인실리코 신약 개발 분야 인공지능(AI) : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the aI in in-Silico drug development market size is expected to increase from USD 3.10 billion in 2025 to USD 4.05 billion in 2026 and reach USD 15.31 billion by 2031, growing at a CAGR of 30.5% over 2026-2031.

This report is Segmented by Application Stage (Target ID, Hit Discovery, Land More), AI Technology (Machine Learning, Deep Learning, and More), Drug Type (Small Molecule, Biologics, Other), Therapeutic Area (Oncology, Neurology, and More), End User (Pharma/Biotech, Cros, Academic), Deployment Mode (Cloud, On-Premise), and Geography (North America, and More). Market Forecasts are in Value (USD).

Global AI In In-Silico Drug Development Market Trends and Insights

Cloud-Native High-Performance Computing Gains Traction

Widespread access to elastic GPU clusters is reducing the financial barriers to AI research. NVIDIA's BioNeMo, operational at several pharmaceutical firms, cuts training latency for extensive protein language models by half and increases inference speed six-fold. Meanwhile, Denmark's Gefion supercomputer enabled Orbis Medicines to analyze 140 billion macrocycles in just a few days. The shift to on-demand infrastructure transforms fixed capital expenses into flexible costs, allowing mid-tier biotech firms to conduct billion-molecule virtual screenings, a capability previously limited to the top 10 pharmaceutical companies five years ago. The FDA's pilot program, set for April 2026, is now utilizing real-time cloud data from AstraZeneca and Amgen trials, streamlining administrative processes by 20 to 40 percent. This combination of cost-effective computing and regulatory support is driving sustained growth in the AI in in-silico drug development market.

Pharmaceuticals Embrace AI for Swift Target Validation

Data-driven validation methods are shifting attrition to the earliest stages. Valo Health's Opal platform, which leverages 17 million de-identified patient records, is prioritizing genetically-backed programs. The company has expanded its partnership with Novo Nordisk to include 20 cardiometabolic targets, with a potential value of up to USD 4.6 billion. Recursion, using over a trillion induced pluripotent stem-cell images for phenotypic mapping, accelerated REC-4881's progression from target identification to Phase 1b-2 in under two years by streamlining SAR and toxicity evaluations.These examples highlight how AI-driven strategies are transforming decision-making and timelines in the AI in in-silico drug development market.

Bias in Current Models Limits Performance on Underexplored Targets

Current models, influenced by publicly available positive data, face challenges in addressing underexplored targets. Approximately 90% of pre-clinical programs that fail are not included in shared repositories. This gap forces sponsors to repeatedly encounter the same challenges. In May 2025, Recursion discontinued REC-994 after Phase 2 signals proved insufficient, emphasizing the risks of relying on biased datasets. Establishing consortia to consolidate negative results may be crucial for improving transferability in the AI-driven in-silico drug development market.

Other drivers and restraints analyzed in the detailed report include:

1. Venture Capital and Corporations Rally Behind AI-Driven Biotechs
2. Regulators Embrace Algorithmic Submissions
3. Patent Law's Challenge with AI-Driven Molecular Design

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

Segment Analysis

In 2025, lead optimization accounted for 33.33% of the AI-driven in-silico drug development market, highlighting the immediate advantages of AI-enhanced SAR and multiparameter optimization. For several oncology programs, fragment-based suites have significantly reduced synthesis iterations from over twenty to just a few. This segment's depth not only increases the market size but also drives higher investments in potency and selectivity by sponsors. Meanwhile, pre-clinical candidate selection is advancing rapidly, with a 33.30% CAGR, and is expected to narrow the gap by 2031. Integrated platforms are streamlining processes by combining design, ADME-Tox prediction, and manufacturability assessments into a continuous loop, accelerating IND filings.

In 2025, machine learning captured 46.45% of the revenue due to its mature supervised models, knowledge graphs, and gradient-boosting ensembles that seamlessly integrate into discovery workflows. Physics-augmented neural networks and graph embeddings exemplify the production readiness of this technology. The market's reliance on machine learning is further supported by proprietary historical SAR and crystallography data, which are challenging for new entrants to replicate.

Reinforcement learning is gaining traction with a 32.16% CAGR, as autonomous exploration bridges the gap between design and assay. Transformer engines have improved structure prediction coverage to 76% of the human proteome. Large language models have surpassed benchmarks in numerous therapeutic tasks. As closed-loop robotics becomes standard, reinforcement-learning agents are evolving from move-suggestion to real-time hypothesis generation and laboratory execution, enhancing the long-term market potential of this technology.

Geography Analysis

In 2025, North America accounted for 40.25% of the revenue, leveraging FDA guidance, Silicon Valley's hardware ecosystems, and discovery clusters in Boston and Salt Lake City. Venture funding remained concentrated, with significant follow-on rounds raised by key players. The region's policy clarity, including FDA's real-time trial monitoring and draft AI guidance, continues to attract global partnerships, reinforcing North America's central role in the AI in in-silico drug development market.

Europe maintained a mid-20% share, supported by stringent regulatory oversight and collaborative public-private federated-data initiatives. The EMA's AIM-NASH qualification and the anticipated EU AI Act provide clear yet demanding pathways for algorithmic submissions. Initiatives such as a safeguarded AI fund and GDPR-compliant hospital partnerships highlight efforts to integrate AI into healthcare while maintaining data privacy standards.

Asia-Pacific is expected to grow at the fastest rate, with a projected CAGR of 33.98% through 2031. China's Digital and Intelligent Transformation Plan, supported by state credits and provincial subsidies, aims to establish 100 digital drug factories and 10 large-model platforms by 2027. Japan's aging population drives a need for increased productivity, prompting investments from major companies. In India, the adoption of a cloud-native multiparameter-optimization platform is enabling a shift from generics to innovation, further expanding the regional AI in in-silico drug development market.

1. Aria Intelligent Solutions
2. Numerion Labs (Atomwise)
3. Benevolent AI
4. BioAge Labs
5. Cloud Pharmaceuticals
6. Cyclica
7. Deep Genomics
8. Evaxion Biotech
9. Exscientia
10. Healx
11. Iktos
12. Insilico Medicine
13. Owkin
14. Recursion Pharmaceuticals
15. Relay Therapeutics
16. Schrodinger
17. Turbine
18. Valo Health
19. Verge Genomics
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 Surging Cloud-Native High-Performance Computing Access
  • 4.2.2 Pharma Shift to Fail-Fast AI-Assisted Screening Models
  • 4.2.3 Growing Venture & Corporate Funding for AI-First Biotech
  • 4.2.4 Regulatory Pilot Programs for Algorithmic Submissions (E.G., FDA's CDS Initiative)
  • 4.2.5 Quantum-Inspired AI Accelerators Reaching Commercial Maturity
  • 4.2.6 Data-Centric Foundation Models Enabling Zero-Shot Target Prediction
• 4.3 Market Restraints
  • 4.3.1 Sparse Negative-Result Datasets Hindering Algorithm Generalization
  • 4.3.2 Unresolved IP Ownership for AI-Generated Molecular Structures
  • 4.3.3 High Switch-Out Costs from Incumbent Wet-Lab CRO Workflows
  • 4.3.4 Regulatory Opacity on Explainability Thresholds for Generative Models
• 4.4 Value / Supply-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 Competitive Rivalry

5 Market Size & Growth Forecasts (Value, USD)

• 5.1 By Application Stage
  • 5.1.1 Target Identification
  • 5.1.2 Hit Discovery
  • 5.1.3 Lead Optimization
  • 5.1.4 Pre-clinical Candidate Selection
• 5.2 By AI Technology
  • 5.2.1 Machine Learning
  • 5.2.2 Deep Learning
  • 5.2.3 Natural Language Processing
  • 5.2.4 Reinforcement Learning
  • 5.2.5 Other AI Methods
• 5.3 By Drug Type
  • 5.3.1 Small Molecule
  • 5.3.2 Biologics
  • 5.3.3 Other Types
• 5.4 By Therapeutic Area
  • 5.4.1 Oncology
  • 5.4.2 Neurology
  • 5.4.3 Infectious Diseases
  • 5.4.4 Cardiovascular
  • 5.4.5 Metabolic Disorders
  • 5.4.6 Other Therapeutic Areas
• 5.5 By End User
  • 5.5.1 Pharmaceutical & Biotech Firms
  • 5.5.2 Contract Research Organizations
  • 5.5.3 Academic & Research Institutes
• 5.6 By Deployment Mode
  • 5.6.1 Cloud-Based Platforms
  • 5.6.2 On-Premise Solutions
• 5.7 By Geography
  • 5.7.1 North America
    • 5.7.1.1 United States
    • 5.7.1.2 Canada
    • 5.7.1.3 Mexico
  • 5.7.2 Europe
    • 5.7.2.1 Germany
    • 5.7.2.2 United Kingdom
    • 5.7.2.3 France
    • 5.7.2.4 Italy
    • 5.7.2.5 Spain
    • 5.7.2.6 Rest of Europe
  • 5.7.3 Asia-Pacific
    • 5.7.3.1 China
    • 5.7.3.2 India
    • 5.7.3.3 Japan
    • 5.7.3.4 South Korea
    • 5.7.3.5 Australia
    • 5.7.3.6 Rest of Asia-Pacific
  • 5.7.4 Middle East & Africa
    • 5.7.4.1 GCC
    • 5.7.4.2 South Africa
    • 5.7.4.3 Rest of Middle East and Africa
  • 5.7.5 South America
    • 5.7.5.1 Brazil
    • 5.7.5.2 Argentina
    • 5.7.5.3 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, Strategic Information, Market Rank/Share, Products & Services, Recent Developments)
  • 6.3.1 Aria Intelligent Solutions
  • 6.3.2 Numerion Labs (Atomwise)
  • 6.3.3 BenevolentAI
  • 6.3.4 BioAge Labs
  • 6.3.5 Cloud Pharmaceuticals
  • 6.3.6 Cyclica
  • 6.3.7 Deep Genomics
  • 6.3.8 Evaxion Biotech
  • 6.3.9 Exscientia
  • 6.3.10 Healx
  • 6.3.11 Iktos
  • 6.3.12 Insilico Medicine
  • 6.3.13 Owkin
  • 6.3.14 Recursion Pharmaceuticals
  • 6.3.15 Relay Therapeutics
  • 6.3.16 Schrodinger
  • 6.3.17 Turbine
  • 6.3.18 Valo Health
  • 6.3.19 Verge Genomics
  • 6.3.20 XtalPi

7 Market Opportunities & Future Outlook

• 7.1 White-space & Unmet-need Assessment