AI in Predictive Toxicology Market Outlook 2025–2032: Strong CAGR of 29.7% with USD 3,925.5 Million Valuation

Burlingame, CA, Nov. 11, 2025 (GLOBE NEWSWIRE) -- The Global AI in Predictive Toxicology Market is estimated to be valued at USD 635.8 Mn in 2025 and is expected to reach USD 3,925.5 Mn by 2032, exhibiting a compound annual growth rate (CAGR) of 29.7% from 2025 to 2032. Market trends reveal a significant shift toward the integration of advanced machine learning algorithms and big data analytics to enhance predictive accuracy and minimize dependence on animal testing. Additionally, the growing focus on personalized medicine and the rapid expansion of AI-powered platforms in pharmaceutical R&D are further driving market growth.

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Global AI in Predictive Toxicology Market Key Takeaways

Global demand for AI in predictive toxicology is poised to rise at a CAGR of 29.7% between 2025 and 2032.

Based on technology, classical machine learning segment is projected to dominate the market with a share of 56.1% in 2025.

North America is slated to account for over two-fifths of the global AI in predictive toxicology market share by 2025.

Asia Pacific, with an estimated share of 21.5% in 2025, is poised to emerge as the fastest-growing AI in predictive toxicology market during the forecast period.

Rising Demand for Faster & Cost-Effective Drug Development Fueling Market Growth

Coherent Market Insights’ latest AI in predictive toxicology market analysis outlines prominent factors fueling industry growth. Increasing demand for faster and cost-effective drug development is one such key growth factor.

Conventional drug development is an expensive and time-consuming process. This prompts companies to use advanced technologies like artificial intelligence (AI) across various drug development stages, including toxicology.

AI-based predictive toxicology allows earlier identification of potential toxic effects, reducing late-stage failure risk. This helps pharmaceutical companies and CROs save time and costs. Thus, growing need for efficient and cost-effective drug development is expected to boost growth of AI in predictive toxicology market during the forecast period.

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Limited Availability of High-quality Toxicological Data Hampering Growth

The global AI in predictive toxicology market outlook appears bright, owing to advancements in machine learning and growing need for faster, animal-free toxicity testing. However, limited availability of high-quality toxicological data might limit market growth to some extent.

AI models need big and reliable datasets to accurately predict chemical toxicity. However, much of the current toxicology data is inconsistent, incomplete, or not reported in a standard way. This makes it hard for AI to give trustworthy results, which lowers confidence among researchers and regulators.

Another key challenge is the lack of clear regulatory guidelines for AI-based toxicology tools. While these systems show potential to streamline drug discovery as well as reduce costs, most regulatory agencies still rely on traditional in vivo studies for toxicity validation.

Transition Towards Non-Animal Testing Unlocking New Growth Prospects

Rising focus on using non-animal testing methodologies is expected to create growth avenues for AI in predictive toxicology market companies. There’s a strong global regulatory and ethical push (e.g., EU REACH, U.S. FDA Modernization Act 2.0) to replace animal models with in silico methods.

AI-based predictive toxicology models support this shift by providing accurate toxicity assessments using computational data. For instance, FDA recently announced plans to reduce or replace animal testing using new methods such as AI-based toxicity models, cell lines, and organoid testing, known as New Approach Methodologies (NAMs).

Emerging AI in Predictive Toxicology Market Trends

Availability of large datasets is encouraging adoption of AI in predictive toxicology. The growth of omics data, chemical libraries, and toxicogenomic databases provides the training material needed for AI algorithms to predict toxic effects more reliably.

There is a rising trend of using AI together with 3D cell culture models like organoids/spheroids. This provides more human-relevant data for training and validation, improving the predictive power of toxicity screening models.

The scope of AI in predictive toxicology is expanding rapidly in non-pharmaceutical sectors. For instance, predictive toxicology using AI is now applied in cosmetics, food safety, agrochemicals, and environmental toxicology.

Advances in machine learning, deep learning, graph neural networks, and generative modelling are increasing the predictive ability of toxicity models. These innovations could unlock new growth opportunities for market players during the forecast period.

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Analyst’s View

“The global AI in predictive toxicology industry is set to grow rapidly owing to rising demand for faster and cost-effective drug development, shift towards non-animal testing, availability of large datasets, advancements in artificial intelligence, and expanding applications beyond pharmaceuticals,” said a senior analyst at CMI.

Current Events and Their Impact on the AI in Predictive Toxicology Market

Event	Description and Impact
Regulatory Evolution in AI-Driven Drug Safety	Description: FDA's AI/ML Guidance Updates. Impact: Faster market adoption of AI toxicology tools is happening because clearer rules make it easier for pharmaceutical companies to follow regulations.
Major Pharmaceutical Industry Consolidation	Description: Big Pharma AI Acquisition Spree. Impact: Increasing market consolidation as companies acquire AI toxicology startups, potentially reducing competition but accelerating technology integration.
Breakthrough AI Model Developments	Description: Large Language Models in Life Sciences. Impact: Enabling more sophisticated toxicity prediction through better understanding of molecular interactions.

Competitor Insights

Key companies in the AI in predictive toxicology market report:

Simulations Plus
Lhasa Limited
Schrödinger
Exscientia
Insilico Medicine
Certara
Clarivate
Atomwise
Charles River Laboratories
MultiCASE
Valo Health
Optibrium
Exvotec
Inotiv
Chemical Computing Group (CCG)

Key Developments

In June 2025, Simulations Plus released ADMET Predictor 13, its main ML modeling platform for drug discovery. The new version includes better prediction models, smarter AI tools, and updated features to make it easier as well as faster to estimate ADMET properties and support drug development.

In July 2024, Schrodinger launched a new initiative to expand use of computational tools for predictive toxicology. The goal of this initiative is to create a computational solution that makes new drug candidates safer as well as reduces the chances of failure during development.

In June 2024, Exscientia launched AWS AI-powered platform for drug discovery. The platform uses generative AI and AWS’s speed and flexibility to design better, faster, and cheaper drug candidates tailored to specific diseases and patients.

Market Segmentation

Technology Insights (Revenue, USD Mn, 2020 - 2032)

Classical Machine Learning
Deep Learning
Physics-based & Molecular Modelling
Others

Regional Insights (Revenue, USD Mn, 2020 - 2032)

North America
- U.S.
- Canada
Latin America
- Brazil
- Argentina
- Mexico
- Rest of Latin America
Europe
- Germany
- U.K.
- Spain
- France
- Italy
- Russia
- Rest of Europe
Asia Pacific
- China
- India
- Japan
- Australia
- South Korea
- ASEAN
- Rest of Asia Pacific
Middle East
- GCC Countries
- Israel
- Rest of Middle East
Africa
- South Africa
- North Africa
- Central Africa

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