  Computational drug discovery provides an efficient tool helping large scale
lead molecules screening. One of the major tasks of lead discovery is
identifying molecules with promising binding affinities towards a target, a
protein in general. The accuracies of current scoring functions which are used
to predict the binding affinity are not satisfactory enough. Thus, machine
learning (ML) or deep learning (DL) based methods have been developed recently
to improve the scoring functions. In this study, a deep convolutional neural
network (CNN) model (called OnionNet) is introduced and the features are based
on rotation-free element-pair specific contacts between ligands and protein
atoms, and the contacts were further grouped in different distance ranges to
cover both the local and non-local interaction information between the ligand
and the protein. The prediction power of the model is evaluated and compared
with other scoring functions using the comparative assessment of scoring
functions (CASF-2013) benchmark and the v2016 core set of PDBbind database.
When compared to a previous CNN-based scoring function, our model shows
improvements of 0.08 and 0.16 in the correlations (R) and standard deviations
(SD) of regression, respectively, between the predicted binding affinities and
the experimental measured binding affinities. The robustness of the model is
further explored by predicting the binding affinities of the complexes
generated from docking simulations instead of experimentally determined PDB
structures.
