Delray Beach, FL, Oct. 16, 2026 (GLOBE NEWSWIRE) -- MarketsandMarkets™ projects the global AI in drug discovery market 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. Market growth is driven by the growing pharmaceutical demand to reduce drug discovery timelines and R&D costs, increasing adoption of generative AI and foundation models, expanding availability of multi-omics datasets, and investments in AI-enabled drug discovery platforms. Increasing strategic collaborations between pharmaceutical companies and AI technology providers, coupled with advances in cloud computing, high-performance computing, and computational biology, are further boosting the adoption of AI across early-stage drug discovery.

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Key Market Highlights

  • Market size, 2026: USD 5.09 billion
  • Market forecast, 2031: USD 17.56 billion
  • Growth rate: CAGR of 28.1% during the forecast period
  • Fastest-growing regional market: Asia Pacific
  • Largest process segment: Hit-to-lead identification/lead generation
  • Largest use case segment: De novo drug design
  • Largest therapeutic area: Oncology
  • Largest player type segment: End-to-end solution providers
  • Fastest-growing AI tool: Machine learning
  • Largest deployment model: Cloud-based deployment
  • Largest end-user segment: Pharmaceutical & biotechnology companies
  • Key players: NVIDIA Corporation (US), Schrödinger, Inc. (US), Microsoft Corporation (US), Recursion (US), Insilico Medicine (US), Google (US), among others

Why This Market Matters

The AI in drug discovery market is undergoing a fundamental shift from hypothesis-driven research to data-driven drug discovery, enabling researchers to identify novel biological targets, predict molecular interactions, and prioritize high-potential drug candidates with greater confidence. AI is enabling pharmaceutical companies to integrate diverse datasets spanning genomics, proteomics, transcriptomics, scientific literature, and real-world evidence, improving decision-making throughout the discovery pipeline. As AI models become increasingly predictive and scalable, organizations are adopting AI as a core component of early-stage drug discovery to enhance portfolio quality, increase research productivity, and improve the probability of successful therapeutic development.

Market Overview

The AI in drug discovery market is primarily driven by the pharmaceutical industry's increasing focus on reducing drug discovery timelines, lowering escalating R&D costs, and improving clinical success rates through AI-enabled predictive modeling. The growing adoption of generative AI, graph neural networks, foundation models, and large biological datasets is transforming target identification, lead optimization, and candidate selection.

Expanding collaborations between pharmaceutical companies, biotechnology firms, AI developers, and cloud technology providers are accelerating the commercialization of AI-driven discovery platforms. Increasing availability of multi-omics data, advancements in high-performance computing, and investments in precision medicine are further strengthening market growth.

Analyst Perspective

AI is increasingly becoming a core component of early-stage drug discovery, helping pharmaceutical and biotechnology companies accelerate target identification, molecular design, lead optimization, and candidate selection. The convergence of artificial intelligence, computational biology, high-performance computing, and multi-omics research is accelerating innovation across the drug discovery value chain. As organizations continue to integrate AI across early-stage drug development workflows, demand for scalable platforms capable of combining computational chemistry, molecular simulation, and biological data analytics is expected to increase significantly.

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Segment Analysis

By process, the hit-to-lead identification/lead generation segment accounted for the largest share of the AI in drug discovery market in 2025. The segment's dominance is driven by the increasing adoption of AI-powered virtual screening, molecular property prediction, and generative chemistry tools that enable rapid identification and optimization of promising lead compounds. By analyzing vast chemical libraries and integrating biological, structural, and pharmacological data, AI significantly improves the quality of lead candidates while reducing the need for resource-intensive laboratory screening.

By use case, the de novo drug design segment accounted for the largest share of the AI in drug discovery market in 2025. This dominance is driven by the increasing adoption of generative AI, deep learning, and computational chemistry platforms that enable the design of novel molecules with desired biological and physicochemical properties. AI-powered de novo drug design significantly accelerates the identification and optimization of potential drug candidates by exploring vast chemical spaces beyond conventional compound libraries.

By therapeutic area, the oncology segment accounted for the largest share of the AI in drug discovery market in 2025. This dominance is driven by the increasing global cancer burden, extensive oncology drug development pipelines, and the growing adoption of AI to accelerate the discovery of targeted therapies and immuno-oncology drugs. AI enables researchers to analyze complex genomic, transcriptomic, proteomic, and clinical datasets for biomarker identification, target discovery, patient stratification, and lead optimization.

By player type, the end-to-end solution providers segment accounted for the largest share of the AI in drug discovery market in 2025. The segment's leadership is driven by the growing demand for integrated AI platforms that support the entire drug discovery workflow, from target identification and hit discovery to lead optimization and candidate selection. Pharmaceutical and biotechnology companies increasingly prefer comprehensive solutions that combine artificial intelligence, computational chemistry, multi-omics data integration, molecular modeling, and predictive analytics within a unified platform.

By AI tool, the machine learning segment is expected to dominate the AI in drug discovery market, registering the highest CAGR during the forecast period. The segment is driven by the widespread application of machine learning algorithms across target identification, virtual screening, molecular property prediction, hit prioritization, and lead optimization. Continuous advancements in deep learning, graph neural networks, and generative AI are further expanding the capabilities of machine learning in molecular design and drug candidate optimization.

By deployment model, the cloud-based deployment segment accounted for the largest share of the AI in drug discovery market in 2025. The segment's dominance is driven by the increasing demand for scalable computing infrastructure capable of supporting AI model training, molecular simulations, and the analysis of large-scale biological and chemical datasets. Cloud-based platforms enable pharmaceutical and biotechnology companies to securely store, process, and share research data while facilitating collaboration across geographically distributed R&D teams.

By end user, the pharmaceutical & biotechnology companies segment accounted for the largest share of the AI in drug discovery market in 2025. The segment's dominance is driven by increasing investments in AI-enabled drug discovery platforms to accelerate target identification, lead optimization, and candidate selection while improving R&D productivity. Pharmaceutical and biotechnology companies are leveraging AI to analyze complex biological and chemical datasets, enhance predictive modeling, and reduce dependence on conventional trial-and-error approaches in early-stage drug discovery.

Regional Analysis

Asia Pacific is the fastest-growing AI in drug discovery market, supported by the rapid expansion of pharmaceutical and biotechnology research, increasing availability of genomic and clinical datasets, and growing investments in AI-native drug discovery companies. The region is emerging as a global hub for computational drug discovery, driven by expanding cloud infrastructure, high-performance computing capabilities, and a strong pipeline of AI-enabled biotechnology startups.

China, Japan, South Korea, Singapore, and India are witnessing increasing adoption of AI across target identification, molecular design, and lead optimization, while collaborations between regional biotechnology companies and global pharmaceutical manufacturers continue to accelerate technology commercialization. These factors are positioning the region as a key growth engine for AI-enabled drug discovery during the forecast period.

Key Industry Trends

  • Increasing adoption of generative AI and foundation models
  • Growing use of multi-omics datasets in drug discovery
  • Increasing adoption of AI-powered virtual screening and molecular property prediction
  • Growing use of AI in de novo drug design
  • Increasing adoption of cloud-based AI platforms
  • Rising strategic collaborations between pharmaceutical companies and AI technology providers
  • Increasing use of high-performance computing and computational biology
  • Growing investments in AI-native biotechnology companies

Competitive Landscape

The competitive landscape of the AI in drug discovery market includes technology providers, AI-native drug discovery companies, pharmaceutical and biotechnology companies, and other participants across the drug discovery value chain. NVIDIA Corporation, Schrödinger, Recursion, Insilico Medicine, and Google are among the major companies operating in the market.

Strategic collaborations and continuous platform innovation remain the two most dominant competitive strategies shaping the AI in drug discovery market. Companies are increasingly partnering with pharmaceutical manufacturers, contract research organizations (CROs), academic institutions, and cloud infrastructure providers to accelerate target discovery, optimize lead generation, and validate AI-derived drug candidates.

The major companies operating in the market include NVIDIA Corporation (US), Schrödinger, Inc. (US), Microsoft Corporation (US), Recursion (US), Insilico Medicine (US), Google (US), BenevolentAI (UK), Numerion Labs (US), Illumina, Inc. (US), XtalPi Inc. (China), Iktos (France), Tempus AI, Inc. (US), Deep Genomics, Inc. (Canada), Verge Labs (US), BPGbio, Inc. (US), BenchSci (Canada), Valo Health (US), insitro (US), Relay Therapeutics (US), and Generate (US).

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