Scoring functions are routinely deployed in structure-based drug design to quantify the potential for protein–ligand (PL) complex formation. Here, we present a new scoring function Bappl+ that is designed to predict the binding affinities of non-metallo and metallo PL complexes. Bappl+ outperforms other state-of-the-art scoring functions, achieving a high Pearson correlation coefficient of up to ~ 0.76 with low standard deviations. The biggest contributors to the increased performance are the use of a machine-learning model and the enlarged training dataset. We have also evaluated the performance of Bappl+ on target-specific proteins, which highlighted the limitations of our function and provides a way for further improvements. We believe that Bappl+ methodology could prove valuable in ranking candidate molecules against a target metallo or non-metallo protein by reliably predicting their binding affinities, thus helping in the drug discovery process.

评分函数通常用于基于结构的药物设计，以量化蛋白质配体 (PL) 复合物形成的潜力。在这里，我们提出了一个新的评分函数 Bappl+，旨在预测非金属和金属 PL 复合物的结合亲和力。Bappl+ 优于其他最先进的评分函数，在低标准偏差下实现了高达 ~ 0.76 的高 Pearson 相关系数。提高性能的最大贡献者是使用机器学习模型和扩大的训练数据集。我们还评估了 Bappl+ 在目标特异性蛋白质上的性能，这突出了我们功能的局限性，并提供了进一步改进的方法。