The identification of the ligand binding sites (LBS) in proteins is of great significance for the elucidation of protein structure and function. Different methods have been published to predict protein–ligand binding sites efficiently. However, most of the current prediction methods only focus on the characteristics of proteins without considering the interactions between proteins and their different ligands, which often leads to frustrating results when the structure or function of the protein is complex or diverse. Therefore, an improved blind docking method with a machine learning-based scoring function is proposed in this paper for the LBS prediction. The blind docking method is used to search binding pockets and an artificial neural network is constructed to analyze binding features, which makes the proposed method possible to distinguish true binding sites from other possible pockets. Two cases of LBS prediction are presented to show the excellent performance of the proposed method. This paper aims to provide new ideas for the prediction of interactions between proteins and small molecules, which can further guide the research of structure-based drug discovery.

蛋白质中配体结合位点（LBS）的鉴定对于阐明蛋白质结构和功能具有重要意义。已经发表了不同的方法来有效地预测蛋白质-配体结合位点。然而，目前大多数预测方法只关注蛋白质的特性，而没有考虑蛋白质与其不同配体之间的相互作用，当蛋白质的结构或功能复杂或多样时，往往会导致令人沮丧的结果。因此，本文提出了一种改进的基于机器学习的评分函数的盲对接方法，用于LBS预测。采用盲对接方法搜索绑定口袋，构建人工神经网络分析绑定特征，这使得所提出的方法可以将真正的结合位点与其他可能的口袋区分开来。提出了两个 LBS 预测案例，以展示所提出方法的优异性能。本文旨在为蛋白质与小分子相互作用的预测提供新思路，进一步指导基于结构的药物发现研究。