Combining Machine Learning and Molecular Dynamics to Predict P‑Glycoprotein Substrates

The efflux transporter P-glycoprotein (P-gp) is responsible for the extrusion of a wide variety of molecules, including drug molecules, from the cell. Therefore, P-gp-mediated efflux transport limits the bioavailability of drugs. To identify potential P-gp substrates early in the drug discovery process, in silico models have been developed based on structural and physicochemical descriptors. In this study, we investigate the use of molecular dynamics fingerprints (MDFPs) as an orthogonal descriptor for the training of machine learning (ML) models to classify small molecules into substrates and nonsubstrates of P-gp. MDFPs encode the information from short MD simulations of the molecules in different environments (water, membrane, or protein pocket). The performance of the MDFPs, evaluated on both an in-house dataset (3930 compounds) and a public dataset from ChEMBL (1114 compounds), is compared to that of commonly used 2D molecular descriptors, including structure-based and property-based descriptors. We find that all tested classifiers interpolate well, achieving high accuracy on chemically diverse subsets. However, by challenging the models with external validation and prospective analysis, we show that only tree-based ML models trained on MDFPs or property-based descriptors generalize well to regions of the chemical space not covered by the training set.外排转运蛋白P-糖蛋白（P-glycoprotein, P-gp）负责将包括药物分子在内的多种分子从细胞中排出。因此，P-gp介导的外排转运会限制药物的生物利用度。为在药物发现早期阶段识别潜在的P-糖蛋白底物，研究人员已基于结构与理化描述符开发了虚拟模型。本研究探讨了将分子动力学指纹（MDFPs）作为正交描述符，用于训练机器学习（ML）模型以将小分子分类为P-糖蛋白底物与非底物的可行性。分子动力学指纹（MDFPs）编码了分子在不同环境（水相、细胞膜或蛋白质口袋）中进行短时间分子动力学模拟所得到的信息。本研究分别在内部数据集（含3930个化合物）与ChEMBL公开数据集（含1114个化合物）上对MDFPs的性能进行了评估，并与常用的二维分子描述符（包括基于结构的描述符与基于性质的描述符）的性能进行了对比。研究发现，所有经测试的分类器均具备良好的内插性能，在化学多样性子集上可实现较高的分类准确率。然而，通过外部验证与前瞻性分析对模型进行性能测试后，本研究表明，仅基于MDFPs或基于性质的描述符训练得到的基于树的机器学习模型，能够很好地泛化至训练集未覆盖的化学空间区域。