Li Junyao, Zhang Jianmei, Guo Rui, Dai Jiawei, Niu Zhiqiang, Wang Yan, Wang Taoyun, Jiang Xiaojian, Hu Weicheng
School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou, China; School of Life Sciences, Huaiyin Normal University, Huaian, 223300, China; Institute of Translational Medicine, School of Medicine, Yangzhou University, Yangzhou, 225009, China.
School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou, China.
Eur J Med Chem. 2025 Mar 5;285:117269. doi: 10.1016/j.ejmech.2025.117269. Epub 2025 Jan 10.
Machine learning (ML) has become an important tool for predicting the pharmaceutical properties of small molecules. Recent advancements in ML algorithms enable the rapid and accurate evaluation of solubility, activity, toxicity, pharmacokinetics, and other molecular properties through ML-based models. By conducting virtual screening of drug targets and elucidating drug-target protein interactions, researchers can conduct preliminary evaluations of the activity and safety of compounds from the ultra-large drug compound libraries, thereby accelerating the screening process for lead compounds. Moreover, ML leverages existing experimental data to train and generate new datasets, addressing the challenge of limited compounds and protein target data. This review provided a concise overview of ML applications in predicting small molecule properties, focusing on model construction principles, molecular feature selection, and other essential aspects. It also discussed the potential applications of ML in the screening of pharmaceutical small molecules.
机器学习（ML）已成为预测小分子药物特性的重要工具。ML算法的最新进展使得通过基于ML的模型能够快速、准确地评估溶解度、活性、毒性、药代动力学和其他分子特性。通过对药物靶点进行虚拟筛选并阐明药物-靶点蛋白相互作用，研究人员可以对来自超大型药物化合物库的化合物的活性和安全性进行初步评估，从而加速先导化合物的筛选过程。此外，ML利用现有的实验数据来训练和生成新的数据集，解决了化合物和蛋白质靶点数据有限的挑战。本综述简要概述了ML在预测小分子特性方面的应用，重点关注模型构建原理、分子特征选择和其他重要方面。它还讨论了ML在药物小分子筛选中的潜在应用。
