Multitask deep networks with grid featurization achieve improved scoring performance for protein-ligand binding.

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.