AI-Powered Drug Formulation Market Size, Share, Trends, and Growth Forecasts, 2026-2031 | Cloud Adoption and Advanced Therapies Propel 24.37% CAGR to USD 2.77 Billion

AI-Powered Drug Formulation Market Size, Share, Trends, and Growth Forecasts, 2026-2031 | Cloud Adoption and Advanced Therapies Propel 24.37% CAGR to USD 2.77 Billion Dublin, Nov. 13, 2026 (GLOBE NEWSWIRE) -- "AI-Powered Drug Formulation - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2026-2031)" has been added to ResearchAndMarkets.com's offering.

The global AI-powered drug formulation market is projected to grow from USD 0.77 billion in 2025 and USD 0.93 billion in 2026 to USD 2.77 billion by 2031. The market is expected to register a CAGR of 24.37% between 2026 and 2031, supported by rising pharmaceutical development costs, demand for faster formulation workflows, and continued growth in complex therapies.

Demand for Faster, More Efficient Formulation Development

Pharmaceutical and biotechnology companies are adopting AI-powered drug formulation tools to reduce trial-and-error, conserve active pharmaceutical ingredients, and shorten development timelines. Late-stage formulation failures can delay regulatory filings, increase costs, and require manufacturing changes after clinical development has advanced. Predictive formulation technologies help development teams identify promising candidates earlier and make more informed decisions before committing resources to laboratory testing.

Regulatory attention to pharmaceutical quality, drug shortages, and manufacturing reliability is also encouraging earlier implementation of quality-by-design strategies. A 2026 Nature Communications study reported 60% lower development time and 65% lower API use in an AI-powered tableting workflow evaluated across 1,199 data points from 170 formulations. These findings demonstrate the potential for AI to improve material efficiency and accelerate formulation decisions. As a result, adoption is progressing from limited pilot projects to routine use by pharmaceutical sponsors and outsourced development partners.

Complex Therapies Create New Growth Opportunities

Expansion in biologics, nucleic acid-based drugs, vaccines, and cell and gene therapies is increasing demand for advanced formulation technologies. These products require precise control of stability, delivery systems, process conditions, and manufacturing performance. AI platforms can evaluate complex datasets and support formulation strategies for high-value therapies with demanding quality requirements.

A 2026 Nature Reviews Materials article highlighted the transition from combinatorial lipid nanoparticle screening to generative design. The LiGen model reported average predicted performance gains of 30.7% compared with retrieval-based baselines. In November 2025, Chime Biologics also launched an AI platform covering cell line development, bioprocessing, and CMC support. Such developments indicate that integrated AI capabilities are becoming increasingly important across biologics development and manufacturing.

Data Quality Remains a Key Market Restraint

The limited availability of standardized formulation data continues to restrict broader adoption. Formulation datasets often contain relatively few experiments, while unsuccessful results may remain unpublished or confined within individual organizations. Differences in data structures, testing methods, and documentation practices can further limit model performance across drug classes and formulation types.

A 2026 International Journal of Pharmaceutics study introduced a structured machine-readable oral formulation database to help address this challenge. However, many current models continue to perform best within specific process windows or therapeutic categories. Greater data standardization, improved interoperability, and access to validated datasets will be important for expanding model generalizability. Additional adoption barriers include limited explainability and the need for stronger validation in regulated use cases.

Software Platforms and Cloud-Based Deployment Lead Growth

Software platforms accounted for 51.38% of the AI-powered drug formulation market in 2025 and are forecast to expand at a CAGR of 24.61% through 2031. Demand is strongest for platforms that combine predictive modeling, data management, workflow traceability, and collaboration capabilities. Buyers increasingly prefer solutions that integrate with broader research, development, and CMC operations rather than tools designed for isolated technical tasks.

Services remain important for implementation, model configuration, validation, integration, and program-specific scientific support. As software-led workflows become more established, platform providers are expected to generate additional opportunities through consulting and technical services.

Cloud-based deployment represented 52.72% of the market in 2025 and is projected to grow at a CAGR of 25.18% through 2031. Cloud platforms allow organizations to scale computing capacity based on project requirements while reducing dependence on internal infrastructure. This model is particularly beneficial for mid-sized pharmaceutical and biotechnology companies seeking advanced capabilities without substantial hardware investment. On-premises deployment remains relevant for organizations with strict intellectual property, data sovereignty, or internal governance requirements.

Regional Market Outlook

North America led the AI-powered drug formulation market with a 44.19% share in 2025. The region benefits from a large pharmaceutical industry, active venture investment, extensive research capabilities, and a strong concentration of software and biosimulation providers. The United States remains the primary regional market due to its combination of sponsor demand, technology development, and regulatory engagement.

Europe also maintained a significant market position, supported by pharmaceutical and manufacturing activity in Germany, Switzerland, the United Kingdom, and France. Regional buyers are placing particular emphasis on explainability, documentation, validation, and lifecycle control for AI-enabled workflows.

Asia-Pacific is expected to record the fastest regional growth, with a projected CAGR of 28.31% through 2031. Expansion is being driven by pharmaceutical digitalization, growing outsourcing capacity, and increased AI adoption across China, India, South Korea, and Japan. India's established CRO sector, South Korea's partnership activity, and China's investment in digital infrastructure and pharmaceutical modernization are expected to support substantial market growth throughout the forecast period.

Key Topics Covered

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

For more information about this report visit https://www.researchandmarkets.com/r/a31b5i

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