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Global Artificial Intelligence in Drug Discovery Market to reach USD 16.62 billion by 2032.

Global Artificial Intelligence in Drug Discovery Market Size Study, by Application (Drug Optimization and Repurposing, Preclinical Testing, Others), by Therapeutic Area (Oncology, Neurodegenerative Diseases, Cardiovascular Diseases, Metabolic Diseases, Infectious Diseases, Others), and Regional Forecasts 2022-2032

Product Code: HLSHIT-44412512
Publish Date: 10-12-2024
Page: 200

The Global Artificial Intelligence (AI) in Drug Discovery Market was valued at approximately USD 1.6 billion in 2023 and is projected to expand at a robust CAGR of 29.7% during the forecast period 2024-2032. This burgeoning growth is attributed to the increasing adoption of AI technologies, including machine learning and deep learning, across various phases of drug discovery, from initial compound screening to clinical trials. The rising need for innovative drug therapies and the integration of advanced analytics in preclinical testing processes drive the market’s expansion. Furthermore, a surge in strategic collaborations between AI startups and pharmaceutical companies is reshaping the drug discovery landscape, optimizing processes, and reducing developmental timelines.
The digitalization of biomedical and clinical research is further propelling the implementation of AI-powered solutions. Large datasets generated during molecule screening and preclinical studies demand sophisticated tools for accurate analysis, making AI indispensable for researchers. Advanced machine learning algorithms not only enhance the precision of molecule binding predictions but also reduce errors, fostering significant cost efficiencies. Notably, government initiatives in emerging and developed economies are accelerating the penetration of AI technologies, enabling streamlined regulatory processes and fostering innovation.
Among applications, Drug Optimization and Repurposing leads the market, contributing the highest share of 53.7% in 2023. This dominance underscores the efficiency of AI in refining existing drug candidates and identifying novel therapeutic uses, thereby addressing unmet medical needs while maximizing investment returns. Meanwhile, the Preclinical Testing segment exhibits the fastest growth, with AI’s ability to optimize testing protocols, predict drug toxicity, and model biological interactions significantly enhancing its appeal to pharmaceutical companies.
Regionally, North America commands the largest market share at 57.7%, driven by substantial investments in healthcare technologies and a favorable regulatory landscape. The region’s robust research infrastructure and collaboration between technology giants and pharmaceutical companies amplify the adoption of AI in drug discovery. Simultaneously, Asia Pacific emerges as the fastest-growing region, fueled by advancements in AI applications across countries like China, Japan, and India. These nations prioritize AI integration to improve clinical trial efficiency and address complex healthcare challenges.
The industry is witnessing an influx of mergers, acquisitions, and strategic partnerships aimed at advancing AI capabilities in drug discovery. For instance, BioNTech’s acquisition of InstaDeep highlights the industry’s focus on leveraging AI for immunotherapy innovations. However, stringent regulations and ethical considerations surrounding AI applications pose challenges, emphasizing the importance of compliance with international standards to sustain market growth.
Major players shaping this market include IBM, Exscientia, Google (DeepMind), and Insilico Medicine, among others. These companies are continually driving innovation, underlining the transformative potential of AI in revolutionizing drug discovery processes.
Key Players Included in This Report:
• IBM
• Exscientia
• Insilico Medicine
• Google (DeepMind)
• BenevolentAI
• Atomwise Inc.
• Berg Health (acquired by BPGbio Inc.)
• BioSymetrics, Inc.
• insitro
• GNS Healthcare (rebranded as Aitia)
• CYCLICA (acquired by Recursion)
• Cloud Pharmaceuticals
• BioAge Labs
• Merck & Co.
• Fujitsu
The detailed segments and sub-segment of the market are explained below:
By Application:
• Drug Optimization and Repurposing
• Preclinical Testing
• Others
By Therapeutic Area:
• Oncology
• Neurodegenerative Diseases
• Cardiovascular Diseases
• Metabolic Diseases
• Infectious Diseases
• Others
By Region:
North America
• U.S.
• Canada
• Mexico
Europe
• U.K.
• Germany
• France
• Italy
• Spain
• Denmark
• Sweden
• Norway
Asia Pacific
• Japan
• China
• India
• South Korea
• Australia
Latin America
• Brazil
• Argentina
Middle East & Africa
• South Africa
• Saudi Arabia
• UAE
• Kuwait
Years considered for the study are as follows:
• Historical year – 2022
• Base year – 2023
• Forecast period – 2024 to 2032

Key Takeaways:
• Market Estimates & Forecast for 10 years from 2022 to 2032.
• Regional and segment-level analysis.
• Comprehensive competitive landscape and key player strategies.
• Supply-side and demand-side analysis of the market.

Table of Contents
Chapter 1. Global Artificial Intelligence in Drug Discovery Market Executive Summary
1.1. Market Size & Forecast (2022-2032)
1.2. Regional Summary
1.3. Segmental Summary
1.3.1. By Application
1.3.2. By Therapeutic Area
1.4. Key Trends
1.5. Recession Impact
1.6. Analyst Recommendations & Conclusion
Chapter 2. Global Artificial Intelligence in Drug Discovery Market Definition and Research Assumptions
2.1. Research Objective
2.2. Market Definition
2.3. Research Assumptions
2.3.1. Inclusion & Exclusion
2.3.2. Limitations
2.3.3. Supply Side Analysis
2.3.3.1. Availability
2.3.3.2. Infrastructure
2.3.3.3. Regulatory Environment
2.3.3.4. Market Competition
2.3.3.5. Economic Viability (Consumer’s Perspective)
2.3.4. Demand Side Analysis
2.3.4.1. Regulatory Frameworks
2.3.4.2. Technological Advancements
2.3.4.3. Environmental Considerations
2.3.4.4. Consumer Awareness & Acceptance
2.4. Estimation Methodology
2.5. Years Considered for the Study
2.6. Currency Conversion Rates
Chapter 3. Global Artificial Intelligence in Drug Discovery Market Dynamics
3.1. Market Drivers
3.1.1. Rising Demand for Novel Therapies
3.1.2. Growing Strategic Collaborations and Partnerships
3.1.3. Cost Efficiency and Faster Drug Development Processes
3.2. Market Challenges
3.2.1. Regulatory Compliance and Ethical Considerations
3.2.2. Data Integration and Standardization Issues
3.3. Market Opportunities
3.3.1. Increasing AI Adoption in Emerging Markets
3.3.2. Advances in Data Mining and Machine Learning Algorithms
Chapter 4. Global Artificial Intelligence in Drug Discovery Market Industry Analysis
4.1. Porter’s Five Forces Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.1.6. Porter’s Five Forces Impact Analysis
4.2. PESTEL Analysis
4.2.1. Political
4.2.2. Economical
4.2.3. Social
4.2.4. Technological
4.2.5. Environmental
4.2.6. Legal
4.3. Top Investment Opportunities
4.4. Top Winning Strategies
4.5. Disruptive Trends in AI-Powered Drug Discovery
4.6. Analyst Recommendations & Conclusion
Chapter 5. Global Artificial Intelligence in Drug Discovery Market Size & Forecast by Application (2022-2032)
5.1. Segment Dashboard
5.2. Revenue Analysis by Application
5.2.1. Drug Optimization and Repurposing
5.2.2. Preclinical Testing
5.2.3. Others
Chapter 6. Global Artificial Intelligence in Drug Discovery Market Size & Forecast by Therapeutic Area (2022-2032)
6.1. Segment Dashboard
6.2. Revenue Analysis by Therapeutic Area
6.2.1. Oncology
6.2.2. Neurodegenerative Diseases
6.2.3. Cardiovascular Diseases
6.2.4. Metabolic Diseases
6.2.5. Infectious Diseases
6.2.6. Others
Chapter 7. Global Artificial Intelligence in Drug Discovery Market Size & Forecast by Region (2022-2032)
7.1. North America
7.1.1. U.S.
7.1.2. Canada
7.1.3. Mexico
7.2. Europe
7.2.1. U.K.
7.2.2. Germany
7.2.3. France
7.2.4. Italy
7.2.5. Spain
7.2.6. Denmark
7.2.7. Sweden
7.2.8. Norway
7.3. Asia Pacific
7.3.1. Japan
7.3.2. China
7.3.3. India
7.3.4. South Korea
7.3.5. Australia
7.4. Latin America
7.4.1. Brazil
7.4.2. Argentina
7.5. Middle East & Africa
7.5.1. South Africa
7.5.2. Saudi Arabia
7.5.3. UAE
7.5.4. Kuwait
Chapter 8. Competitive Intelligence
8.1. Key Company SWOT Analysis
8.1.1. IBM
8.1.2. Exscientia
8.1.3. Insilico Medicine
8.2. Top Market Strategies
8.3. Company Profiles
8.3.1. IBM
8.3.1.1. Key Information
8.3.1.2. Overview
8.3.1.3. Financial (Subject to Data Availability)
8.3.1.4. Product Summary
8.3.1.5. Market Strategies
8.3.2. Exscientia
8.3.3. Insilico Medicine
Chapter 9. Research Process
9.1. Research Process
9.1.1. Data Mining
9.1.2. Analysis
9.1.3. Market Estimation
9.1.4. Validation
9.1.5. Publishing
9.2. Research Attributes

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Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
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