AI 기반 의약품 제형 개발 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the AI-powered drug formulation market is projected to expand from USD 0.77 billion in 2025 and USD 0.93 billion in 2026 to USD 2.77 billion by 2031, registering a CAGR of 24.37% between 2026 and 2031.

This report is Segmented by Component (Software Platforms, Services), Deployment Mode (Cloud-Based, On-Premises), Technology (Machine Learning and Deep Learning, and More), Application (Formulation Design and Optimization, and More), Drug Type (Small Molecules, and More), End-User (Contract Research Organizations, and More), and Geography (North America, Europe, and More). Market Forecasts in Value (USD).

Global AI-Powered Drug Formulation Market Trends and Insights

Rising Need to Reduce Trial-and-Error in Formulation Development

Escalating development costs and shorter commercial timelines are pushing the AI-powered drug formulation market toward tools that can reduce repeated lab iterations. This pressure is strongest when late-stage formulation failures delay filing plans, waste scarce API, or force manufacturing changes after clinical progress has already been made. The FDA's ongoing focus on quality issues, drug shortages, and manufacturing reliability supports earlier use of quality-by-design thinking, which strengthens the case for predictive formulation work at the front end of development. A 2026 Nature Communications study reinforced that point by showing 60% lower development time and 65% lower API use in an AI-powered tableting workflow tested across 1,199 data points from 170 formulations. For buyers in the AI-powered drug formulation market, the appeal is practical because lower material consumption and faster decision cycles directly affect development cost and speed. This is why adoption is shifting from pilot activity toward routine workflow inclusion in both sponsor and outsourced formulation settings.

Growth in Biologics, Nucleic Acids, and Advanced Therapy Formulations

The AI-powered drug formulation market is also being lifted by the fast expansion of complex drug classes that require tighter formulation control than small-molecule programs. Cell and gene therapy pipelines remained active at a large scale in late 2025, and each program depends on highly specific decisions around vector stability, delivery systems, and process conditions. A 2026 Nature Reviews Materials article described the shift from combinatorial lipid nanoparticle screening toward generative design, and the LiGen model reported average predicted performance gains of 30.7% against retrieval-based baselines.Chime Biologics responded to this demand in November 2025 by launching an AI platform that spans cell line development, bioprocessing, and CMC support, which shows that end-to-end biologics workflow tools are becoming a commercial need rather than a narrow experiment. As these therapy classes grow, the AI-powered drug formulation market is being pulled toward platforms that can handle higher variability, higher value batches, and more demanding stability targets.

Sparse, Proprietary, and Non-Standardized Formulation Data

A major limit on the AI-powered drug formulation market is the small and fragmented nature of real formulation datasets. Unlike genomics or imaging, formulation records often include only tens or hundreds of experiments, and many negative results remain unpublished or locked within single organizations. A 2026 International Journal of Pharmaceutics study reached a similar conclusion and introduced a structured machine-readable oral formulation database to help address that gap. The practical result is that many models in the AI-powered drug formulation market still work best within narrow drug classes, process windows, or formulation types. Until broader data standards and shared datasets improve, model generalizability will remain one of the clearest limits on adoption at scale.

Other drivers and restraints analyzed in the detailed report include:

1. Increasing Demand for Personalized and Precision Medicine Dose Design
2. Pharma-CDMO Workflow Digitization and Self-Service Formulation Platforms
3. Limited Validation and Explainability for Regulated Use Cases

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

Segment Analysis

Software platforms held 51.38% of the AI-powered drug formulation market share in 2025 and are also expected to be the fastest-growing segment with 24.61% CAGR through 2031, which shows that buyers currently place more value on repeatable computational capability than on project-based support. In the AI-powered drug formulation market, this favors vendors that can combine model execution, data handling, workflow traceability, and user collaboration in one environment. It also points to a purchasing preference for tools that fit into wider R&D and CMC operating models rather than narrowly scoped technical tasks.

As sponsors and outsourced partners gain comfort with software-led workflows, service revenue is likely to follow through model setup, integration, validation, and program-specific support. This keeps the AI-powered drug formulation industry tied closely to software licensing economics even when hands-on scientific services continue to grow. It also means vendors with strong platform footprints can extend into adjacent support work without changing the core structure of the segment.

Cloud-based deployment accounted for 52.72% of the AI-powered drug formulation market size in 2025 and is also anticipated to be the fastest-growing deployment segment with a 25.18% CAGR through 2031. For many users, cloud deployment reduces the need for frequent hardware upgrades and allows capacity to scale with program demands. In the AI-powered drug formulation market, this matters because program intensity can change quickly across discovery, preclinical work, and CMC preparation.

Mid-sized pharma and biotech firms benefit because they can access advanced tools without maintaining the same level of internal infrastructure as larger enterprises. On-premises deployments still have a role where IP sensitivity, data sovereignty, or internal control policies remain high. Even so, the direction of the AI-powered drug formulation market still favors cloud architecture because collaborative formulation work increasingly crosses sites, organizations, and development functions.

Complete Report Scope:

• By Component
  • Software Platforms
  • Services
• By Deployment Mode
  • Cloud-Based
  • On-Premises
• By Technology
  • Machine Learning and Deep Learning
  • Predictive Modeling and Simulation
  • Generative AI and Neural Networks
  • Hybrid AI Approaches
  • Natural Language Processing
• By Application
  • Formulation Design and Optimization
  • Excipient Compatibility Prediction
  • Stability and Shelf-Life Prediction
  • High-Throughput Screening Optimization
  • Personalized and Precision Medicine Formulations
  • Advanced Therapy Formulations
• By Drug Type
  • Small Molecules
  • Biologics
  • Nucleic Acid-Based Drugs
  • Cell and Gene Therapy Payloads
  • Vaccines
• By End-User
  • Pharmaceutical and Biotechnology Companies
  • Contract Research Organizations
  • Contract Development and Manufacturing Organizations
  • Academic and Research Institutes
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • India
    • Australia
    • South Korea
    • Rest of Asia-Pacific
  • Middle East and Africa
    • GCC
    • South Africa
    • Rest of Middle East and Africa
  • South America
    • Brazil
    • Argentina
    • Rest of South America

Geography Analysis

North America held 44.19% of the AI-powered drug formulation market share in 2025, which made it the leading regional contributor. The region benefits from a dense concentration of large pharmaceutical companies, a mature startup and venture environment, and a strong base of software and biosimulation vendors. The United States remains the center of regional activity because it combines sponsor demand, platform development, and regulatory engagement in the same market. This keeps North America at the front of the AI-powered drug formulation market, where regulated deployment pathways are being tested in parallel with new technical capabilities.

Europe held a significant share in 2025, supported by Germany, Switzerland, the United Kingdom, and France. The region combines a strong pharma manufacturing base with demanding regulatory and quality expectations, which makes it an important proving ground for validated AI workflows. European buyers appear focused on platforms that can support explainability, documentation, and lifecycle control rather than pure experimental speed alone. This gives the AI-powered drug formulation market in Europe a more measured but structurally important role.

Asia-Pacific is projected to be the fastest-growing region with a 28.31% CAGR through 2031, driven by China, India, South Korea, and Japan. The region is adding momentum through pharmaceutical digitalization, expanding outsourcing capacity, and stronger use of AI tools in development and manufacturing settings. South Korea stands out for active partnership flow, while India's large CRO base supports adoption where service differentiation depends increasingly on speed and analytical capability. China adds scale because digital infrastructure and pharmaceutical modernization are being pushed together in the same operating environment. As a result, the AI-powered drug formulation market is likely to see much of its incremental regional expansion come from Asia-Pacific over the forecast period.

1. Aspen Technology, Inc.
2. Atomwise, Inc.
3. Benchling, Inc.
4. BenevolentAI S.A.
5. Certara, Inc.
6. Cloud Pharmaceuticals
7. Dassault Systemes
8. Dotmatics Limited
9. Exscientia plc
10. ID Business Solutions, Inc.
11. Insilico Medicine
12. IBM
13. Isomorphic Labs Limited
14. Microsoft
15. NVIDIA
16. Recursion Pharmaceuticals
17. Schrodinger, Inc.
18. Simulations Plus
19. TetraScience, Inc.
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 and 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 Rising Need to Reduce Trial-and-Error in Formulation Development
  • 4.2.2 Growth in Biologics, Nucleic Acids, and Advanced Therapy Formulations
  • 4.2.3 Increasing Demand for Personalized and Precision Medicine Dose Design
  • 4.2.4 Expansion of Multi-Modal Pharma Data and Compute Infrastructure
  • 4.2.5 Pharma-CDMO Workflow Digitization and Self-Service Formulation Platforms
  • 4.2.6 Rising Need for Faster Excipient Screening and Stability Prediction
• 4.3 Market Restraints
  • 4.3.1 Sparse, Proprietary, and Non-Standardized Formulation Data
  • 4.3.2 Limited Validation and Explainability for Regulated Use Cases
  • 4.3.3 Integration Friction with Legacy Lab, ELN, and QbD Workflows
  • 4.3.4 High Switching Costs for Enterprise and CDMO Deployment
• 4.4 Supply/Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 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 Component
  • 5.1.1 Software Platforms
  • 5.1.2 Services
• 5.2 By Deployment Mode
  • 5.2.1 Cloud-Based
  • 5.2.2 On-Premises
• 5.3 By Technology
  • 5.3.1 Machine Learning and Deep Learning
  • 5.3.2 Predictive Modeling and Simulation
  • 5.3.3 Generative AI and Neural Networks
  • 5.3.4 Hybrid AI Approaches
  • 5.3.5 Natural Language Processing
• 5.4 By Application
  • 5.4.1 Formulation Design and Optimization
  • 5.4.2 Excipient Compatibility Prediction
  • 5.4.3 Stability and Shelf-Life Prediction
  • 5.4.4 High-Throughput Screening Optimization
  • 5.4.5 Personalized and Precision Medicine Formulations
  • 5.4.6 Advanced Therapy Formulations
• 5.5 By Drug Type
  • 5.5.1 Small Molecules
  • 5.5.2 Biologics
  • 5.5.3 Nucleic Acid-Based Drugs
  • 5.5.4 Cell and Gene Therapy Payloads
  • 5.5.5 Vaccines
• 5.6 By End-User
  • 5.6.1 Pharmaceutical and Biotechnology Companies
  • 5.6.2 Contract Research Organizations
  • 5.6.3 Contract Development and Manufacturing Organizations
  • 5.6.4 Academic and Research Institutes
• 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 Japan
    • 5.7.3.3 India
    • 5.7.3.4 Australia
    • 5.7.3.5 South Korea
    • 5.7.3.6 Rest of Asia-Pacific
  • 5.7.4 Middle East and 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 as available, Strategic Information, Market Rank/Share for key companies, Products & Services, Recent Developments)
  • 6.3.1 Aspen Technology, Inc.
  • 6.3.2 Atomwise, Inc.
  • 6.3.3 Benchling, Inc.
  • 6.3.4 BenevolentAI S.A.
  • 6.3.5 Certara, Inc.
  • 6.3.6 Cloud Pharmaceuticals, Inc.
  • 6.3.7 Dassault Systemes SE
  • 6.3.8 Dotmatics Limited
  • 6.3.9 Exscientia plc
  • 6.3.10 ID Business Solutions, Inc.
  • 6.3.11 Insilico Medicine
  • 6.3.12 International Business Machines Corporation
  • 6.3.13 Isomorphic Labs Limited
  • 6.3.14 Microsoft Corporation
  • 6.3.15 NVIDIA Corporation
  • 6.3.16 Recursion Pharmaceuticals, Inc.
  • 6.3.17 Schrodinger, Inc.
  • 6.3.18 Simulations Plus, Inc.
  • 6.3.19 TetraScience, Inc.
  • 6.3.20 XtalPi Inc.

7 Market Opportunities & Future Outlook

• 7.1 White-space & Unmet-need Assessment