Generative AI in Drug Discovery Market By Technology (Machine Learning, Deep Learning, Natural Language Processing, Reinforcement Learning), By Application (Drug Discovery, Drug Repurposing, Clinical Trials, Preclinical Testing, Personalized Medicine), By End-User (Pharmaceutical Companies, Biotech Companies, Research Institutes, Contract Research Organizations, Academic & Government Institutions), and By Region; Global Insights & Forecast (2023 – 2030)

As per Intent Market Research, the Generative AI in Drug Discovery Market was valued at USD 1.3 billion in 2024-e and will surpass USD 12.1 billion by 2030; growing at a CAGR of 37.6% during 2025 - 2030.

The generative AI in drug discovery market is evolving rapidly, driven by the increasing demand for faster, more efficient drug development processes. Artificial intelligence technologies such as machine learning, deep learning, and natural language processing are enabling researchers to discover new drugs, repurpose existing ones, and streamline clinical trials. These technologies are revolutionizing drug discovery by accelerating the identification of promising drug candidates, reducing costs, and improving the success rate of clinical trials. With the growing emphasis on personalized medicine and precision therapies, generative AI is playing a pivotal role in transforming how drugs are developed, tested, and brought to market.

Technology Segment is Largest Owing to Machine Learning

In the technology segment, machine learning (ML) is the largest subsegment driving growth in the generative AI in drug discovery market. Machine learning algorithms are widely used to analyze large datasets, identify patterns, and make predictions that are crucial in drug discovery. These algorithms help researchers to sift through enormous amounts of chemical and biological data, enabling faster identification of potential drug candidates. Machine learning is particularly effective in predicting the activity, toxicity, and metabolism of new compounds, which significantly accelerates the drug development process.

The continuous advancements in machine learning, including supervised and unsupervised learning models, are empowering pharmaceutical companies to create more accurate predictive models. These models are now essential in various stages of drug discovery, from identifying drug targets to optimizing molecular structures. With ML’s ability to process complex datasets in real-time and continually improve predictions, its application is expected to grow even further in the coming years, solidifying its position as the dominant technology in the market.

Application Segment is Fastest Growing Owing to Drug Repurposing

The drug repurposing application is the fastest-growing subsegment within the generative AI in drug discovery market. Drug repurposing, also known as drug repositioning, involves identifying new therapeutic uses for existing drugs. AI technologies, particularly deep learning and natural language processing, have made drug repurposing more efficient by analyzing vast amounts of biomedical data, including clinical trials, medical records, and scientific literature. Generative AI algorithms can uncover hidden relationships between existing drugs and diseases, significantly reducing the time and cost required for bringing new treatments to market.

As the demand for quicker solutions to public health crises increases, drug repurposing has gained significant attention, especially for rapidly emerging diseases. The ability to quickly identify candidates for repurposing has proven valuable in areas such as oncology, infectious diseases, and neurological disorders, where new treatments are often in high demand. AI is enhancing this process by predicting how well existing drugs may work against diseases they were not initially designed to treat, thereby improving the efficiency of repurposing efforts.

End-User Segment is Largest Owing to Pharmaceutical Companies

The pharmaceutical companies segment is the largest subsegment within the end-user category for generative AI in drug discovery. Pharmaceutical companies are major adopters of AI technologies due to their need to reduce the time and cost associated with drug development while improving the likelihood of success in clinical trials. These companies leverage AI to optimize drug discovery workflows, predict drug efficacy, and identify novel drug candidates. With large-scale, complex datasets at their disposal, pharmaceutical companies are utilizing AI to streamline the discovery process, making it more cost-effective and efficient.

Furthermore, pharmaceutical companies are heavily investing in AI-driven solutions to accelerate the development of personalized medicines, where AI can identify the best treatments for individual patients based on genetic, clinical, and environmental data. As the pharmaceutical sector continues to focus on innovation and efficiency, the use of AI in drug discovery is becoming indispensable, driving growth in this end-user segment.

Region Segment is Fastest Growing in North America

Regionally, North America is the fastest-growing region in the generative AI in drug discovery market. The region is home to numerous leading pharmaceutical and biotechnology companies, as well as academic and research institutions that are increasingly adopting AI technologies for drug discovery. North America benefits from a well-established healthcare infrastructure, substantial investment in healthcare research, and a strong focus on technological innovation. This combination of factors has positioned North America as the leader in the generative AI market, with the U.S. being particularly active in advancing AI applications in drug discovery.

Moreover, government initiatives and private sector collaborations in North America are pushing the boundaries of AI-driven drug discovery. Research institutions and contract research organizations (CROs) are at the forefront of utilizing AI to accelerate drug development, and the region is expected to maintain a dominant position due to its ongoing advancements in technology and regulatory support for AI-based medical research.

Leading Companies and Competitive Landscape

The generative AI in drug discovery market is highly competitive, with several leading companies driving technological innovations and forming strategic partnerships. IBM Watson Health, BenevolentAI, Insilico Medicine, Exscientia, and Recursion Pharmaceuticals are among the top companies making significant strides in the AI-driven drug discovery space. These companies are leveraging advanced AI algorithms to accelerate drug discovery, identify novel therapeutic targets, and optimize clinical trials. Many of them are collaborating with pharmaceutical giants to bring AI solutions to mainstream drug development, enabling them to scale their technologies across multiple therapeutic areas.

The competitive landscape is also marked by ongoing mergers and acquisitions, as companies seek to strengthen their AI capabilities and expand their portfolios. Additionally, partnerships between AI-focused startups and large pharmaceutical companies are common, as both parties look to combine AI-driven innovation with industry expertise. With the market's rapid growth, leading companies are expected to continue pushing the boundaries of AI in drug discovery, driving innovation and improving the efficiency of drug development processes.

Recent Developments:

• BenevolentAI partnered with a major pharmaceutical company to apply AI-driven approaches to the discovery of new drugs for diseases with unmet needs, enhancing the drug development pipeline.
• Moderna announced the use of machine learning algorithms to optimize vaccine design, expanding its AI capabilities beyond traditional mRNA vaccine platforms.
• Exscientia raised new funding to expand its AI-driven drug discovery platform, focusing on increasing its portfolio of small molecules in preclinical testing.
• Insilico Medicine received regulatory approval for a generative AI platform used in the design of novel antiviral drugs, paving the way for accelerated drug development.
• Schrodinger launched a new software tool for drug discovery leveraging AI models to predict protein-ligand binding, enhancing precision in drug design and testing.

List of Leading Companies:

• IBM Corporation
• Atomwise
• BenevolentAI
• Insilico Medicine
• Exscientia
• Recursion Pharmaceuticals
• Healx
• Exscientia
• Moderna
• Schrodinger
• Biogen
• Novartis
• GSK
• Pfizer
• Eli Lilly

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