most active players, [H] most active investors and [I] geographical analysis.
• Patent Analysis: An in-depth analysis of patents filed / granted till date in the AI in drug discovery domain, based on various relevant parameters, such as [A] type of patent, [B] patent application year, [C] patent publication year, [D] geography, [E] CPC symbols, [F] emerging focus area, [G] type of applicant, [H] leading players, [I] patent age, [J] patent benchmarking, and [K] patent valuation analysis.
• PORTER'S Five Forces Analysis: A detailed analysis of the five competitive forces prevalent in AI in the drug discovery market, including [A] threats for new entrants, [B] bargaining power of drug developers, [C] bargaining power of AI-based drug discovery companies, [D] threats of substitute technologies, and [E] rivalry among existing competitors.
• Company Valuation Analysis: An in-depth analysis of the companies engaged in the AI in drug discovery market, based on [A] our proprietary, multi-variable dependent valuation model to estimate the current valuation / net worth of industry players.
• Cost Saving Analysis: An in-depth analysis of the likely cost-saving potential associated with the use of AI in the drug discovery sector, based on various parameters, such as [A] pharmaceutical R&D expenditure, [B] drug discovery expenditure / budget, and [C] adoption of AI across various drug discovery steps.
• Market Impact Analysis: A thorough analysis of various factors, such as drivers, restraints, opportunities, and existing challenges that are likely to impact market growth.

KEY QUESTIONS ANSWERED IN THIS REPORT

• How many companies are currently engaged in this market?
• Which are the leading companies in this market?
• What factors are likely to influence the evolution of this market?
• What is the current and future market size?
• What is the CAGR of this market?
• How is the current and future market opportunity likely to be distributed across key market segments?

REASONS TO BUY THIS REPORT

• The report provides a comprehensive market analysis, offering detailed revenue projections of the overall market and its specific sub-segments. This information is valuable to both established market leaders and emerging entrants.
• Stakeholders can leverage the report to gain a deeper understanding of the competitive dynamics within the market. By analyzing the competitive landscape, businesses can make informed decisions to optimize their market positioning and develop effective go-to-market strategies.
• The report offers stakeholders a comprehensive overview of the market, including key drivers, barriers, opportunities, and challenges. This information empowers stakeholders to stay abreast of market trends and make data-driven decisions to capitalize on growth prospects.

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TABLE OF CONTENTS

1. PREFACE

• 1.1. Scope of the Report
• 1.2. Research Methodology
• 1.3. Key Questions Answered
• 1.4. Chapter Outlines

2. EXECUTIVE SUMMARY

3. INTRODUCTION

• 3.1. Chapter Overview
• 3.2. Artificial Intelligence
• 3.3. Subsets of AI
  • 3.3.1. Machine Learning
    • 3.3.1.1. Supervised Learning
    • 3.3.1.2. Unsupervised Learning
    • 3.3.1.3. Reinforced / Reinforcement Learning
    • 3.3.1.4. Deep Learning
    • 3.3.1.5. Natural Language Processing (NLP)
• 3.4. Data Science
• 3.5. Applications of AI in Healthcare
  • 3.5.1. Drug Discovery
  • 3.5.2. Disease Prediction, Diagnosis and Treatment
  • 3.5.3. Manufacturing and Supply Chain Operations
  • 3.5.4. Marketing
  • 3.5.5. Clinical Trials
• 3.6. AI in Drug Discovery
  • 3.6.1. Identification of Pathway and Target
  • 3.6.2. Identification of Hit or Lead
  • 3.6.3. Lead Optimization
  • 3.6.4. Synthesis of Drug-Like Compounds
• 3.7. Advantages of Using AI in the Drug Discovery Process
• 3.8. Challenges Associated with the Adoption of AI
• 3.9. Concluding Remarks

4. COMPETITIVE LANDSCAPE

• 4.1. Chapter Overview
• 4.2. AI-based Drug Discovery: Overall Market Landscape
  • 4.2.1. Analysis by Year of Establishment
  • 4.2.2. Analysis by Company Size
  • 4.2.3. Analysis by Location of Headquarters
  • 4.2.4. Analysis by Type of Company
  • 4.2.5. Analysis by Type of Technology
  • 4.2.6. Analysis by Drug Discovery Steps
  • 4.2.7. Analysis by Type of Drug Molecule
  • 4.2.8. Analysis by Drug Development Initiatives
  • 4.2.9. Analysis by Technology Licensing Option
  • 4.2.10. Analysis by Target Therapeutic Area
  • 4.2.11. Key Players: Analysis by Number of Platforms / Tools Available

5. COMPANY PROFILES: AI-BASED DRUG DISCOVERY PROVIDERS IN NORTH AMERICA

• 5.1. Chapter Overview
• 5.2. Atomwise
  • 5.2.1. Company Overview
  • 5.2.2. AI-based Drug Discovery Technology Portfolio
  • 5.2.3. Recent Developments and Future Outlook
• 5.3. BioSyntagma
  • 5.3.1. Company Overview
  • 5.3.2. AI-based Drug Discovery Technology Portfolio
  • 5.3.3. Recent Developments and Future Outlook
• 5.4. Collaborations Pharmaceuticals
  • 5.4.1. Company Overview
  • 5.4.2. AI-based Drug Discovery Technology Portfolio
  • 5.4.3. Recent Developments and Future Outlook
• 5.5. Cyclica
  • 5.5.1. Company Overview
  • 5.5.2. AI-based Drug Discovery Technology Portfolio
  • 5.5.3. Recent Developments and Future Outlook
• 5.6. InveniAI
  • 5.6.1. Company Overview
  • 5.6.2. AI-based Drug Discovery Technology Portfolio
  • 5.6.3. Recent Developments and Future Outlook
• 5.7. Recursion Pharmaceuticals
  • 5.7.1. Company Overview
  • 5.7.2. AI-based Drug Discovery Technology Portfolio
  • 5.7.3. Recent Developments and Future Outlook
• 5.8. Valo Health
  • 5.8.1. Company Overview
  • 5.8.2. AI-based Drug Discovery Technology Portfolio
  • 5.8.3. Recent Developments and Future Outlook

6. COMPANY PROFILES: AI-BASED DRUG DISOCVERY SERVICE PROVIDERS IN EUROPE

• 6.1. Chapter Overview
• 6.2. iforia Technologies
  • 6.2.1. Company Overview
  • 6.2.2. AI-based Drug Discovery Technology Portfolio
  • 6.2.3. Recent Developments and Future Outlook
• 6.3. Chemalive
  • 6.3.1. Company Overview
  • 6.3.2. AI-based Drug Discovery Technology Portfolio
  • 6.3.3. Recent Developments and Future Outlook
• 6.4. DeepMatter
  • 6.4.1. Company Overview
  • 6.4.2. AI-based Drug Discovery Technology Portfolio
  • 6.4.3. Recent Developments and Future Outlook
• 6.5. Exscientia
  • 6.5.1. Company Overview
  • 6.5.2. AI-based Drug Discovery Technology Portfolio
  • 6.5.3. Recent Developments and Future Outlook
• 6.6. MAbSilico
  • 6.6.1. Company Overview
  • 6.6.2. AI-based Drug Discovery Technology Portfolio
  • 6.6.3. Recent Developments and Future Outlook
• 6.7. Optibrium
  • 6.7.1. Company Overview
  • 6.7.2. AI-based Drug Discovery Technology Portfolio
  • 6.7.3. Recent Developments and Future Outlook
• 6.8. Sensyne Health
  • 6.8.1. Company Overview
  • 6.8.2. AI-based Drug Discovery Technology Portfolio
  • 6.8.3. Recent Developments and Future Outlook

7. COMPANY PROFILES: AI-BASED DRUG DISOCVERY SERVICE PROVIDERS IN ASIA PACIFIC

• 7.1. Chapter Overview
• 7.2. 3BIGS
  • 7.2.1. Company Overview
  • 7.2.2. AI-based Drug Discovery Technology Portfolio
  • 7.2.3. Recent Developments and Future Outlook
• 7.3. Gero
  • 7.3.1. Company Overview
  • 7.3.2. AI-based Drug Discovery Technology Portfolio
  • 7.3.3. Recent Developments and Future Outlook
• 7.4. Insilico Medicine
  • 7.4.1. Company Overview
  • 7.4.2. AI-based Drug Discovery Technology Portfolio
  • 7.4.3. Recent Developments and Future Outlook
• 7.5. KeenEye
  • 7.5.1. Company Overview
  • 7.5.2. AI-based Drug Discovery Technology Portfolio
  • 7.5.3. Recent Developments and Future Outlook

8. PARTNERSHIPS AND COLLABORATIONS

• 8.1. Chapter Overview
• 8.2. Partnership Models
• 8.3. AI-based Drug Discovery: Partnerships and Collaborations
  • 8.3.1. Analysis by Year of Partnership
  • 8.3.2. Analysis by Type of Partnership
  • 8.3.3. Analysis by Year and Type of Partnership
  • 8.3.4. Analysis by Target Therapeutic Area
  • 8.3.5. Analysis by Focus Area
  • 8.3.6. Analysis by Year of Partnership and Focus Area
  • 8.3.7. Analysis by Type of Partner Company
  • 8.3.8. Analysis by Type of Partnership and Type of Partner Company
  • 8.3.9. Most Active Players: Analysis by Number of Partnerships
  • 8.3.10. Analysis by Region
    • 8.3.10.1. Intercontinental and Intracontinental Deals
    • 8.3.10.2. International and Local Deals

9. FUNDING AND INVESTMENT ANALYSIS

• 9.1. Chapter Overview
• 9.2. Types of Funding
• 9.3. AI-based Drug Discovery: Funding and Investments
  • 9.3.1. Analysis of Number of Funding Instances by Year
  • 9.3.2. Analysis of Amount Invested by Year
  • 9.3.3. Analysis by Type of Funding
  • 9.3.4. Analysis of Amount Invested and Type of Funding
  • 9.3.5. Analysis of Amount Invested by Company Size
  • 9.3.6. Analysis by Type of Investor
  • 9.3.7. Analysis of Amount Invested by Type of Investor
  • 9.3.8. Most Active Players: Analysis by Number of Funding Instances
  • 9.3.9. Most Active Players: Analysis by Amount Invested
  • 9.3.10. Most Active Investors: Analysis by Number of Funding Instances
  • 9.3.11. Analysis of Amount Invested by Geography
    • 9.3.11.1. Analysis by Region
    • 9.3.11.2. Analysis by Country

10. PATENT ANALYSIS

• 10.1. Chapter Overview
• 10.2. Scope and Methodology
• 10.3. AI-based Drug Discovery: Patent Analysis
  • 10.3.1. Analysis by Application Year
  • 10.3.2. Analysis by Geography
  • 10.3.3. Analysis by CPC Symbols
  • 10.3.4. Analysis by Emerging Focus Areas
  • 10.3.5. Analysis by Type of Applicant
  • 10.3.6. Leading Players: Analysis by Number of Patents
• 10.4. AI-based Drug Discovery: Patent Benchmarking
  • 10.4.1. Analysis by Patent Characteristics
• 10.5. AI-based Drug Discovery: Patent Valuation
• 10.6. Leading Patents: Analysis by Number of Citations

11. PORTER'S FIVE FORCES ANALYSIS

• 11.1. Chapter Overview
• 11.2. Methodology and Assumptions
• 11.3. Key Parameters
  • 11.3.1. Threats of New Entrants
  • 11.3.2. Bargaining Power of Drug Developers
  • 11.3.3. Bargaining Power of Companies Using AI for Drug Discovery
  • 11.3.4. Threats of Substitute Technologies
  • 11.3.5. Rivalry Among Existing Competitors
• 11.4. Concluding Remarks

12. COMPANY VALUATION ANALYSIS

• 12.1. Chapter Overview
• 12.2. Company Valuation Analysis: Key Parameters
• 12.3. Methodology
• 12.4. Company Valuation Analysis: Roots Analysis Proprietary Scores

13. AI-BASED HEALTHCARE INITIATIVES OF TECHNOLOGY GIANTS

• 13.1. Chapter Overview
  • 13.1.1. Amazon Web Services
  • 13.1.2. Microsoft
  • 13.1.3. Intel
  • 13.1.4. Alibaba Cloud
  • 13.1.5. Siemens
  • 13.1.6. Google
  • 13.1.7. IBM

14. COST SAVING ANALYSIS

• 14.1. Chapter Overview
• 14.2. Key Assumptions and Methodology
• 14.3. Overall Cost Saving Potential Associated with Use of AI-based Solutions in Drug Discovery, till 2035
  • 14.3.1. Likely Cost Savings: Analysis by Drug Discovery Steps, till 2035
    • 14.3.1.1. Likely Cost Savings During Target Identification / Validation, till 2035
    • 14.3.1.2. Likely Cost Savings During Hit Generation / Lead Identification, till 2035
    • 14.3.1.3. Likely Cost Savings During Lead Optimization, till 2035
  • 14.3.2. Likely Cost Savings: Analysis by Target Therapeutic Area, till 2035
    • 14.3.2.1. Likely Cost Savings for Drugs Targeting Oncological Disorders, till 2035
    • 14.3.2.2. Likely Cost Savings for Drugs Targeting Neurological Disorders, till 2035
    • 14.3.2.3. Likely Cost Savings for Drugs Targeting Infectious Diseases, till 2035
    • 14.3.2.4. Likely Cost Savings for Drugs Targeting Respiratory Disorders, till 2035
    • 14.3.2.5. Likely Cost Savings for Drugs Targeting Cardiovascular Disorders, till 2035
    • 14.3.2.6. Likely Cost Savings for Drugs Targeting Endocrine Disorders, till 2035
    • 14.3.2.7. Likely Cost Savings for Drugs Targeting Gastrointestinal Disorders, till 2035
    • 14.3.2.8. Likely Cost Savings for Drugs Targeting Musculoskeletal Disorders, till 2035
    • 14.3.2.9. Likely Cost Savings for Drugs Targeting Immunological Disorders, till 2035
    • 14.3.2.10. Likely Cost Savings for Drugs Targeting Dermatological Disorders, till 2035
    • 14.3.2.11. Likely Cost Savings for Drugs Targeting Other Disorders, till 2035
  • 14.3.3. Likely Cost Savings: Analysis by Geography, till 2035
    • 14.3.3.1. Likely Cost Savings in North America, till 2035
    • 14.3.3.2. Likely Cost Savings in Europe, till 2035
    • 14.3.3.3. Likely Cost Savings in Asia Pacific, till 2035
    • 14.3.3.4. Likely Cost Savings in MENA, till 2035
    • 14.3.3.5. Likely Cost Savings in Latin America, till 2035
    • 14.3.3.6. Likely Cost Savings in Rest of the World, till 2035

15. MARKET FORECAST

• 15.1. Chapter Overview
• 15.2. Key Assumptions and Methodology
• 15.3. Global AI-based Drug Discovery Market, till 2035
  • 15.3.1. AI-based Drug Discovery Market: Distribution by Drug Discovery Steps, till 2035
    • 15.3.1.1. AI-based Drug Discovery Market for Target Identification / Validation, till 2035
    • 15.3.1.2. AI-based Drug Discovery Market for Hit Generation / Lead Identification, till 2035
    • 15.3.1.3. AI-based Drug Discovery Market for Lead Optimization, till 2035
  • 15.3.2. AI-based Drug Discovery Market: Distribution by Target Therapeutic Area, till 2035
    • 15.3.2.1. AI-based Drug Discovery Market for Oncological Disorders, till 2035
    • 15.3.2.2. AI-based Drug Discovery Market for Neurological Disorders, till 2035
    • 15.3.2.3. AI-based Drug Discovery Market for Infectious Diseases, till 2035
    • 15.3.2.4. AI-based Drug Discovery Market for Respiratory Disorders, till 2035
    • 15.3.2.5. AI-based Drug Discovery Market for Cardiovascular Disorders, till 2035
    • 15.3.2.6. AI-based Drug Discovery Market for Endocrine Disorders, till 2035
    • 15.3.2.7. AI-based Drug Discovery Market for Gastrointestinal Disorders, till 2035
    • 15.3.2.8. AI-based Drug Discovery Market for Musculoskeletal Disorders, till 2035
    • 15.3.2.9. AI-based Drug Discovery Market for Immunological Disorders, till 2035
    • 15.3.2.10. AI-based Drug Discovery Market for Dermatological Disorders, till 2035
    • 15.3.2.11. AI-based Drug Discovery Market for Other Disorders, till 2035
  • 15.3.3. AI-based Drug Discovery Market: Distribution by Geography, till 2035
    • 15.3.3.1. AI-based Drug Discovery Market in North America, till 2035
      • 15.3.3.1.1. AI-based Drug Discovery Market in the US, till 2035
      • 15.3.3.1.2. AI-based Drug Discovery Market in Canada, till 2035
    • 15.3.3.2. AI-based Drug Discovery Market in Europe, till 2035
      • 15.3.3.2.1. AI-based Drug Discovery Market in the UK, till 2035
      • 15.3.3.2.2. AI-based Drug Discovery Market in France, till 2035
      • 15.3.3.2.3. AI-based Drug Discovery Market in Germany, till 2035
      • 15.3.3.2.4. AI-based Drug Discovery Market in Spain, till 2035
      • 15.3.3.2.5. AI-based Drug Discovery Market in Italy, till 2035
      • 15.3.3.2.6. AI-based Drug Discovery Market in Rest of Europe, till 2035
    • 15.3.3.3. AI-based Drug Discovery Market in Asia Pacific, 2020-2035
      • 15.3.3.3.1. AI-based Drug Discovery Market in China, till 2035
      • 15.3.3.3.2. AI-based Drug Discovery Market in India, till 2035
      • 15.3.3.3.3. AI-based Drug Discovery Market in Japan, till 2035
      • 15.3.3.3.4. AI-based Drug Discovery Market in Australia, till 2035
      • 15.3.3.3.5. AI-based Drug Discovery Market in South Korea, till 2035
    • 15.3.3.4. AI-based Drug Discovery Market in MENA, till 2035
      • 15.3.3.4.1. AI-based Drug Discovery Market in Saudi Arabia, till 2035
      • 15.3.3.4.2. AI-based Drug Discovery Market in UAE, till 2035
      • 15.3.3.4.3. AI-based Drug Discovery Market in Iran, till 2035
    • 15.3.3.5. AI-based Drug Discovery Market in Latin America, till 2035
      • 15.3.3.5.1. AI-based Drug Discovery Market in Argentina, till 2035
    • 15.3.3.6. AI-based Drug Discovery Market in Rest of the World, till 2035

16. CONCLUSION

17. EXECUTIVE INSIGHTS

• 17.1. Chapter Overview
• 17.2. Aigenpulse
  • 17.2.1. Company Snapshot
  • 17.2.2. Interview Transcript: Steve Yemm (Chief Commercial Officer) and Satnam Surae (Chief Product Officer)
• 17.3. Cloud Pharmaceuticals
  • 17.3.1. Company Snapshot
  • 17.3.2. Interview Transcript: Ed Addison (Co-founder, Chairman and Chief Executive Officer)
• 17.4. DEARGEN
  • 17.4.1. Company Snapshot
  • 17.4.2. Interview Transcript: Bo Ram Beck (Head Researcher)
• 17.5. Intelligent Omics
  • 17.5.1. Company Snapshot
  • 17.5.2. Interview Transcript: Simon Haworth (Chief Executive Officer)
• 17.6. Pepticom
  • 17.6.1. Company Snapshot
  • 17.6.2. Interview Transcript: Immanuel Lerner (Chief Executive Officer, Co-Founder)
• 17.7. Sage-N Research
  • 17.7.1. Company Snapshot
  • 17.7.2. Interview Transcript: David Chiang (Chairman)

18. APPENDIX I: TABULATED DATA

19. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS

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