Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, Changchun 130012, China.
Edmond Henri Fischer Signal Transduction Laboratory, School of Life Sciences, Jilin University, Changchun 130012, China.
Int J Mol Sci. 2024 Jul 22;25(14):7978. doi: 10.3390/ijms25147978.
Reproductive toxicity poses significant risks to fertility and progeny health, making its identification in pharmaceutical compounds crucial. In this study, we conducted a comprehensive in silico investigation of reproductive toxic molecules, identifying three distinct categories represented by Dimethylhydantoin, Phenol, and Dicyclohexyl phthalate. Our analysis included physicochemical properties, target prediction, and KEGG and GO pathway analyses, revealing diverse and complex mechanisms of toxicity. Given the complexity of these mechanisms, traditional molecule-target research approaches proved insufficient. Support Vector Machines (SVMs) combined with molecular descriptors achieved an accuracy of 0.85 in the test dataset, while our custom deep learning model, integrating molecular SMILES and graphs, achieved an accuracy of 0.88 in the test dataset. These models effectively predicted reproductive toxicity, highlighting the potential of computational methods in pharmaceutical safety evaluation. Our study provides a robust framework for utilizing computational methods to enhance the safety evaluation of potential pharmaceutical compounds.
生殖毒性对生育能力和后代健康构成重大风险，因此在药物化合物中识别生殖毒性至关重要。在这项研究中，我们对生殖毒性分子进行了全面的计算研究，确定了由二甲基海因、苯酚和二环己基邻苯二甲酸酯代表的三个不同类别。我们的分析包括物理化学性质、靶标预测以及 KEGG 和 GO 通路分析，揭示了毒性的多种复杂机制。鉴于这些机制的复杂性，传统的分子靶标研究方法证明是不够的。支持向量机 (SVM) 与分子描述符相结合，在测试数据集中达到了 0.85 的准确率，而我们的自定义深度学习模型，将分子 SMILES 和图集成在一起，在测试数据集中达到了 0.88 的准确率。这些模型有效地预测了生殖毒性，突出了计算方法在药物安全性评估中的潜力。我们的研究为利用计算方法增强潜在药物化合物的安全性评估提供了一个强大的框架。
