AI in Drug Discovery Market Size, Share, Trends, and Forecast, 2026-2035 | Generative AI and Precision Medicine Drive 30.5% CAGR to USD 43.9 Billion

Dublin, Oct. 12, 2026 (GLOBE NEWSWIRE) -- "Artificial Intelligence in Drug Discovery Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2026-2035" has been added to ResearchAndMarkets.com's offering.

Global Artificial Intelligence in Drug Discovery Market to Reach USD 43.9 Billion by 2035

The Global Artificial Intelligence In Drug Discovery Market was valued at USD 3.1 billion in 2025 and is projected to reach USD 43.9 billion by 2035, expanding at a CAGR of 30.5%. Growth is being driven by pharmaceutical and biotechnology companies integrating advanced computational technologies into research and development workflows.

The increasing prevalence of complex and long-term health conditions is strengthening demand for innovative therapeutics and faster development processes. AI-driven discovery tools help organizations improve research efficiency, shorten timelines, enhance decision-making, and analyze extensive biomedical datasets. These technologies are increasingly applied to biological target identification, candidate compound optimization, novel molecular design, predictive modeling, and early-stage testing.

High costs and lengthy timelines associated with conventional drug research are encouraging companies to adopt AI platforms that improve productivity and analytical accuracy. Rising interest in precision medicine and personalized treatment is also increasing demand for intelligent solutions capable of evaluating complex biological, clinical, and chemical information. Additional market growth is supported by expanding digital healthcare infrastructure, biotechnology investment, and research focused on developing more transparent and reliable artificial intelligence models.

Software Segment Leads the Market

The software segment accounted for 67.9% of the market in 2025 and is forecast to grow at a CAGR of 30.2% from 2026-2035. Software platforms are central to the AI-driven drug discovery ecosystem, enabling researchers to manage large biomedical datasets, conduct computational simulations, perform molecular modeling, and generate predictive insights.

Ongoing advances in machine learning and deep learning architectures are improving the analytical capabilities of drug discovery software. Sophisticated algorithms support large-scale chemical simulations and help research teams identify promising therapeutic candidates more efficiently. As pharmaceutical organizations modernize their research operations, demand for scalable software platforms is expected to remain strong.

Machine Learning Accounts for a Dominant Share

The machine learning segment held 82.6% of the Global Artificial Intelligence in Drug Discovery Market in 2025. Machine learning technologies support the analysis of complex biological and chemical datasets, allowing researchers to identify patterns, assess molecular properties, and generate predictive insights for early-stage development decisions.

Applications include the evaluation of genomic information, molecular libraries, experimental results, clinical records, and real-world data. The increasing integration of these sources is supporting personalized treatment strategies and more informed therapeutic development. Advances in cloud computing and scalable data processing infrastructure are also enabling broader deployment of machine learning across pharmaceutical research environments.

North America Holds the Largest Regional Share

North America accounted for 47.7% of the market in 2025. Regional growth is supported by the rapid adoption of advanced digital technologies among pharmaceutical and biotechnology organizations, a well-developed innovation ecosystem, and substantial investment in biotechnology research.

Continued development of digital healthcare infrastructure is accelerating the deployment of AI-driven drug discovery platforms across the region. Supportive regulatory frameworks and collaboration among technology companies, life sciences organizations, and research institutions are also encouraging the responsible use of artificial intelligence in biomedical research.

Competitive Landscape and Company Strategies

Major companies operating in the Global Artificial Intelligence in Drug Discovery Market include Isomorphic Labs (Alphabet), Microsoft Corporation, NVIDIA Corporation, International Business Machines Corporation, Schrodinger, Recursion Pharmaceuticals, Insilico Medicine, BenevolentAI, Atomwise, Insitro, Deep Genomics, Iktos, Deargen, 9Bio Therapeutics, Aureka Biotechnologies, CellCodex Technology Limited, chAIron, DenovAI Biotech, Examol, Helical.AI, Orakl Oncology, and Therenia.

Market participants are investing in research and development to improve AI algorithms for molecular modeling, predictive analytics, and candidate identification. Strategic collaborations with pharmaceutical companies, biotechnology firms, and academic research institutions are helping accelerate therapeutic innovation. Companies are also expanding cloud-based computing infrastructure and high-performance data platforms to process complex biomedical information at scale. Additional priorities include automation, advanced analytics, and scalable machine learning models designed to optimize drug discovery workflows.

Comprehensive Market Analysis and Forecast

Key Attributes

Key Topics Covered

Chapter 1 Methodology and Scope

1.1 Market scope and definition

1.2 Research approach

1.3 Quality commitments

1.3.1 AI policy and data integrity commitment

1.3.1.1 Source consistency protocol

1.4 Research trail and confidence scoring

1.4.1 Research trail components

1.4.2 Scoring components

1.5 Data collection

1.5.1 Partial list of primary sources

1.6 Data mining sources

1.6.1 Paid sources

1.6.1.1 Sources, by region

1.7 Base estimates and calculations

1.7.1 Revenue share analysis

1.7.2 Base year calculation

1.8 Forecast model

1.9 Research transparency addendum

1.9.1 Source attribution framework

1.9.2 Quality assurance metrics

1.9.3 Our commitment to trust

Chapter 2 Executive Summary

2.1 Industry 360-degree synopsis

2.2 Market Trends

2.2.1 Business trends

2.2.2 Regional trends

2.2.3 Component trends

2.2.4 Technology trends

2.2.5 Application type trends

2.2.6 Therapeutic area trends

2.2.7 End use trends

2.3 CXO perspectives: Strategic imperatives

Chapter 3 Industry Insights

3.1 Industry ecosystem analysis

3.2 Industry impact forces

3.2.1 Growth drivers

3.2.1.1 Increasing prevalence of complex and chronic diseases

3.2.1.2 Data explosion and digitization in healthcare

3.2.1.3 Advancements in AI algorithms and computing power

3.2.1.4 Growing collaboration between tech and pharma companies

3.2.2 Industry pitfalls and challenges

3.2.2.1 Data quality and integration issues

3.2.2.2 Regulatory and ethical concerns

3.2.3 Market opportunities

3.2.3.1 Expansion of personalized and precision medicine

3.2.3.2 Emergence of generative AI in molecule design

3.3 Growth potential analysis

3.4 Regulatory landscape (Driven by Primary Research)

3.4.1 North America

3.4.1.1 U.S.

3.4.1.2 Canada

3.4.2 Europe

3.4.3 Asia-Pacific

3.4.4 Latin America

3.4.5 Middle East and Africa

3.5 Future market trends (Driven by Primary Research)

3.6 Technological landscape

3.6.1 Current technologies

3.6.2 Emerging technologies

3.7 Investment and funding landscape

3.8 Impact of AI and generative AI on the market

3.9 Porter's analysis

3.10 PESTEL analysis

Chapter 4 Competitive Landscape, 2025

4.1 Introduction

4.2 Company market share analysis

4.2.1 North America

4.2.2 Europe

4.2.3 Asia-Pacific

4.3 Company matrix analysis

4.4 Competitive analysis of major market players

4.5 Competitive positioning matrix

4.6 Key developments

4.6.1 Merger and acquisition

4.6.2 Partnership and collaboration

4.6.3 New product launches

4.6.4 Expansion plans

Chapter 5 Market Estimates and Forecast, by Component, 2022-2035 ($ Mn)

5.1 Key trends

5.2 Software

5.2.1 On-premise

5.2.2 Cloud-based

5.3 Services

Chapter 6 Market Estimates and Forecast, by Technology, 2022-2035 ($ Mn)

6.1 Key trends

6.2 Machine learning

6.2.1 Deep learning

6.2.2 Supervised learning

6.2.3 Unsupervised learning

6.2.4 Other machine learning technologies

6.3 Other technologies

Chapter 7 Market Estimates and Forecast, by Application Type, 2022-2035 ($ Mn)

7.1 Key trends

7.2 Molecular library screening

7.3 Target identification

7.4 Drug optimization and repurposing

7.5 De novo drug designing

7.6 Preclinical testing

Chapter 8 Market Estimates and Forecast, by Therapeutic Area, 2022-2035 ($ Mn)

8.1 Key trends

8.2 Oncology

8.3 Neurodegenerative diseases

8.4 Inflammatory

8.5 Infectious diseases

8.6 Metabolic diseases

8.7 Rare diseases

8.8 Cardiovascular diseases

8.9 Other therapeutic areas

Chapter 9 Market Estimates and Forecast, by End Use, 2022-2035 ($ Mn)

9.1 Key trends

9.2 Pharmaceutical and biotechnology companies

9.3 Contract research organizations (CROs)

9.4 Other end users

Chapter 10 Market Estimates and Forecast, by Region, 2022-2035 ($ Mn)

10.1 Key trends

10.2 North America

10.2.1 U.S.

10.2.2 Canada

10.3 Europe

10.3.1 Germany

10.3.2 UK

10.3.3 France

10.3.4 Spain

10.3.5 Italy

10.3.6 Netherlands

10.4 Asia-Pacific

10.4.1 China

10.4.2 Japan

10.4.3 India

10.4.4 Australia

10.4.5 South Korea

10.5 Latin America

10.5.1 Brazil

10.5.2 Mexico

10.5.3 Argentina

10.6 Middle East and Africa

10.6.1 South Africa

10.6.2 Saudi Arabia

10.6.3 UAE

Chapter 11 Company Profiles

11.1 Isomorphic Labs (Alphabet)

11.2 Microsoft Corporation

11.3 NVIDIA Corporation

11.4 International Business Machines Corporation

11.5 Schrodinger

11.6 Recursion Pharmaceuticals

11.7 Insilico Medicine

11.8 BenevolentAI

11.9 Atomwise

11.10 Insitro

11.11 Deep Genomics

11.12 Iktos

11.13 Deargen

11.14 9 Bio Therapeutics

11.15 Aureka Biotechnologies

11.16 CellCodex Technology Limited

11.17 chAIron

11.18 DenovAI Biotech

11.19 Examol

11.20 Helical.AI

11.21 Orakl Oncology

11.22 Therenia

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