Jining First People's Hospital, Jining Medical University, Jining, China.
Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China.
Mol Divers. 2020 May;24(2):407-412. doi: 10.1007/s11030-019-09961-4. Epub 2019 May 27.
Methods of three-dimensional molecular alignment generally treat all pharmacophore features equally when superimposing. However, some pharmacophore features can be more important in a specific system. In this work, we derived the overlap volume of pharmacophore features from a molecular alignment approach as new features of molecules to build machine learning models. Features can be assigned weights to indicate their importance. With validation on DUD-E collection, models based on pharmacophore features represented by the overlap volume yielded significant performances with median AUC of approximately 0.98 and recall rate of almost 0.8.
三维分子对接方法在叠加时通常平等对待所有药效团特征。然而，在特定系统中，某些药效团特征可能更为重要。在这项工作中，我们从分子对接方法中得出药效团特征的重叠体积作为构建机器学习模型的分子新特征。可以为特征分配权重以指示其重要性。在 DUD-E 数据集上进行验证，基于重叠体积表示的药效团特征的模型产生了显著的性能，中位数 AUC 约为 0.98，召回率接近 0.8。
