A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Drug toxicity prediction is an important step in ensuringpatientsafety during drug design studies. While traditional preclinical studieshave historically relied on animal models to evaluate toxicity, recentadvances in deep-learning approaches have shown great promise in advancingdrug safety science and reducing animal use in preclinical studies.However, deep-learning-based approaches also face challenges in handlinglarge biological data sets, model interpretability, and regulatoryacceptance. In this review, we provide an overview of recent developmentsin deep-learning-based approaches for predicting drug toxicity, highlightingtheir potential advantages over traditional methods and the need toaddress their limitations. Deep-learning models have demonstratedexcellent performance in predicting toxicity outcomes from variousdata sources such as chemical structures, genomic data, and high-throughputscreening assays. The potential of deep learning for automated featureengineering is also discussed. This review emphasizes the need toaddress ethical concerns related to the use of deep learning in drugtoxicity studies, including the reduction of animal use and ensuringregulatory acceptance. Furthermore, emerging applications of deeplearning in drug toxicity prediction, such as predicting drug-druginteractions and toxicity in rare subpopulations, are highlighted.The integration of deep-learning-based approaches with traditionalmethods is discussed as a way to develop more reliable and efficientpredictive models for drug safety assessment, paving the way for saferand more effective drug discovery and development. Overall, this reviewhighlights the critical role of deep learning in predictive toxicologyand drug safety evaluation, emphasizing the need for continued researchand development in this rapidly evolving field. By addressing thelimitations of traditional methods, leveraging the potential of deeplearning for automated feature engineering, and addressing ethicalconcerns, deep-learning-based approaches have the potential to revolutionizedrug toxicity prediction and improve patient safety in drug discoveryand development.

Automated summary

Drug toxicity prediction is a crucial step in ensuring patient safety during drug design studies. Recent advances in deep-learning approaches have shown promise in improving drug safety science and reducing animal use. However, challenges in handling large biological datasets, model interpretability, and regulatory acceptance need to be addressed. This review highlights the potential advantages of deep-learning models in predicting drug toxicity and emphasizes the importance of addressing ethical concerns and integrating deep-learning approaches with traditional methods.