(MENAFN- GlobeNewsWire - Nasdaq) AI-driven predictive design, patient stratification and lab automation cut screening costs, reduce late-stage failures and accelerate drug development.Dublin, Nov. 09, 2026 (GLOBE NEWSWIRE) -- "AI for Drug Development and Discovery - Global Strategic Business Report 2026" has been added to ResearchAndMarkets.com's offering.
The global artificial intelligence (AI) for drug development and discovery market was valued at US$2.6 billion in 2025 and is projected to reach US$18.1 billion by 2032, expanding at a compound annual growth rate (CAGR) of 31.8%. Growth is being supported by advances in predictive molecular design, expanding genomic and proteomic datasets, increasing adoption of personalized medicine, and continued pressure to reduce pharmaceutical research costs and development timelines.
AI Transforms Molecular Discovery and Target Identification
Artificial intelligence is reshaping early-stage pharmaceutical research by moving compound identification from extensive laboratory screening toward predictive molecular design. Machine learning models assess biological activity, physicochemical properties, protein-binding affinity, solubility, toxicity, and other pharmacological parameters before synthesis. This enables researchers to evaluate millions of theoretical structures and prioritize the most promising drug candidates for laboratory validation.
Generative AI models can propose novel molecular scaffolds that meet predefined therapeutic requirements, while biological pathway analysis platforms process genomic and proteomic data to identify disease mechanisms and potential drug targets. Integration with laboratory automation creates an iterative research environment in which assay results continuously improve model performance. These capabilities support focused validation strategies, accelerate lead optimization, and reduce dependence on broad screening campaigns.
Predictive Analytics Supports Clinical Development Decisions
AI applications increasingly extend into pharmacokinetics, toxicity assessment, patient stratification, trial design, and therapeutic response forecasting. Models trained on historical clinical trial data can estimate the probability of candidate success across selected patient populations. Simulation platforms assess absorption, distribution, metabolism, excretion, safety risks, and potential off-target interactions before clinical exposure.
Biomarker-based patient selection can improve recruitment strategies and strengthen trial outcomes, while clinical protocol optimization tools support dosage planning and cohort design. Continuous learning systems update development risk assessments as new experimental and clinical evidence becomes available. By improving candidate prioritization, AI can help pharmaceutical and biotechnology companies reduce costly late-stage failures and allocate capital more effectively.
Connected AI Platforms Expand Across Pharmaceutical R&D
Pharmaceutical organizations are integrating discovery models, laboratory robotics, clinical analytics, manufacturing systems, and post-market surveillance within connected data environments. Automated laboratory instruments transfer assay results to centralized platforms, enabling near-real-time model refinement. Digital twins and manufacturing analytics support formulation development, production yield optimization, stability analysis, batch consistency, and deviation forecasting.
Knowledge graphs connect scientific publications, patents, clinical evidence, and proprietary research data to support hypothesis generation. Cloud infrastructure also enables secure collaboration among geographically distributed teams. At the same time, partnerships between pharmaceutical companies, biotechnology firms, and technology providers are driving the development of specialized AI platforms for antibody engineering, RNA therapeutics, gene editing, rare diseases, and precision medicine.
Key AI Drug Discovery Market Growth Drivers
Increasing availability of genomic, proteomic, clinical, and real-world patient data. High failure rates and rising costs associated with late-stage clinical trials. Growing complexity of biologics, cell therapies, gene therapies, and RNA-based treatments. Expansion of biomarker-guided personalized medicine and targeted clinical development. Demand for rapid therapeutic design in response to emerging pathogens. Greater use of predictive safety screening and computational candidate prioritization. Strategic partnerships between pharmaceutical companies and AI technology providers. Increasing adoption of analytical monitoring across advanced therapy manufacturing.
Report Scope
The report analyzes the global AI for drug development and discovery market by value in US dollars, covering the following segments and geographic markets:
Type: Target identification, molecule screening, de novo drug design and drug optimization, and preclinical and clinical testing. Indication: Oncology, infectious diseases, neurology, and other indications. End Use: Pharmaceutical and biotechnology companies and contract research organizations. Geography: United States, Canada, Japan, China, France, Germany, Italy, United Kingdom, the rest of Europe, Asia-Pacific, and the rest of the world.
Key Market Insights
The target identification segment is forecast to reach US$9.3 billion by 2032, registering a CAGR of 35.5%. The molecule screening segment is projected to expand at a CAGR of 26.9% during the analysis period. The United States AI drug development and discovery market was valued at US$790.6 million in 2025. China is forecast to grow at a CAGR of 29.9% and reach US$2.9 billion by 2032. Additional regional analysis covers Japan, Canada, Germany, Europe, and Asia-Pacific.
Strategic Value of the Report
The report provides independent annual market estimates and forecasts from 2025 through 2032, along with analysis of major technologies, applications, end users, geographic markets, competitive conditions, and emerging revenue opportunities. It examines the principal factors influencing adoption, identifies high-growth segments, and evaluates how regional and segment shares may change through 2032.
Company coverage includes BenevolentAI, BioSymetrics, BioXcel Therapeutics, BostonGene, BPGbio Inc., Collaborations Pharmaceuticals Inc., Delta 4, Existencia, Healx Ltd., and Insilico Medicine. Purchasers also receive complimentary report updates for one year.
Domain Expert Insights and Report Differentiation
Domain-informed perspectives: The analysis incorporates insights emerging from industry, enterprise, and academic experts to provide context beyond conventional published research. Early market signals: The report tracks developments emerging through expert discussions, corporate activity, partnerships, and market-relevant events before they are fully represented in secondary research. Validated intelligence base: Findings draw on conference sessions, keynote presentations, panel discussions, corporate disclosures, annual reports, institutional publications, and other verified sources. Strategic market direction: The report connects emerging signals with market drivers, competitive developments, investment priorities, and long-term growth opportunities. Company intelligence: Coverage extends beyond market-level forecasts to examine organizations influencing the competitive landscape. Relevant market share analysis: Where appropriate, market share is assessed across major regions to reflect geographic differences in competitive structure and adoption patterns.
Key Attributes:
CONTACT:
CONTACT: ResearchAndMarkets.com
Laura Wood,Senior Press Manager...
For E.S.T Office Hours Call 1-917-300-0470
For U.S./ CAN Toll Free Call 1-800-526-8630
For GMT Office Hours Call +353-1-416-8900
Comments
No comment