Deep learning‐based methods have been extensively developed to improve scoring performance in structure‐based drug discovery. Extending multitask deep networks in addressing pharmaceutical problems shows remarkable improvements over single task network. Recently, grid featurization has been introduced to convert protein–ligand complex co‐ordinates into fingerprints with the advantage of incorporating inter‐ and intra‐molecular information. The combination of grid featurization with multitask deep networks would hold great potential to boost the scoring performance. We examined the performance of three novel multitask deep networks (standard multitask, bypass, and progressive network) in reproducing the binding affinities of protein–ligand complexes in comparison with AutoDock Vina docking and MM/GBSA method. Among five evaluated methods, progressive network combined with grid featurization provided the best Pearson correlation coefficient (0.74) and least mean absolute average error (0.98) for the overall scoring performance. Moreover, all networks increased screening ability for the re‐docking pose and progressive network even achieved AUC of 0.87 over 0.52 of AutoDock Vina. Our results demonstrated that progressive network combined with grid featurization would be one powerful rescoring approach to strengthen screening results after obtaining protein–ligand complex in the conventional docking software.

中文翻译：

---

具有网格特征的多任务深度网络可提高蛋白质-配体结合的评分性能

已经广泛开发了基于深度学习的方法，以提高基于结构的药物发现中的评分性能。扩展多任务深度网络以解决制药问题显示出比单任务网络显着的改进。最近，网格融合已被引入，可以将蛋白质-配体复杂的坐标转换为指纹，并具有整合分子间和分子内信息的优势。网格功能化与多任务深度网络的结合将具有巨大的潜力来提高评分性能。与AutoDock Vina对接和MM / GBSA方法相比，我们研究了三种新颖的多任务深层网络（标准的多任务，旁路和渐进网络）在复制蛋白质-配体复合物的结合亲和力方面的性能。在五种评估方法中，渐进网络与网格特征化相结合，为整体评分性能提供了最佳的Pearson相关系数（0.74）和最小平均绝对平均误差（0.98）。此外，所有网络都增加了重新停靠姿势的筛选能力，而渐进式网络甚至达到了AutoDock Vina的0.52的AUC为0.87。我们的结果表明，在常规对接软件中获得蛋白质-配体复合物后，将渐进式网络与网格特征化相结合将是一种有效的筛选方法，可增强筛选结果。所有网络都增加了重新停靠姿势的筛选能力，而渐进式网络甚至达到了AutoDock Vina的0.52的AUC为0.87。我们的结果表明，在常规对接软件中获得蛋白质-配体复合物后，将渐进式网络与网格特征化相结合将是一种有效的筛选方法，可增强筛选结果。所有网络都增加了重新停靠姿势的筛选能力，而渐进式网络甚至达到了AutoDock Vina的0.52的AUC为0.87。我们的结果表明，在常规对接软件中获得蛋白质-配体复合物后，将渐进式网络与网格特征化相结合将是一种有效的筛选方法，可增强筛选结果。