(MENAFN- GlobeNewsWire - Nasdaq) Opportunities span personalized medicine, generative AI, cloud platforms and pharma-tech partnerships, with target discovery, virtual screening and lead optimization accelerating development.Dublin, Sept. 16, 2026 (GLOBE NEWSWIRE) -- "AI in Drug Discovery Market Report 2026" has been added to ResearchAndMarkets.com's offering.
Artificial Intelligence in Drug Discovery Market Projected to Reach $7.42 Billion by 2030
The global artificial intelligence (AI) in drug discovery market is experiencing exponential growth as pharmaceutical and biotechnology organizations adopt advanced technologies to accelerate research, identify promising drug candidates and improve development outcomes. The market is projected to increase from $2.33 billion in 2025 to $2.93 billion in 2026, representing a compound annual growth rate (CAGR) of 25.9%.
Historic market growth has been supported by rising pharmaceutical research and development spending, increasingly complex drug discovery pipelines and the expanding availability of biological and chemical datasets. Early adoption of machine learning in bioinformatics, combined with stronger collaboration between pharmaceutical businesses and technology providers, has also contributed to the wider implementation of AI-powered drug discovery platforms.
The artificial intelligence in drug discovery market is forecast to reach $7.42 billion by 2030, expanding at a CAGR of 26.2%. Key growth drivers include greater adoption of generative AI models, increasing demand for precision therapeutics, wider use of AI-enabled clinical trial design and growing investment in cloud-based drug discovery platforms. Regulatory acceptance of AI-assisted drug development is also expected to support market expansion.
Prominent AI in drug discovery market trends include the increasing use of AI-driven target identification, deep learning for molecular screening and virtual drug screening platforms. Drug developers are also deploying AI-based lead optimization tools to assess and refine potential compounds more efficiently. These technologies are expected to help reduce drug development timelines, improve candidate selection and support more informed research decisions throughout the discovery pipeline.
Growing demand for personalized medicine is a major factor driving the market. The increasing focus on patient-specific therapies reflects the need to improve treatment effectiveness while reducing adverse effects. AI supports personalized drug development by accelerating molecular target identification, optimizing drug candidates and enhancing clinical decision-making. According to the Personalized Medicine Coalition, the U.S. Food and Drug Administration approved 26 new personalized treatments in 2023, comprising 20 new molecular entities and six gene- or cell-based therapies.
Investment activity is further strengthening the AI in drug discovery industry. Pharmaceutical, biotechnology and technology organizations are directing capital toward AI platforms that can identify new therapeutic opportunities and provide scalable discovery models. In July 2023, NVIDIA invested $50 million in Recursion Pharmaceuticals. The investment was intended to accelerate the development of AI foundation models for biology and chemistry through NVIDIA's cloud services, supporting Recursion's technology-enabled drug discovery programs.
Strategic partnerships are also advancing AI-driven pharmaceutical innovation. In November 2025, Merck KGaA partnered with Valo Health to accelerate drug discovery in neurology. The collaboration is designed to strengthen Merck KGaA's neurology pipeline by using Valo Health's AI platform to identify novel targets and develop preclinical candidates for Parkinson's disease and related disorders. The initiative combines large-scale human data, machine learning and automated chemistry to support the generation of preclinical small-molecule candidates.
North America was the largest region in the artificial intelligence in drug discovery market in 2025, while Western Europe ranked as the second-largest regional market. The geographic coverage of the market includes Asia-Pacific, Southeast Asia, Western Europe, Eastern Europe, North America, South America, the Middle East and Africa. Countries covered include Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, the United Kingdom, the United States, Canada, Italy and Spain.
Markets Covered:
1) By Technology: Context-Aware Processing; Natural Language Processing; Deep Learning
2) By Drug Type: Small Molecules; Large Molecules
3) By Therapeutic Type: Metabolic Disease; Cardiovascular Disease; Oncology; Neurodegenerative Diseases; Anti-Infective Diseases; Respiratory Diseases; Other Therapeutics
4) By End User: Pharmaceutical Companies; Biopharmaceutical Companies; Academic And Research Institutes; Other End-Users
Subsegments:
1) By Context-Aware Processing: Contextual Data Integration; Predictive Modeling; Adaptive Learning Systems
2) By Natural Language Processing (NLP): Text Mining And Analysis; Drug Interaction Extraction; Clinical Trial Data Analysis
3) By Deep Learning: Neural Networks; Convolutional Neural Networks (CNNs); Recurrent Neural Networks (RNNs); Reinforcement Learning Applications
Time Series: Five years historic and ten years forecast.
Data: Ratios of market size and growth to related markets, GDP proportions, expenditure per capita.
Data Segmentation: Country and regional historic and forecast data, market share of competitors, market segments.
Key Attributes:
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