AI in Predictive Toxicology Market Size & Share 2023 to 2032
Market Size by Technology (Machine Learning, Natural Language Processing, Computer Vision), Toxicity Endpoints (Genotoxicity, Hepatotoxicity, Neurotoxicity, Cardiotoxicity), Component, End User & Global Forecast.
Report ID: GMI7363
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Published Date: November 2023
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Report Format: PDF
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Authors:
Preeti Wadhwani, Aishvarya Ambekar
AI in Predictive Toxicology Market Size
AI in Predictive Toxicology Market size was valued at USD 281 million in 2022 and is estimated to register a CAGR of over 29.5% between 2023 and 2032. The increasing investments in pharmaceutical AI startups are driving the market growth. These funds enable the development and implementation of advanced technologies, such as Machine Learning (ML) and predictive modeling, to enhance toxicological assessments of chemical compounds.
AI in Predictive Toxicology Market Key Takeaways
Market Size & Growth
Key Market Drivers
Challenges
For instance, in December 2022, Quris Technologies Ltd., an Israeli pharmaceutical AI startup, gained an extra USD 9 million in seed funding, bringing the total raised amount to USD 37 million. The funding round was spearheaded by SoftBank Vision Fund 2, with contributions from current investors including GlenRock Capital, iAngels, Welltech Ventures, and Richter Group.
Advancements in AI technologies, particularly in ML and deep learning, play a pivotal role in propelling the AI in predictive toxicology market. These technologies enhance the capability to analyze complex data sets, recognize intricate patterns, and generate more accurate predictions regarding the toxicological properties of chemical compounds. The continuous refinement of AI algorithms and the integration of sophisticated computational techniques contribute to the development of robust & reliable models, making AI a key factor in advancing the field of predictive toxicology.
The quality and availability of data pose a significant barrier to the AI in predictive toxicology market growth. Inadequate or suboptimal datasets can compromise the training and validation of ML models, potentially leading to inaccurate predictions. Issues, such as data incompleteness, biases, or variability, can undermine the reliability of AI applications. Ensuring access to high-quality, diverse, and representative datasets is crucial for developing robust predictive models in toxicology, but acquiring such data can be a complex and resource-intensive task.
COVID-19 Impact
The COVID-19 pandemic positively impacted the AI in predictive toxicology market. The increased focus on drug development and the urgency for efficient solutions prompted a heightened interest in AI applications for predictive toxicology. The pandemic accelerated the adoption of advanced technologies, encouraging pharmaceutical companies to invest in innovative approaches. There has been a surge in the demand for faster & more accurate toxicity assessments, facilitated by the integration of AI. This has contributed to market size, establishing it as a crucial tool in the pharmaceutical research & development landscape.
AI in Predictive Toxicology Market Trends
The utilization of AI operating systems to accelerate drug development is fostering lucrative growth in the AI in predictive toxicology industry. By swiftly identifying and developing promising drug candidates, these systems streamline the drug development process. For instance, in November 2023, BioPhy unveiled its AI operating system, aiming to significantly expedite the discovery and development of promising drug candidates. Integrating clinical, scientific, and regulatory insights with a unique operational assessment model, BioPhy's AI platform evaluates biological feasibility and forecasts the probability of a positive outcome in clinical trials. Overall, this approach is spurring the adoption of AI in predictive toxicology, fostering a robust & profitable market landscape.
The heightened demand for streamlined drug development processes is propelling the AI in predictive toxicology industry. As pharmaceutical companies seek more efficient approaches, AI plays a pivotal role in expediting toxicological assessments. By leveraging ML and predictive modeling, AI enables rapid identification of potential drug candidates, reducing time and costs. This increased efficiency in drug development aligns with industry needs, boosting the adoption of AI technologies for predictive toxicology and contributing to the market's growth.
AI in Predictive Toxicology Market Analysis
Based on the component, the solution segment held over 70% of the market share in 2022. Advanced precision medicine solutions are fueling the market. These solutions, with their sophisticated capabilities, play a crucial role in tailoring treatments by interpreting genomic data swiftly and accurately.
For instance, in May 2023, Google Cloud introduced two innovative AI-driven life sciences solutions, aiming to expedite drug discovery and enhance precision medicine across the healthcare sector. The Target & Lead Identification Suite aids researchers in improved identification of amino acid functions and the prediction of protein structures. The Multiomics Suite accelerates the discovery and interpretation of genomic data, assisting companies in the development of precision treatments.
Based on the end user, the pharmaceutical & biotechnology companies segment accounted for 52% of the AI in predictive toxicology market share in 2022, owing to their substantial investments in research & development while prioritizing the need for streamlined drug development. Faced with intense competition, these firms leverage AI technologies to accelerate the drug discovery process, optimizing efficiency and reducing time-to-market. Their financial resources and in-house expertise enable seamless integration of AI, empowering data-driven decision making and compliance with rigorous regulatory standards, ultimately providing a competitive edge in the dynamic landscape of pharmaceutical innovations.
North America AI in predictive toxicology market recorded around 44% of the revenue share in 2022. The strong presence of the pharmaceutical industry in the region is a key factor propelling the market. The region's pharmaceutical companies are witnessing the need for more efficient drug development processes. Embracing AI technologies in predictive toxicology allows these companies to accelerate drug discovery, optimize research & development efforts, and reduce the overall costs. The competitive landscape and the constant pursuit of innovative solutions in the pharmaceutical sector contribute significantly to the demand for advanced AI applications in predictive toxicology in North America.
AI in Predictive Toxicology Market Share
Major companies operating in the AI in predictive toxicology industry are:
Major companies in the AI in predictive toxicology market are fiercely competing for a share through substantial investments in R&D along with technological advancements. This strategy is aimed at developing cutting-edge solutions, stay ahead in innovations, and capture a significant share of the rapidly evolving predictive toxicology market.
AI in Predictive Toxicology Industry News
The AI in predictive toxicology market research report includes in-depth coverage of the industry with estimates & forecast in terms of revenue (USD Million) from 2018 to 2032, for the following segments:
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Market, By Component
Market, By Technology
Market, By Toxicity Endpoints
Market, By End User
The above information has been provided for the following regions and countries:
Research methodology, data sources & validation process
This report draws on a structured research process built around direct industry conversations, proprietary modelling, and rigorous cross-validation and not just desk research.
Our 6-step research process
1. Research design & analyst oversight
At GMI, our research methodology is built on a foundation of human expertise, rigorous validation, and complete transparency. Every insight, trend analysis, and forecast in our reports is developed by experienced analysts who understand the nuances of your market.
Our approach integrates extensive primary research through direct engagement with industry participants and experts, complemented by comprehensive secondary research from verified global sources. We apply quantified impact analysis to deliver dependable forecasts, while maintaining complete traceability from original data sources to final insights.
2. Primary research
Primary research forms the backbone of our methodology, contributing nearly 80% to overall insights. It involves direct engagement with industry participants to ensure accuracy and depth in analysis. Our structured interview program covers regional and global markets, with inputs from C-suite executives, directors, and subject matter experts. These interactions provide strategic, operational, and technical perspectives, enabling well-rounded insights and reliable market forecasts.
3. Data mining & market analysis
Data mining is a key part of our research process, contributing nearly 20% to the overall methodology. It involves analysing market structure, identifying industry trends, and assessing macroeconomic factors through revenue share analysis of major players. Relevant data is collected from both paid and unpaid sources to build a reliable database. This information is then integrated to support primary research and market sizing, with validation from key stakeholders such as distributors, manufacturers, and associations.
4. Market sizing
Our market sizing is built on a bottom-up approach, starting with company revenue data gathered directly through primary interviews, alongside production volume figures from manufacturers and installation or deployment statistics. These inputs are then pieced together across regional markets to arrive at a global estimate that stays grounded in actual industry activity.
5. Forecast model & key assumptions
Every forecast includes explicit documentation of:
✓ Key growth drivers and their assumed impact
✓ Restraining factors and mitigation scenarios
✓ Regulatory assumptions and policy change risk
✓ Technology adoption curve parameter
✓ Macroeconomic assumptions (GDP growth, inflation, currency)
✓ Competitive dynamics and market entry/exit expectations
6. Validation & quality assurance
The final stages involve human validation, where domain experts manually review filtered data to identify nuances and contextual errors that automated systems might miss. This expert review adds a critical layer of quality assurance, ensuring data aligns with research objectives and domain-specific standards.
Our triple-layer validation process ensures maximum data reliability:
✓ Statistical Validation
✓ Expert Validation
✓ Market Reality Check
Trust & credibility
Verified data sources
Trade publications
Security & defense sector journals and trade press
Industry databases
Proprietary and third-party market databases
Regulatory filings
Government procurement records and policy documents
Academic research
University studies and specialist institution reports
Company reports
Annual reports, investor presentations, and filings
Expert interviews
C-suite, procurement leads, and technical specialists
GMI archive
13,000+ published studies across 30+ industry verticals
Trade data
Import/export volumes, HS codes, and customs records
Parameters studied & evaluated
Every data point in this report is validated through primary interviews, true bottom-up modelling, and rigorous cross-checks. Read about our research process →