Table 3_Quality over quantity: how to get the best results when using docking for repurposing.docx

Molecular docking is among the fastest and most readily available computational tools to explore protein–ligand interactions. However, little effort has been put into assessing the quality of its results. In this paper, we compared eight free license docking programs to screen a drug library against the human target, phosphodiesterase 5A (PDE5A), to evaluate their ability to find its known ligand, sildenafil, and other ligands that became erectile dysfunction drugs because they inhibit this target. GNINA was superior at identifying the known target because it offers a convolutional neural network (CNN) score that ranks the quality of docking results. Using this CNN score improved the ranking of known positives. Receiver operating characteristic (ROC) analysis revealed that all docking suites lack specificity; that is, they often misidentify true negatives. Employing a CNN score cutoff before ranking by docking affinity raised specificity with a small loss in sensitivity. After the cutoff, datasets became smaller but of higher quality. We propose a heuristic to produce relevant docking results, which includes an overall evaluation of the target on docking performance through ROC and an improvement of candidate binder selection using a CNN score cutoff of 0.9.

分子对接（Molecular docking）是探索蛋白质-配体相互作用的最快速、最易获取的计算工具之一。然而，目前针对其结果质量的评估工作尚显不足。本文中，我们以人类靶标磷酸二酯酶5A（phosphodiesterase 5A, PDE5A）为研究对象，通过筛选药物库对8款免费授权的分子对接程序进行对比，以评估它们识别该靶标已知配体西地那非，以及其他因抑制该靶标而被开发为勃起功能障碍治疗药物的配体的能力。GNINA在识别已知靶标方面表现更优，这得益于其提供的卷积神经网络（convolutional neural network, CNN）打分函数，可对对接结果的质量进行排序。使用该CNN打分函数能够提升已知阳性样本的排序优先级。受试者工作特征（Receiver Operating Characteristic, ROC）分析表明，所有分子对接套件均存在特异性不足的问题，即它们常将真阴性样本误判为阳性样本。在基于对接亲和力进行排序前，先应用CNN打分阈值可在小幅牺牲灵敏度的前提下提升特异性。经过该阈值筛选后，数据集规模虽有所缩小，但质量显著提升。本文提出了一种可生成有效对接结果的启发式方法，该方法包括通过ROC曲线对靶标的对接性能进行整体评估，以及通过设置0.9的CNN打分阈值优化候选结合物的筛选流程。