Ai In Drug Discovery Market
AI in Drug Discovery Market Forecasts to 2032 - Global Analysis By Type (Preclinical and Clinical Testing, Molecule Screening, Target Identification and De Novo Drug Design), Drug Type, Offering, Technology, Application, End User and By Geography
|
Years Covered |
2024-2032 |
|
Estimated Year Value (2025) |
US $2.6 BN |
|
Projected Year Value (2032) |
US $17.8 BN |
|
CAGR (2025-2032) |
31.7% |
|
Regions Covered |
North America, Europe, Asia Pacific, South America, and Middle East & Africa |
|
Countries Covered |
US, Canada, Mexico, Germany, UK, Italy, France, Spain, Japan, China, India, Australia, New Zealand, South Korea, Rest of Asia Pacific, South America, Argentina, Brazil, Chile, Middle East & Africa, Saudi Arabia, UAE, Qatar, and South Africa |
|
Largest Market |
Asia Pacific |
|
Highest Growing Market |
North America |
According to Stratistics MRC, the Global AI in Drug Discovery Market is accounted for $2.6 billion in 2025 and is expected to reach $17.8 billion by 2032 growing at a CAGR of 31.7% during the forecast period. Artificial Intelligence (AI) in drug discovery refers to the application of machine learning and data-driven algorithms to accelerate and optimize the process of developing new drugs. AI can analyze vast datasets—from molecular structures to clinical trial results—to identify promising drug candidates, predict drug-target interactions, and even design novel compounds. It reduces the time, cost, and failure rate associated with traditional drug development methods. By simulating biological systems and learning from existing data, AI helps researchers uncover patterns and make decisions with greater precision.
According to the estimates by WHO, in 2022, 20 million new cancer cases and 9.7 million deaths were reported globally.
Market Dynamics:
Driver:
Rising R&D Costs and Time Pressure
Rising R&D costs and time pressure are accelerating the adoption of AI in drug discovery, acting as catalysts for innovation. These challenges push pharmaceutical companies to embrace AI-driven solutions that streamline target identification, optimize clinical trials, and reduce costly failures. As a result, AI enhances R&D productivity, shortens development timelines, and improves success rates. This urgency fosters investment in intelligent technologies, transforming traditional workflows and enabling faster, more cost-effective drug development to meet growing healthcare demands.
Restraint:
Lack of Standardized, High-Quality Data
The lack of standardized, high-quality data severely hampers AI’s effectiveness in drug discovery. Inconsistent formats, incomplete annotations, and biased datasets compromise model accuracy and reproducibility. These data issues lead to flawed predictions, increased development costs, and delayed timelines. Without harmonized data, AI struggles to identify viable drug candidates or predict outcomes reliably, limiting its transformative potential and widening the gap between research innovation and real-world pharmaceutical application.
Opportunity:
Explosion of Biomedical Data
The explosion of biomedical data is fueling a transformative leap in AI-driven drug discovery. With vast datasets from genomics, proteomics, and clinical records, AI models can now uncover hidden patterns, predict drug-target interactions, and accelerate lead identification. This data abundance enhances precision, reduces trial-and-error, and supports personalized medicine. As a result, pharmaceutical R&D becomes faster, more efficient, and cost-effective. The synergy between big data and AI is reshaping drug development into a smarter, data-powered frontier.
Threat:
High Implementation Costs
High implementation costs significantly hinder the adoption of AI in drug discovery, especially among small and mid-sized pharmaceutical firms. These expenses include advanced infrastructure, skilled personnel, and ongoing system maintenance. Such financial barriers delay integration, limit innovation, and widen the gap between large corporations and emerging players. As a result, the full potential of AI remains underutilized, slowing progress in developing faster, cost-effective, and personalized therapeutic solutions.
Covid-19 Impact
The COVID-19 pandemic significantly accelerated the adoption of AI in drug discovery, as pharmaceutical companies urgently sought faster, cost-effective solutions. AI tools were pivotal in identifying therapeutic targets, repurposing drugs, and optimizing vaccine development. This surge in demand led to increased investments, collaborations, and integration of AI platforms across R&D pipelines. The pandemic ultimately highlighted AI’s transformative potential, establishing it as a critical asset in future pharmaceutical innovation and crisis response.
The oncology segment is expected to be the largest during the forecast period
The oncology segment is expected to account for the largest market share during the forecast period due to the urgent demand for precise, personalized cancer treatments. AI accelerates biomarker discovery, predicts therapeutic responses, and enhances clinical trial design, especially in complex cancers like lung and breast cancer. With oncology accounting for the largest share of AI drug discovery investments, it fosters innovation in targeted therapies and immuno-oncology. This synergy improves success rates, reduces development time, and positions AI as a transformative force in cancer research and treatment.
The deep learning segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the deep learning segment is predicted to witness the highest growth rate as it enables rapid analysis of complex biomedical data. Its ability to model intricate biological interactions accelerates target identification, optimizes compound screening, and enhances de novo drug design. Deep learning reduces development time and costs by improving prediction accuracy and minimizing trial failures. As pharmaceutical companies increasingly adopt these models, they unlock scalable, data-driven innovation—transforming drug discovery into a faster, more precise, and cost-effective process.
Region with largest share:
During the forecast period, the Asia Pacific region is expected to hold the largest market share due to robust R&D ecosystems, government support, and a surge in biotech startups. Countries like China, India, and Japan are leveraging AI to accelerate clinical trials, reduce costs, and enhance precision medicine. With vast genomic datasets and digital infrastructure, the region fosters innovation in oncology, immunology, and rare diseases. This momentum positions Asia Pacific as a global leader, transforming drug development into a faster, smarter, and more accessible process.
Region with highest CAGR:
Over the forecast period, the North America region is anticipated to exhibit the highest CAGR, owing to robust pharmaceutical infrastructure and leading tech innovators, the region leads global adoption. AI enables rapid compound screening, predictive modeling, and personalized medicine development. Strategic collaborations between biotech firms and AI startups are fueling innovation, while regulatory support fosters growth. This synergy is driving a projected market surge, positioning North America as a powerhouse in AI-driven pharmaceutical breakthroughs
Key players in the market
Some of the key players profiled in the AI in Drug Discovery Market include Atomwise, Inc., BenevolentAI, Insilico Medicine, Exscientia Ltd., Recursion Pharmaceuticals, BioXcel Therapeutics, Deep Genomics, Cloud Pharmaceuticals, Numerate, Inc., Cyclica Inc., Iktos, Evaxion Biotech, BERG LLC, Verge Genomics, Healx, PathAI, NVIDIA Corporation, IBM Watson Health, Google DeepMind and Schrödinger, Inc.
Key Developments:
In August 2022, Atomwise and Sanofi have launched a strategic, exclusive collaboration to harness Atomwise’s AtomNet® AI platform for structure-based drug discovery targeting up to five molecular targets.
In March 2020, Atomwise and Bridge Biotherapeutics struck potential $1?billion research collaboration, aiming to develop up to 13 AI-driven small-molecule programs targeting inflammation-related proteins, especially Pellino E3 ubiquitin ligases.
Types Covered:
• Preclinical and Clinical Testing
• Molecule Screening
• Target Identification
• De Novo Drug Design
Drug Types Covered:
• Small Molecules
• Large Molecules
Offerings Covered:
• Software
• Services
Technologies Covered:
• Machine Learning
• Deep Learning
• Natural Language Processing (NLP)
• Other Technologies
Applications Covered:
• Oncology
• Neurology
• Infectious Diseases
• Cardiovascular Diseases
• Metabolic Diseases
• Immunology
• Other Applications
End Users Covered:
• Pharmaceutical Companies
• Biotechnology Companies
• Academic & Research Institutes
• Contract Research Organizations (CROs)
• Other End Users
Regions Covered:
• North America
o US
o Canada
o Mexico
• Europe
o Germany
o UK
o Italy
o France
o Spain
o Rest of Europe
• Asia Pacific
o Japan
o China
o India
o Australia
o New Zealand
o South Korea
o Rest of Asia Pacific
• South America
o Argentina
o Brazil
o Chile
o Rest of South America
• Middle East & Africa
o Saudi Arabia
o UAE
o Qatar
o South Africa
o Rest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2022, 2023, 2024, 2026, and 2030
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Free Customization Offerings:
All the customers of this report will be entitled to receive one of the following free customization options:
• Company Profiling
o Comprehensive profiling of additional market players (up to 3)
o SWOT Analysis of key players (up to 3)
• Regional Segmentation
o Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
o Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances
Table of Contents
1 Executive Summary
2 Preface
2.1 Abstract
2.2 Stake Holders
2.3 Research Scope
2.4 Research Methodology
2.4.1 Data Mining
2.4.2 Data Analysis
2.4.3 Data Validation
2.4.4 Research Approach
2.5 Research Sources
2.5.1 Primary Research Sources
2.5.2 Secondary Research Sources
2.5.3 Assumptions
3 Market Trend Analysis
3.1 Introduction
3.2 Drivers
3.3 Restraints
3.4 Opportunities
3.5 Threats
3.6 Technology Analysis
3.7 Application Analysis
3.8 End User Analysis
3.9 Emerging Markets
3.10 Impact of Covid-19
4 Porters Five Force Analysis
4.1 Bargaining power of suppliers
4.2 Bargaining power of buyers
4.3 Threat of substitutes
4.4 Threat of new entrants
4.5 Competitive rivalry
5 Global AI in Drug Discovery Market, By Type
5.1 Introduction
5.2 Preclinical and Clinical Testing
5.3 Molecule Screening
5.4 Target Identification
5.5 De Novo Drug Design
6 Global AI in Drug Discovery Market, By Drug Type
6.1 Introduction
6.2 Small Molecules
6.3 Large Molecules
7 Global AI in Drug Discovery Market, By Offering
7.1 Introduction
7.2 Software
7.3 Services
8 Global AI in Drug Discovery Market, By Technology
8.1 Introduction
8.2 Machine Learning
8.2.1 Supervised Learning
8.2.2 Reinforcement Learning
8.2.3 Unsupervised Learning
8.3 Deep Learning
8.4 Natural Language Processing (NLP)
8.5 Other Technologies
9 Global AI in Drug Discovery Market, By Application
9.1 Introduction
9.2 Oncology
9.3 Neurology
9.4 Infectious Diseases
9.5 Cardiovascular Diseases
9.6 Metabolic Diseases
9.7 Immunology
9.8 Other Applications
10 Global AI in Drug Discovery Market, By End User
10.1 Introduction
10.2 Pharmaceutical Companies
10.3 Biotechnology Companies
10.4 Academic & Research Institutes
10.5 Contract Research Organizations (CROs)
10.6 Other End Users
11 Global AI in Drug Discovery Market, By Geography
11.1 Introduction
11.2 North America
11.2.1 US
11.2.2 Canada
11.2.3 Mexico
11.3 Europe
11.3.1 Germany
11.3.2 UK
11.3.3 Italy
11.3.4 France
11.3.5 Spain
11.3.6 Rest of Europe
11.4 Asia Pacific
11.4.1 Japan
11.4.2 China
11.4.3 India
11.4.4 Australia
11.4.5 New Zealand
11.4.6 South Korea
11.4.7 Rest of Asia Pacific
11.5 South America
11.5.1 Argentina
11.5.2 Brazil
11.5.3 Chile
11.5.4 Rest of South America
11.6 Middle East & Africa
11.6.1 Saudi Arabia
11.6.2 UAE
11.6.3 Qatar
11.6.4 South Africa
11.6.5 Rest of Middle East & Africa
12 Key Developments
12.1 Agreements, Partnerships, Collaborations and Joint Ventures
12.2 Acquisitions & Mergers
12.3 New Product Launch
12.4 Expansions
12.5 Other Key Strategies
13 Company Profiling
13.1 Atomwise, Inc.
13.2 BenevolentAI
13.3 Insilico Medicine
13.4 Exscientia Ltd.
13.5 Recursion Pharmaceuticals
13.6 BioXcel Therapeutics
13.7 Deep Genomics
13.8 Cloud Pharmaceuticals
13.9 Numerate, Inc.
13.10 Cyclica Inc.
13.11 Iktos
13.12 Evaxion Biotech
13.13 BERG LLC
13.14 Verge Genomics
13.15 Healx
13.16 PathAI
13.17 NVIDIA Corporation
13.18 IBM Watson Health
13.19 Google DeepMind
13.20 Schrödinger, Inc.
List of Tables
1 Global AI in Drug Discovery Market Outlook, By Region (2024-2032) ($MN)
2 Global AI in Drug Discovery Market Outlook, By Type (2024-2032) ($MN)
3 Global AI in Drug Discovery Market Outlook, By Preclinical and Clinical Testing (2024-2032) ($MN)
4 Global AI in Drug Discovery Market Outlook, By Molecule Screening (2024-2032) ($MN)
5 Global AI in Drug Discovery Market Outlook, By Target Identification (2024-2032) ($MN)
6 Global AI in Drug Discovery Market Outlook, By De Novo Drug Design (2024-2032) ($MN)
7 Global AI in Drug Discovery Market Outlook, By Drug Type (2024-2032) ($MN)
8 Global AI in Drug Discovery Market Outlook, By Small Molecules (2024-2032) ($MN)
9 Global AI in Drug Discovery Market Outlook, By Large Molecules (2024-2032) ($MN)
10 Global AI in Drug Discovery Market Outlook, By Offering (2024-2032) ($MN)
11 Global AI in Drug Discovery Market Outlook, By Software (2024-2032) ($MN)
12 Global AI in Drug Discovery Market Outlook, By Services (2024-2032) ($MN)
13 Global AI in Drug Discovery Market Outlook, By Technology (2024-2032) ($MN)
14 Global AI in Drug Discovery Market Outlook, By Machine Learning (2024-2032) ($MN)
15 Global AI in Drug Discovery Market Outlook, By Supervised Learning (2024-2032) ($MN)
16 Global AI in Drug Discovery Market Outlook, By Reinforcement Learning (2024-2032) ($MN)
17 Global AI in Drug Discovery Market Outlook, By Unsupervised Learning (2024-2032) ($MN)
18 Global AI in Drug Discovery Market Outlook, By Deep Learning (2024-2032) ($MN)
19 Global AI in Drug Discovery Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
20 Global AI in Drug Discovery Market Outlook, By Other Technologies (2024-2032) ($MN)
21 Global AI in Drug Discovery Market Outlook, By Application (2024-2032) ($MN)
22 Global AI in Drug Discovery Market Outlook, By Oncology (2024-2032) ($MN)
23 Global AI in Drug Discovery Market Outlook, By Neurology (2024-2032) ($MN)
24 Global AI in Drug Discovery Market Outlook, By Infectious Diseases (2024-2032) ($MN)
25 Global AI in Drug Discovery Market Outlook, By Cardiovascular Diseases (2024-2032) ($MN)
26 Global AI in Drug Discovery Market Outlook, By Metabolic Diseases (2024-2032) ($MN)
27 Global AI in Drug Discovery Market Outlook, By Immunology (2024-2032) ($MN)
28 Global AI in Drug Discovery Market Outlook, By Other Applications (2024-2032) ($MN)
29 Global AI in Drug Discovery Market Outlook, By End User (2024-2032) ($MN)
30 Global AI in Drug Discovery Market Outlook, By Pharmaceutical Companies (2024-2032) ($MN)
31 Global AI in Drug Discovery Market Outlook, By Biotechnology Companies (2024-2032) ($MN)
32 Global AI in Drug Discovery Market Outlook, By Academic & Research Institutes (2024-2032) ($MN)
33 Global AI in Drug Discovery Market Outlook, By Contract Research Organizations (CROs) (2024-2032) ($MN)
34 Global AI in Drug Discovery Market Outlook, By Other End Users (2024-2032) ($MN)
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.
List of Figures
RESEARCH METHODOLOGY
We at ‘Stratistics’ opt for an extensive research approach which involves data mining, data validation, and data analysis. The various research sources include in-house repository, secondary research, competitor’s sources, social media research, client internal data, and primary research.
Our team of analysts prefers the most reliable and authenticated data sources in order to perform the comprehensive literature search. With access to most of the authenticated data bases our team highly considers the best mix of information through various sources to obtain extensive and accurate analysis.
Each report takes an average time of a month and a team of 4 industry analysts. The time may vary depending on the scope and data availability of the desired market report. The various parameters used in the market assessment are standardized in order to enhance the data accuracy.
Data Mining
The data is collected from several authenticated, reliable, paid and unpaid sources and is filtered depending on the scope & objective of the research. Our reports repository acts as an added advantage in this procedure. Data gathering from the raw material suppliers, distributors and the manufacturers is performed on a regular basis, this helps in the comprehensive understanding of the products value chain. Apart from the above mentioned sources the data is also collected from the industry consultants to ensure the objective of the study is in the right direction.
Market trends such as technological advancements, regulatory affairs, market dynamics (Drivers, Restraints, Opportunities and Challenges) are obtained from scientific journals, market related national & international associations and organizations.
Data Analysis
From the data that is collected depending on the scope & objective of the research the data is subjected for the analysis. The critical steps that we follow for the data analysis include:
- Product Lifecycle Analysis
- Competitor analysis
- Risk analysis
- Porters Analysis
- PESTEL Analysis
- SWOT Analysis
The data engineering is performed by the core industry experts considering both the Marketing Mix Modeling and the Demand Forecasting. The marketing mix modeling makes use of multiple-regression techniques to predict the optimal mix of marketing variables. Regression factor is based on a number of variables and how they relate to an outcome such as sales or profits.
Data Validation
The data validation is performed by the exhaustive primary research from the expert interviews. This includes telephonic interviews, focus groups, face to face interviews, and questionnaires to validate our research from all aspects. The industry experts we approach come from the leading firms, involved in the supply chain ranging from the suppliers, distributors to the manufacturers and consumers so as to ensure an unbiased analysis.
We are in touch with more than 15,000 industry experts with the right mix of consultants, CEO's, presidents, vice presidents, managers, experts from both supply side and demand side, executives and so on.
The data validation involves the primary research from the industry experts belonging to:
- Leading Companies
- Suppliers & Distributors
- Manufacturers
- Consumers
- Industry/Strategic Consultants
Apart from the data validation the primary research also helps in performing the fill gap research, i.e. providing solutions for the unmet needs of the research which helps in enhancing the reports quality.
For more details about research methodology, kindly write to us at info@strategymrc.com
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