  The machine learning (ML) and deep learning (DL) techniques are widely
recognized to be powerful tools for virtual drug screening. The recently
reported ML- or DL-based scoring functions have shown exciting performance in
predicting protein-ligand binding affinities with fruitful application
prospects. However, the differentiation between highly similar ligand
conformations, including the native binding pose (the global energy minimum
state), remains challenging which could greatly enhance the docking. In this
work, we propose a fully differentiable framework for ligand pose optimization
based on a hybrid scoring function (SF) combined with a multi-layer perceptron
(DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which
combines (1) the root mean square deviation (RMSD) of the docking pose with
respect to the native pose and (2) the AutoDock Vina score, is fully
differentiable thus is capable of optimizing the ligand binding pose to the
energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset,
the DeepRMSD+Vina reaches a success rate of 95.4%, which is by far the best
reported SF to date. Based on this SF, an end-to-end ligand pose optimization
framework was implemented to improve the docking pose quality. We demonstrated
that this method significantly improves the docking success rate (by 15%) in
redocking and crossdocking tasks, revealing the high potentialities of this
framework in drug design and discovery.
