AI in Drug Discovery Market Analysis and Forecast to 2035: Offering, Services, Type, Deployment, Technology, Application, End User, Therapeutic Area

AI in Drug Discovery Market is anticipated to expand from $2.8 billion in 2025 to $14.0 billion by 2035, growing at a CAGR of approximately 16.1%. The AI-driven drug discovery market in 2025 is witnessing rapid growth, fueled by strong government support, strategic collaborations, innovative startups, and increasing global adoption. Governments are actively promoting AI integration in drug development: China has prioritized AI drug discovery in its 2025 Five-Year Plan, with funding from local pharmaceutical hubs such as Shanghai supporting biotech ventures; the U.S. Department of Health and Human Services (HHS) released a strategic plan to enhance efficiency and innovation in health services, including drug discovery; and the UK's MHRA introduced the "AI Airlock," a regulatory sandbox facilitating safe development and approval of AI-driven medical devices and diagnostics.

Industry collaborations are also driving market momentum, exemplified by XtalPi and DoveTree's $6 billion partnership targeting oncology, immunology, neurological, and metabolic diseases, Almirall and Absci expanding AI drug creation to dermatology, and Servier's extended collaboration with Google Cloud to leverage AI and generative AI in R&D.

Startups are increasingly contributing to innovation, with AMPLY Discovery securing $1.75 million to develop therapies for aggressive cancers, Atomic AI raising $42 million to advance RNA-based therapeutics, and Israel's AION Labs emerging as a key player in pharmaceutical AI.

The market is supported by flexible pricing models, including annual license fees ranging from $500,000 to several million dollars, tiered access, and volume-based pricing, allowing companies to balance AI infrastructure costs with scalable adoption. Investment trends remain robust, with venture capital funding rebounding in 2024, Ignota Labs reducing drug development timelines to under two years at costs below $1 million, and AMD investing $20 million in Absci to support biologics innovation.

Regionally, China is experiencing a surge in domestic AI-driven drug discovery as companies like Shanghai Titan Scientific and Nanjing Vazyme Biotech capitalize on local reagent supply, while Chinese firms accounted for 32% of global biotech licensing deal value in Q1 2025. In India, market growth is driven by rising chronic disease prevalence and adoption of AI and ML tools by pharmaceutical and biotechnology companies. Meanwhile, the UK's £108 billion biotech sector faces pricing and investment challenges despite government initiatives such as a £600 million Health Data Research Service and a £520 million manufacturing investment, prompting some firms to explore opportunities abroad. Hence, the AI drug discovery market is characterized by accelerating innovation, substantial investments, and increasing global adoption across both established and emerging pharmaceutical hubs.

Segment Overview

Based on technology, the AI in drug discovery market is segmented into Machine Learning, Computer Vision, Context Awareness Computing, Natural Language Processing. In 2024, Machine Learning (ML) accounted for 35.9% of the AI in drug discovery market, highlighting its ability to process both structured and unstructured biomedical data efficiently. Traditional ML methods, such as gradient-boosted trees, are useful for straightforward tasks, while advanced approaches like graph neural networks help identify complex patterns in protein-ligand interactions. The growth of this segment is driven by the increasing use of ML in key stages of drug discovery, including target identification and preclinical testing. For example, BenevolentAI uses ML to analyze large datasets, allowing researchers to find potential drug candidates faster and more accurately. In February 2025, Merck expanded its internal generative AI platform, built on Amazon SageMaker, to accelerate clinical study report production and streamline decision-making in drug development. The platform uses AWS DataSync to efficiently ingest and process large datasets, reducing time and costs in critical-path clinical activities. ML is also applied in bioinformatics to discover new biomarkers and therapeutic targets, supporting personalized medicine approaches. In virtual screening, ML helps researchers quickly evaluate large chemical libraries to identify promising compounds. Cloud-based ML platforms, such as Google Cloud AI, further support collaboration by providing scalable computing power and shared data resources, enabling faster and more efficient drug development.

The application segment includes various areas such as Drug Optimization and Repurposing, Preclinical Testing, Target Identification, Others. Among these, the preclinical testing segment is expected to grow at a CAGR of 21.1% from 2025 to 2034, driven by the increasing use of AI to improve early-stage drug development. Preclinical testing involves evaluating drug candidates for safety, efficacy, and toxicity before human trials, a process that is traditionally time-consuming and expensive. AI technologies, including machine learning, deep learning, and computer vision, are being leveraged to simulate biological processes, analyze large datasets, and predict drug behavior more efficiently. In June 2025, MIT and Recursion Pharmaceuticals released the Boltz-2 AI model, a next-generation system that achieves best-in-class accuracy in modeling complex molecular structures and predicting binding affinities, enhancing preclinical candidate selection. In July 2025, Tahoe Therapeutics secured $30 million in funding, led by Amplify Partners, to develop AI-driven human cell models. The initiative aims to generate one billion single-cell data points and map one million drug-patient interactions, accelerating safety and efficacy testing in preclinical research. These advancements are reducing timelines and costs, making preclinical testing the fastest-growing application segment in AI-driven drug discovery.

Geographical Overview

In 2024, North America accounted for the largest share of the AI in drug discovery market, holding 53.5% of the global market. This growth is driven by the increasing demand for innovative drug development processes and the integration of AI in research and clinical trials. In February 2024, Ginkgo Bioworks acquired Reverie Labs' AI/ML tools to strengthen AI-driven discovery services and develop next-generation biological foundation models. Similarly, in November 2023, Brainomix, specializing in AI-powered software for precision medicine, expanded into the U.S., while in May 2023, Google introduced AI solutions to streamline drug discovery and precision medicine for biotech and pharmaceutical firms. Academic contributions, such as Stanford University's Artificial Intelligence for Structure-Based Drug Discovery program, are enhancing safe and effective drug design. Supportive regulatory frameworks, including the FDA's AI/ML-Based Software as a Medical Device (SaMD) Action Plan, are fostering innovation while ensuring safety and efficacy. Additionally, U.S. funding for drug discovery and biotechnology reached 72% of total biotech investment in 2022, further driving market expansion and AI adoption across the region.

The Asia-Pacific region is expected to be the fastest-growing market for AI in drug discovery, with a projected CAGR of 24.9% from 2025 to 2034. Growth is driven by rising healthcare demand, increasing R&D investments, and adoption of AI technologies to accelerate drug discovery and clinical trials. China is rapidly emerging as a powerhouse, filing thousands of AI-driven drug discovery patents and accounting for 32% of global biotech licensing deal value in Q1 2025, up from 21% in 2023 and 2024. Strong government backing, large investments, and access to extensive population data are fueling this surge. In India, the Department of Biotechnology (DBT) and BIRAC actively promote AI, organizing Bio-AI workshops and issuing proposals under the BioE3 policy in 2025 to advance high-performance biomanufacturing and AI applications. Companies like Fujitsu and RIKEN collaborated in 2023 to develop AI-driven technology integrating generative AI with electron microscope imagery for protein structure prediction. Additionally, Japanese companies are investing in AI to enhance drug discovery and personalized therapies, with startups like CytoReason entering Japan through partnerships with Summit Pharmaceuticals. These developments position Asia-Pacific as a rapidly evolving hub for AI-powered drug discovery.

Key Trends and Drivers

Growing Focus on Collaborations & Partnerships -

The adoption of AI in drug discovery is rapidly accelerating, driven by the increasing recognition of its potential across pharmaceutical and biotechnology sectors. Companies are integrating AI not only in drug discovery but also in clinical development, safety monitoring, and risk assessment. To strengthen their competitive position, major players are increasingly forming strategic partnerships and collaborations with AI technology providers, enabling access to advanced AI-driven platforms and fostering innovation. For example, in September 2023, Merck partnered with BenevolentAI UK to leverage its sophisticated AI platform, collaborating with multidisciplinary experts to identify promising preclinical candidates and develop novel compounds. Similarly, Intelligent OMICS Ltd joined forces with Janssen Research & Development, LLC to co-develop Intellomx's AI platform, combining multi-omics data with deep learning to reveal new disease mechanisms and therapeutic opportunities. In January 2023, BioNTech acquired InstaDeep for $440 million, significantly enhancing its AI-powered drug discovery capabilities. This acquisition brought in approximately 290 professionals specializing in AI, machine learning, bioengineering, data science, and software development, strengthening BioNTech's capacity to drive innovation and accelerate the development of next-generation therapeutics.

Growth of AI-Powered Drug Discovery Startups -

Startups developing AI-powered solutions are driving significant growth in the drug discovery sector. By leveraging artificial intelligence and machine learning, these companies accelerate drug development, cut costs, and improve the precision of identifying promising drug candidates. AI integration with biological data helps streamline processes like target identification, molecule screening, and clinical trial optimization. Key players include Insilico Medicine, which uses AI to design and optimize novel drug candidates, Exscientia, employing AI-driven automation for personalized medicines and higher clinical trial success, and Atomwise, which applies deep learning to efficiently predict drug interactions. These startups attract substantial investments from venture capitalists and pharmaceutical companies, fostering innovation and collaboration. Notable examples include Xaira Therapeutics, which received $1 million in funding in April 2024, and BioNTech's acquisition of AI-driven startup InstaDeep for $440 million in 2023. Through partnerships and technological advancements, AI-powered startups are transforming drug discovery, improving efficiency, reducing costs, and enhancing patient outcomes.

Biotech Firms Driving Growth Through AI-Powered Drug Discovery -

The biotechnology industry is experiencing significant growth, creating major opportunities for AI integration in drug discovery. The United States remains the global hub, hosting over 2,000 private and public companies, while India's biotechnology sector has rapidly expanded, contributing 4.25% to GDP and projected to reach $150 billion by 2025 and $270-300 billion by 2030. According to IBISWorld, 3,429 biotech businesses were operating in the U.S. as of 2023. Biotechnology drives innovation in product development, particularly in the biopharmaceutical industry, with biopharmaceuticals being the fastest-growing segment due to their efficacy and ability to treat previously untreatable diseases. In 2025, companies such as Exscientia, Atomwise, Recursion Pharmaceuticals, Chai Discovery, Antiverse, Relay Therapeutics, Xaira, LTZ Therapeutics, and Mount Sinai are leveraging AI to design molecules, predict molecular structures, and develop therapies for cancer, rare diseases, and immuno-oncology. These AI-driven innovations streamline drug development, reduce timelines, and unlock new treatment possibilities, positioning the biotechnology sector for transformative advancements globally and in India.

Research Scope

• Estimates and forecasts the overall market size across type, application, and region.
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• Identifies factors influencing market growth and challenges, opportunities, drivers, and restraints.
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• Evaluates key development strategies like acquisitions, product launches, mergers, collaborations, business expansions, agreements, partnerships, and R&D activities.
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