What Is the Role of AI in Toxicology Screening?
What Is the Role of AI in Toxicology Screening?
By Rasit Dinc
The field of toxicology, dedicated to understanding the adverse effects of chemicals on living organisms, is undergoing a profound transformation. For decades, toxicological screening has relied on time-consuming and expensive methods, often involving animal testing, which have limitations in predicting human-specific responses [1]. However, the advent of artificial intelligence (AI) and machine learning is ushering in a new era of predictive toxicology, offering faster, more accurate, and ethically sound approaches to safety assessment [2].
The Dawn of a Data-Driven Era in Toxicology
Traditionally, toxicology has been a discipline heavily reliant on empirical observation. However, the proliferation of large-scale datasets, including genomic, transcriptomic, and metabolomic data, has created a data-rich environment ripe for AI integration [3]. AI algorithms are exceptionally well-suited to sift through these vast and diverse datasets, identifying complex patterns and correlations that would be impossible for human researchers to discern. This ability to harness big data is at the heart of AI's transformative potential in toxicology, enabling a shift from a reactive to a predictive science [2].
Key Applications of AI in Toxicology Screening
AI is not a single technology but a collection of methods and approaches that are being applied across the spectrum of toxicological research. Some of the most promising applications include:
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Quantitative Structure-Activity Relationship (QSAR) Modeling: QSAR models use machine learning algorithms to predict the toxicity of a chemical based on its molecular structure. These models can rapidly screen large chemical libraries, prioritizing compounds for further testing and reducing the need for animal studies [1].
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Physiologically Based Pharmacokinetic (PBPK) Modeling: PBPK models simulate the absorption, distribution, metabolism, and excretion (ADME) of chemicals in the body. AI can enhance these models by incorporating complex biological data, leading to more accurate predictions of human exposure and toxicity [1].
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High-Throughput Screening (HTS): HTS technologies can test thousands of chemicals for biological activity in a short period. AI can analyze the vast amounts of data generated by HTS assays, identifying potential toxic effects and mechanisms of action [1].
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Adverse Outcome Pathway (AOP) Analysis: AOPs provide a framework for understanding the chain of events from a molecular initiating event to an adverse outcome at the organism or population level. AI can help to identify and validate AOPs, providing a mechanistic basis for toxicity prediction [1].
The Multifaceted Benefits of AI in Toxicology
The integration of AI into toxicology screening offers a multitude of benefits. By providing earlier and more accurate predictions of toxicity, AI can significantly reduce the high attrition rates in drug development, saving both time and money [3]. Furthermore, by reducing the reliance on animal testing, AI aligns with the ethical principles of the 3Rs (Replacement, Reduction, and Refinement) and addresses growing public concern over animal welfare [3]. The ability of AI to analyze complex datasets also provides deeper insights into the mechanisms of toxicity, paving the way for the development of safer chemicals and drugs.
Navigating the Challenges and Looking to the Future
Despite its immense promise, the widespread adoption of AI in toxicology is not without its challenges. Ensuring the quality and reliability of the data used to train AI models is paramount [3]. The "black box" nature of some machine learning models can also be a hurdle, as it can be difficult to understand the reasoning behind their predictions. The development of explainable AI (xAI) is a crucial area of research to address this issue [2]. Furthermore, the regulatory acceptance of AI-based methods is a critical step for their integration into routine safety assessment.
Looking ahead, the future of AI in toxicology is bright. Continued advancements in machine learning, coupled with the generation of high-quality toxicological data, will undoubtedly lead to more powerful and predictive models. The integration of AI with other emerging technologies, such as organ-on-a-chip and CRISPR-Cas9, holds the potential to create a new paradigm for toxicology that is more predictive, human-relevant, and ethically sound. As we continue to navigate the challenges, the transformative power of AI is poised to revolutionize toxicology, leading to a safer and healthier world.
References
[1] Lin, Z., & Chou, W. C. (2022). Machine Learning and Artificial Intelligence in Toxicological Sciences. Toxicological Sciences, 189(1), 7–19. https://doi.org/10.1093/toxsci/kfac075
[2] Hartung, T. (2023). Artificial intelligence as the new frontier in chemical risk assessment. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1269932
[3] Ajisafe, O. M., Adekunle, Y. A., Egbon, E., Ogbonna, C. E., & Olawade, D. B. (2025). The role of machine learning in predictive toxicology: A review of current trends and future perspectives. Life Sciences, 378, 123821. https://doi.org/10.1016/j.lfs.2025.123821