AI in Drug Discovery Market to Reach $17.56 Billion by 2031, Driven by Generative AI Adoption and Rising Pharma R&D Investments

AI in Drug Discovery Market by Process (Target, Lead), Use Case (Repurposing, DE Novo Drug Design [Vaccine] Optimization, Disease Understanding, PK/PD), Therapy (Cancer, CNS), Tool (DL [CNN, GAN]), End User - Global Forecast to 2031
According to MarketsandMarkets Trademark , The global AI in drug discovery market [https://www.marketsandmarkets.com/Market-Reports/ai-in-drug-discovery-market-151193446.html?utm_source=abnewswire.com&utm_medium=referral&utm_campaign=paidpr] is projected to grow from USD 5.09 billion in 2026 to USD 17.56 billion by 2031, at a CAGR of 28.1% during the forecast period. This rapid growth is driven by increasing pharmaceutical investments in AI-enabled research and development, rising adoption of generative AI and foundation models, and the need to reduce drug discovery timelines and costs.

Browse 355 market data Tables and 65 Figures spread through 450 Pages and in-depth TOC on "AI in Drug Discovery Market ".

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Governments across North America, Europe, and Asia Pacific are actively strengthening national AI ecosystems to accelerate innovation in life sciences. Strategic initiatives aimed at integrating artificial intelligence with biomedical research are supporting the development of advanced drug discovery platforms and expanding the role of AI across pharmaceutical pipelines.

De Novo Drug Design Leads Market by Use Case

By use case, the de novo drug design segment accounted for the largest share of the AI in drug discovery market in 2025. The growing adoption of generative AI and deep learning technologies has significantly enhanced the ability to design novel molecular structures with desired biological and physicochemical properties.

AI-driven de novo drug design enables rapid generation, screening, and optimization of candidate molecules, reducing reliance on traditional trial-and-error methods. Pharmaceutical and biotechnology companies are increasingly leveraging these technologies to accelerate early-stage drug discovery, improve candidate success rates, and enhance R&D productivity.

Cloud-Based Deployment Gains Strong Momentum

By deployment model, the cloud-based segment is expected to register a significant CAGR during the forecast period. The growing computational demands of AI models, coupled with the need to process large-scale biological and chemical datasets, are driving adoption of scalable cloud infrastructure.

Cloud platforms enable organizations to access high-performance computing resources, accelerate AI model training, and support seamless collaboration across global research teams. Additionally, partnerships between AI platform providers and cloud service companies are making advanced AI capabilities more accessible without heavy investments in on-premises infrastructure.

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Asia Pacific Emerges as Fastest-Growing Region

Asia Pacific is projected to register the highest growth rate in the AI in drug discovery market during the forecast period. The region is witnessing strong expansion in pharmaceutical R&D investments, biotechnology innovation, and government-backed AI initiatives.

Countries such as China, Japan, South Korea, Singapore, and India are actively investing in AI research, high-performance computing, and precision medicine. Increasing collaborations among pharmaceutical companies, AI technology providers, and research institutions are further accelerating adoption of AI-driven drug discovery solutions in the region.

Competitive Landscape

Key players operating in the AI in drug discovery market include NVIDIA Corporation (US), Schrodinger, Inc. (US), Recursion (US), Insilico Medicine (US), Google (US), Microsoft Corporation (US), Tempus AI, Inc. (US), Illumina, Inc. (US), XtalPi Inc. (China), and Iktos (France), among others.

These companies are focusing on product innovation, strategic collaborations, and technology advancements to strengthen their market position and accelerate the adoption of AI-driven drug discovery platforms globally.

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