의약품 스크리닝용 인공지능(AI) : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the aI in drug screening market size is expected to grow from USD 0.34 billion in 2025 to USD 0.41 billion in 2026 and is forecast to reach USD 1.14 billion by 2031 at 22.65% CAGR over 2026-2031.

This report is Segmented by Offering (Software, Services), Technology (ML, Deep Learning & Gen AI, NLP), Application (Target ID, Hit ID, Lead Gen, Repurposing, Preclinical Selection), Therapeutic Area (Oncology, Infectious Dis, Neurology, Cardiovasc, Metabolic, Immunology), End User (Pharma/Biotech, CROs/CDMOs, Academic, Hospitals), and Geography (North America, Europe, and More). Forecasts in Value (USD).

Global AI In Drug Screening Market Trends and Insights

Pressure to Cut Discovery Cost and Cycle Time

Pharmaceutical pipelines continue to face a structural efficiency problem because average drug development cost still approaches USD 2.6 billion, timelines often exceed 10 years, and 90% of clinical candidates fail before approval. The AI in drug screening market is benefiting directly from this pressure because buyers now evaluate AI against a clear cost and time benchmark rather than against experimental novelty. Some AI-driven workflows have already reduced selected discovery stages to 12 to 18 months and delivered cost reductions of up to 40% against traditional methods. That compression is changing how early research budgets are assigned, with more capital moving toward platform subscriptions, model access, and compute infrastructure instead of only physical compound library expansion. The result is a stronger commercial case for platforms that can surface safety, efficacy, and screening signals earlier in the discovery cycle.

Better Generative AI and Molecular Design Models

The AI in drug screening market is also being lifted by a step change in model capability, especially in early lead generation where generative systems now produce outputs that can compete with parts of physical screening. Chai Discovery reported that its Chai-2 model achieved a near-20% experimental hit rate in de novo antibody design, a major increase over the prior 0.1% computational benchmark. VantAI launched Neo-1 in March 2025 as an atomistic foundation model that combines de novo molecular generation with multimodal structure prediction in one architecture, which broadens the range of tractable targets for platform users. Schrodinger has also positioned its Bunsen agentic AI co-scientist for early access in summer 2026, showing how platform vendors are moving from single-task models toward systems that can execute larger parts of discovery workflows. As these tools improve faster than wet-lab synthesis capacity, the next investment cycle is shifting toward automated chemistry and validation infrastructure, a pattern reinforced by Profluent's USD 2.25 billion collaboration with Eli Lilly for AI-designed recombinases.

Siloed Data, Privacy Constraints, and Poor Interoperability

The AI in drug screening market still faces a core data problem because many public ADMET datasets are assembled from 20 to 50 separate papers that use different experimental conditions and show weak reproducibility across studies. Collaborative Drug Discovery reported that ADMET models can reach R2 near 0.9 on internal test sets but fall to around 0.75 on external benchmarks when assay methods differ, which shows that transfer across organizations is still limited. Multi-omics integration adds another layer of friction because genomic, transcriptomic, and proteomic data often sit in separate institutional silos with incompatible metadata and require manual harmonization before cross-study training can begin. This slows model development and also gives a lasting advantage to firms that have already federated large proprietary data assets into usable training environments. As a result, data governance in the AI in drug screening market is becoming just as important as model design for long-term competitive positioning.

Other drivers and restraints analyzed in the detailed report include:

1. AlphaFold-Scale Structure Maps Improving Target Enablement
2. Rising Pharma-Tech Partnerships and Licensing Activity
3. Limited Explainability and Wet-Lab Translation Confidence

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software platforms captured 62.31% of AI in drug screening market size in 2025, which confirms that scalable model access still represents the main commercial format for buyers. The current revenue base favors software because cloud deployment lets drug discovery teams screen very large virtual libraries without a matching increase in laboratory cost. One platform subscription can support multiple programs at the same time, which gives internal R&D groups a more predictable cost structure than project-based service engagements. This model has kept software at the center of the AI in drug screening market while platform buyers continue to test different workflow combinations across target discovery and screening.

Services are the fastest-growing offering segment, with a projected CAGR of 27.38% from 2026 to 2031. This rise reflects stronger outsourcing demand from mid-size biotech firms that do not yet have the internal data, compute, or specialist teams needed to operate full discovery platforms on their own. CROs and CDMOs are responding by adding AI-enabled screening, data interpretation, and bespoke model training into their discovery packages. Over time, the balance in the AI in drug screening market is likely to move toward more service-led value capture as software functionality becomes easier to replicate and differentiation shifts toward integrated workflow execution on client-owned data.

Machine learning held 46.24% of the technology segment in 2025, which reflects its established role across ADMET prediction, property scoring, and quantitative structure-activity relationship modeling in production discovery workflows. In the AI in drug screening market, this installed base matters because machine learning methods are already embedded in day-to-day screening tasks and remain trusted for repeatable scoring functions. They also fit well into existing R&D systems, which lowers switching friction for buyers that want practical gains without rebuilding entire discovery stacks. That installed position explains why machine learning still anchors the current technology mix even as newer model classes gain attention.

Deep learning and generative AI is the fastest-growing technology segment, with a CAGR of 28.52% from 2026 to 2031. Chai Discovery's Chai-2 model showed a near-20% experimental hit rate in de novo antibody design, which signals that generative systems are becoming viable first-pass screening tools rather than optional design aids. Receptor.AI also reported first-place ranking on 10 of 16 TDC benchmark tasks in 2025 for its ADMET model family, including strong results on DILI, hERG, and CYP450 endpoints. Natural language processing and graph-based methods continue to serve as enabling layers for literature mining and protein-ligand modeling, but the AI in drug screening industry is moving toward multimodal architectures that blur old segment boundaries and push vendors toward full workflow competition.

Geography Analysis

North America held 43.24% of AI in drug screening market share in 2025, which kept it as the largest regional base. The region benefits from the highest concentration of AI-native drug discovery firms, deep venture funding, and close ties between biotech platforms, major pharmaceutical companies, and research institutions. In April 2026, the FDA launched an AI pilot program for drug development that supports real-time trial monitoring and reflects a broader move toward AI-enabled regulatory workflows. The NIH Complement-ARIE program also committed USD 150 million to AI-driven predictive systems that can act as alternatives to animal models, which lowers adoption risk for vendors that need credible validation pathways. Canada and Mexico add useful academic, biotech, and manufacturing links, but the region remains centered on U.S.-based platforms and capital formation.

Europe remains an important center of the AI in drug screening market, with activity increasingly concentrated around the UK, Germany, and Switzerland. The UK stands out in AI-first biotech, and Isomorphic Labs' USD 2.1 billion Series B in May 2026 showed that very large pools of institutional capital are now backing long-horizon AI drug design platforms. The FDA and EMA jointly issued 10 guiding principles for AI in medicine development in January 2026, which gave European developers a clearer governance framework for explainability, oversight, and data handling EMA. Germany and France provide strong CRO and academic medical center capacity, while Spain and Italy are building momentum through growing biotech clusters and earlier adoption of AI-enabled drug development tools.

Asia-Pacific is the fastest-growing regional segment in the AI in drug screening market, with a CAGR of 26.53% from 2026 to 2031. China and India are the main growth engines because discovery outsourcing capacity, AI adoption in R&D, and platform integration are improving across regional ecosystems. Japan and South Korea add depth through pharmaceutical digitalization efforts and government-backed AI biotech initiatives that strengthen the broader innovation base. Middle East and Africa, supported in part by GCC capital participation in large AI drug design financings, and South America, led by Brazil's developing CRO network, remain smaller today but still matter for long-term expansion of the AI in drug screening market.

1. Absci
2. Atomwise
3. BenchSci
4. Benevolent AI
5. CytoReason
6. Deep Genomics
7. Generate:Biomedicines
8. Genesis Molecular AI
9. Iktos
10. Insilico Medicine
11. insitro
12. Isomorphic Labs
13. Lantern Pharma
14. Owkin
15. Recursion Pharmaceuticals
16. Schrodinger
17. Standigm
18. Valo Health
19. WuXi App Tec
20. XtalPi

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 Introduction

• 1.1 Study Assumptions & Market Definition
• 1.2 Scope of the Study

2 Research Methodology

3 Executive Summary

4 Market Landscape

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Pressure to Cut Discovery Cost and Cycle Time
  • 4.2.2 Expanding Multi-Omics and Clinical Training Datasets
  • 4.2.3 Rising Pharma-Tech Partnerships and Licensing Activity
  • 4.2.4 Better Generative AI and Molecular Design Models
  • 4.2.5 Alphafold-Scale Structure Maps Improving Target Enablement
  • 4.2.6 Benchmark-Grade ADMET and Protein-Ligand Datasets Improving Model Reliability
• 4.3 Market Restraints
  • 4.3.1 Siloed Data, Privacy Constraints, and Poor Interoperability
  • 4.3.2 Limited Explainability and Wet-Lab Translation Confidence
  • 4.3.3 Rising FDA and EMA Documentation Expectations for AI-Enabled Evidence
  • 4.3.4 Noisy Public Datasets and Weak Scaffold Transfer in Real-World Screening
• 4.4 Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces
  • 4.7.1 Threat of New Entrants
  • 4.7.2 Bargaining Power of Suppliers
  • 4.7.3 Bargaining Power of Buyers
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Intensity of Competitive Rivalry

5 Market Size & Growth Forecasts (Value, USD)

• 5.1 By Offering
  • 5.1.1 Software Platforms
  • 5.1.2 Services
• 5.2 By Technology
  • 5.2.1 Machine Learning
  • 5.2.2 Deep Learning and Generative AI
  • 5.2.3 Natural Language Processing
  • 5.2.4 Other Technologies
• 5.3 By Application
  • 5.3.1 Target Identification and Validation
  • 5.3.2 Hit Identification and Virtual Screening
  • 5.3.3 Lead Generation and Optimization
  • 5.3.4 Drug Repurposing
  • 5.3.5 Preclinical Candidate Selection and Toxicity Prediction
  • 5.3.6 Biomarker Discovery and Companion Insights
• 5.4 By Therapeutic Area
  • 5.4.1 Oncology
  • 5.4.2 Infectious Diseases
  • 5.4.3 Neurology and Psychiatric Disorders
  • 5.4.4 Cardiovascular Disorders
  • 5.4.5 Metabolic and Endocrine Disorders
  • 5.4.6 Immunology and Inflammatory Disorders
  • 5.4.7 Other Therapeutic Areas
• 5.5 By End User
  • 5.5.1 Pharmaceutical and Biotechnology Companies
  • 5.5.2 CROs and CDMOs
  • 5.5.3 Academic and Research Institutes
  • 5.5.4 Hospitals and Clinical Research Networks
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
    • 5.6.1.3 Mexico
  • 5.6.2 Europe
    • 5.6.2.1 Germany
    • 5.6.2.2 United Kingdom
    • 5.6.2.3 France
    • 5.6.2.4 Italy
    • 5.6.2.5 Spain
    • 5.6.2.6 Rest of Europe
  • 5.6.3 Asia-Pacific
    • 5.6.3.1 China
    • 5.6.3.2 India
    • 5.6.3.3 Japan
    • 5.6.3.4 South Korea
    • 5.6.3.5 Australia
    • 5.6.3.6 Rest of Asia-Pacific
  • 5.6.4 Middle East and Africa
    • 5.6.4.1 GCC
    • 5.6.4.2 South Africa
    • 5.6.4.3 Rest of Middle East and Africa
  • 5.6.5 South America
    • 5.6.5.1 Brazil
    • 5.6.5.2 Argentina
    • 5.6.5.3 Rest of South America

6 Competitive Landscape

• 6.1 Market Concentration
• 6.2 Market Share Analysis
• 6.3 Company Profiles (includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products & Services, and Recent Developments)
  • 6.3.1 Absci
  • 6.3.2 Atomwise
  • 6.3.3 BenchSci
  • 6.3.4 BenevolentAI
  • 6.3.5 CytoReason
  • 6.3.6 Deep Genomics
  • 6.3.7 Generate:Biomedicines
  • 6.3.8 Genesis Molecular AI
  • 6.3.9 Iktos
  • 6.3.10 Insilico Medicine
  • 6.3.11 insitro
  • 6.3.12 Isomorphic Labs
  • 6.3.13 Lantern Pharma
  • 6.3.14 Owkin
  • 6.3.15 Recursion Pharmaceuticals
  • 6.3.16 Schrodinger
  • 6.3.17 Standigm
  • 6.3.18 Valo Health
  • 6.3.19 WuXi AppTec
  • 6.3.20 XtalPi

7 Market Opportunities & Future Outlook

• 7.1 White-space & unmet-need assessment