OnionNet: a Multiple-Layer Intermolecular-Contact-Based
Convolutional Neural Network for Protein–Ligand Binding Affinity
Prediction
Posted on 2019-09-16 - 08:29
Computational drug
discovery provides an efficient tool for helping
large-scale lead molecule screening. One of the major tasks of lead
discovery is identifying molecules with promising binding affinities
toward a target, a protein in general. The accuracies of current scoring
functions that are used to predict the binding affinity are not satisfactory
enough. Thus, machine learning or deep learning based methods have
been developed recently to improve the scoring functions. In this
study, a deep convolutional neural network model (called OnionNet)
is introduced; its features are based on rotation-free element-pair-specific
contacts between ligands and protein atoms, and the contacts are further
grouped into different distance ranges to cover both the local and
nonlocal 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 the PDBbind database.
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.
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Zheng, Liangzhen; Fan, Jingrong; Mu, Yuguang (2019). OnionNet: a Multiple-Layer Intermolecular-Contact-Based
Convolutional Neural Network for Protein–Ligand Binding Affinity
Prediction. ACS Publications. Collection. https://doi.org/10.1021/acsomega.9b01997
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AUTHORS (3)
LZ
Liangzhen Zheng
JF
Jingrong Fan
YM
Yuguang Mu