Artificial Intelligence in Drug Discovery Market Size, Growth, Share & Trends Analysis

Based on process, the hit-to-lead identification/lead generation segment accounted for the largest share of 29.2% of the Al in drug discovery market in 2023. This is due to the growing adoption of AI-driven virtual screening, predictive modeling, and generative chemistry tools that significantly accelerate the identification of viable lead compounds. AI enables rapid evaluation of large chemical libraries, improves hit quality, and reduces experimental workload. As a result, biopharma companies increasingly rely on AI platforms to shorten timelines and enhance the efficiency of early-stage discovery.

In 2023, the De novo drug design accounted for the largest market share of 31.7% in 2023. This dominance is driven by the the increasing use of AI algorithms such as generative models and deep learning to create entirely new molecular structures with optimized drug-like properties. These technologies enable rapid exploration of large chemical spaces, reduce dependence on existing compound libraries, and enhance the efficiency of early discovery. Consequently, pharmaceutical and biotech companies are adopting AI-driven de novo design to accelerate innovation and identify promising candidates.

In 2023, oncology segment accounted for the largest share of the AI in drug discovery market. This is largely due to the growing need for more effective cancer therapies and the high volume of genomic, clinical, and molecular data generated in oncology research. AI tools help identify novel targets, predict drug responses, and design precision treatments more efficiently than traditional methods. As cancer remains a leading global health burden, biopharma companies are increasingly leveraging AI to accelerate the development of targeted and personalized oncology drugs.

By player type, the end-to-end solution providers segment accounted for the largest share of the AI in drug discovery market due to its ability to offer fully integrated platforms that streamline the entire R&D workflow. These providers combine data management, target identification, molecular design, predictive modeling, and optimization tools within a single ecosystem, reducing the need for multiple vendors. Their comprehensive capabilities enable faster decision-making, improved accuracy, and greater operational efficiency, leading pharmaceutical and biotech companies to increasingly adopt end-to-end AI solutions.

By AI Tool, the machine learning segment is expected to dominate the market, growing at the highest CAGR during the forecast period due to its strong ability to analyze vast chemical and biological datasets, uncover complex patterns, and accurately predict drug behavior. ML models enhance target identification, optimize lead compounds, and reduce experimental failures by improving decision-making at early discovery stages. As adoption of deep learning, generative models, and predictive analytics accelerates, machine learning continues to drive faster, more efficient drug development.

By Deployment, the cloud-based deployment segment accounted for the largest share of the AI in drug discovery market. This is driven by the need for scalable computing power, seamless data integration, and cost-efficient access to advanced AI tools without heavy on-premise infrastructure. Cloud platforms enable faster model training, real-time collaboration, and secure storage of large genomic and chemical datasets. As drug discovery workflows become increasingly data-intensive, organizations are adopting cloud-based solutions to enhance flexibility, speed, and overall R&D productivity.

By end user, the pharmaceutical and biotechnology companies segment accounted for the largest share in 2023, driven by the growing need to accelerate R&D, reduce development costs, and improve drug discovery success rates. These companies increasingly rely on AI to enhance target identification, optimize lead compounds, and predict safety and efficacy earlier in the pipeline. The rising adoption of AI-based platforms, expanding investments in digital transformation, and strong focus on developing precision and biologic therapies further support their leading market position.