AI predictive toxicology market seen topping $2B by 2030

The Business Research Company says the AI in predictive toxicology market is headed for rapid growth through 2030, driven by demand for faster drug discovery, more data and growing regulatory pressure to reduce animal testing. Asia-Pacific is forecast to lead regional growth, while the U.S. is expected to remain the largest country market.

Why it matters: - AI is moving deeper into drug safety work, where faster toxicity prediction can cut late-stage failures and speed preclinical decisions. - The market’s projected growth signals broader adoption of computational toxicology across pharma, biotech, chemical and cosmetics sectors. - The shift could also support efforts to reduce animal testing and improve compliance with changing safety standards.

What happened: - The Business Research Company released its Artificial Intelligence in Predictive Toxicology Global Market Report 2026–Market Size, Forecast 2026-2035. - The report projects the AI in predictive toxicology market will exceed $2 billion by 2030. - The market is forecast to grow at a 29% compound annual growth rate through 2030. - The report says the broader AI in pharma market will reach about $11 billion by 2030. - AI in predictive toxicology is expected to account for about 21% of that wider AI in pharma market. - The market is forecast to represent about 0.02% of the global information technology market, which is projected to reach $13,788 billion by 2030.

The details: - Asia-Pacific is forecast to be the largest regional market by 2030 at about $0.80 billion. - Asia-Pacific market value is projected to rise from $0.18 billion in 2025, implying a 34% CAGR. - The report points to pharmaceutical and biotech research growth, AI drug-discovery investment, contract research organization adoption and government support as growth drivers in the region. - The USA is projected to be the largest country market by 2030 at $0.69 billion. - U.S. market value is expected to grow from $0.22 billion in 2025 at a 26% CAGR. - The solutions segment is expected to hold the largest component share at 63% of the market, or about $1 billion in 2030. - The market is segmented by technology into machine learning and other advanced methods. - The report groups toxicity endpoints into genotoxicity, hepatotoxicity, neurotoxicity, cardiotoxicity and other categories. - End users include pharmaceutical and biotechnology companies, chemical and cosmetic industries, contract research organizations and others. - The solutions and services segments together are expected to surpass $2 billion by 2030. - Between 2025 and 2030, the solutions segment is expected to grow by $1 billion, with services expanding by a similar amount. - Certara Inc. led the global market in 2025 with a 4% share. - Other companies named in the market include Schrödinger, Simulations Plus, Instem, Recursion Pharmaceuticals, Insilico Medicine, BenevolentAI, Merative, ChemAxon and Optibrium. - The top 10 companies accounted for 23% of revenue in 2025, indicating a moderately fragmented market. - The report lists major suppliers and infrastructure players including Thermo Fisher Scientific, Illumina, QIAGEN, Amazon Web Services, Microsoft, Google Cloud and NVIDIA. - Major end users named in the report include Pfizer, Roche, Novartis, Merck, Johnson & Johnson, AstraZeneca, Sanofi, Bristol Myers Squibb, Eli Lilly, GSK, Bayer, AbbVie, Amgen, Boehringer Ingelheim, Takeda, Novo Nordisk, Regeneron, Biogen, Moderna and Gilead.

Between the lines: - The report’s growth case rests on three forces: faster drug discovery, richer data sets and stronger pressure to reduce animal testing. - More toxicological, genomic, proteomic, chemical and clinical data should improve model performance and widen use cases. - The moderate concentration level suggests room for both platform leaders and newer entrants, but barriers remain high because validation, data access and regulatory acceptance matter. - Strategic partnerships are becoming a key competitive tool, including a January 2024 collaboration between SyntheticGestalt and Enamine to build AI models using Enamine’s REAL database of 38 billion molecules. - That partnership is designed to optimize synthetic accessibility and physicochemical and ADME/Tox profiles, underscoring the industry’s push toward safer compound design earlier in discovery.

What's next: - The report expects continued growth in AI-powered toxicity screening, cloud-based predictive analytics and explainable AI tools. - Companies are likely to keep investing in multiomics integration, regulatory-ready in silico testing and platform expansion. - Competitive positioning will likely hinge on partnerships, model accuracy and the ability to support drug discovery workflows at scale.

The bottom line: - AI in predictive toxicology is moving from a niche capability to a core drug-development tool, with the strongest growth expected in markets that combine deep life sciences activity, data availability and regulatory openness.