Department of Zoology, Jhargram Raj College, Jhargram 721507, West Bengal, India.
Department of Zoology, Maulana Azad College, Kolkata 700013, West Bengal, India.
Chem Res Toxicol. 2023 Aug 21;36(8):1174-1205. doi: 10.1021/acs.chemrestox.2c00375. Epub 2023 Aug 10.
Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.
药物毒性预测是确保药物设计研究中患者安全的重要步骤。虽然传统的临床前研究历史上依赖于动物模型来评估毒性，但深度学习方法的最新进展在推进药物安全科学和减少临床前研究中的动物使用方面显示出了巨大的潜力。然而，基于深度学习的方法在处理大型生物数据集、模型可解释性和监管接受度方面也面临挑战。在这篇综述中，我们提供了基于深度学习的药物毒性预测方法的最新发展概述，强调了它们相对于传统方法的潜在优势，以及解决其局限性的必要性。深度学习模型在预测来自各种数据源（如化学结构、基因组数据和高通量筛选测定）的毒性结果方面表现出了优异的性能。还讨论了深度学习在自动特征工程中的潜力。这篇综述强调了需要解决与深度学习在药物毒性研究中的应用相关的伦理问题，包括减少动物使用和确保监管部门的接受。此外，还强调了深度学习在药物毒性预测中的新兴应用，如预测药物-药物相互作用和稀有亚人群中的毒性。讨论了将基于深度学习的方法与传统方法相结合，以开发更可靠和高效的药物安全性评估预测模型的方法，为更安全、更有效的药物发现和开发铺平了道路。总的来说，这篇综述强调了深度学习在预测毒理学和药物安全评估中的关键作用，强调了在这个快速发展的领域中需要持续的研究和开发。通过解决传统方法的局限性、利用深度学习在自动特征工程方面的潜力以及解决伦理问题，基于深度学习的方法有可能彻底改变药物毒性预测，并提高药物发现和开发过程中的患者安全性。
