AI in Drug Discovery Market Forecasts Diverge: $17.56B and $2.29B by 2031
New reports project AI in drug discovery at $17.56B by 2031 (28.1% CAGR) or $2.29B by 2031 (12.49% CAGR). North America led in 2025, oncology topped therapy areas, and talent shortages persist.
The AI in drug discovery market is projected to reach USD 17.56 billion by 2031 from USD 5.09 billion in 2026, at a CAGR of 28.1%, according to a MarketsandMarkets analysis. A separate research report forecasts the global market expanding from USD 1.13 billion in 2025 to USD 2.29 billion by 2031, reflecting a CAGR of 12.49%. Both analyses describe the market as being driven by the need to accelerate drug development, reduce R&D costs, and manage increasingly complex biological data.
The market is shifting from fragmented, stage-specific applications toward integrated discovery ecosystems that connect biological data, computational chemistry, and experimental validation in a unified workflow. North America accounted for a 44.9% share of the AI in drug discovery market in 2025, and the oncology segment held the largest share of 38.6%. By deployment model, the cloud-based segment is projected to exhibit a CAGR of 28.7% during the forecast period. The de novo drug design segment accounted for the largest market share by use case in 2025, driven by adoption of generative AI, deep learning, and foundation models.
Adoption is being fueled by examples of accelerated research: Pfizer used AI to scan millions of compounds and identify viable drug candidates in 30 days, and KERMT, a small-molecule AI model by Merck and Nvidia, was trained on over 11 million molecules. Benchling's 2026 Biotech AI Report found that half of biotech firms actively employing AI in 2025 experienced a quicker time-to-target. Another estimate in February 2026 put the sector's value at $8-10 billion by 2026, up from roughly $5-7 billion in 2025.
A major constraint is the shortage of AI professionals with life sciences expertise. A 2025 Pistoia Alliance survey found that 34% of life sciences R&D groups viewed the talent shortage as a primary roadblock to implementing AI, up from 23% the previous year. This deficit limits the validation and improvement of predictive algorithms and slows the integration of AI across drug development.
The competitive landscape is evolving with autonomous scientific workflows, foundation models for biology and chemistry, and AI-powered laboratory automation. In January 2026, NVIDIA and Eli Lilly announced the establishment of a co-innovation AI laboratory with planned investments of up to USD 1 billion over five years to develop next-generation foundation models for biology and chemistry using the BioNeMo platform.