A fully differentiable ligand pose optimization framework guided by deep learning and traditional scoring functions

1 code implementation • 27 Jun 2022 • Zechen Wang, Liangzhen Zheng, Sheng Wang, Mingzhi Lin, Zhihao Wang, Adams Wai-Kin Kong, Yuguang Mu, Yanjie Wei, Weifeng Li

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.

OnionNet-2: A Convolutional Neural Network Model for Predicting Protein-Ligand Binding Affinity based on Residue-Atom Contacting Shells

1 code implementation • 22 Mar 2021 • Zechen Wang, Liangzhen Zheng, Yang Liu, Yuanyuan Qu, Yong-Qiang Li, Mingwen Zhao, Yuguang Mu, Weifeng Li

In this study, we proposed a simple scoring function (called OnionNet-2) based on convolutional neural network to predict $\triangle$$G$.

Quantitative Interpretations of Energetic Features and Key Residues at SARS Coronavirus Spike Receptor-Binding Domain and ACE2 Receptor Interface

no code implementations • 11 Mar 2021 • Yanmei Yang, Yunju Zhang, Yuanyuan Qu, Xuewei Liu, Mingwen Zhao, Yuguang Mu, Weifeng Li

Energy decomposition analyses identified three binding patches in the SARS-CoV-2 RBD and eleven key residues (Phe486, Tyr505, Asn501, Tyr489, Gln493, Leu455 and etc) which are believed to be the main targets for drug development.

OnionNet: a multiple-layer inter-molecular contact based convolutional neural network for protein-ligand binding affinity prediction

2 code implementations • 6 Jun 2019 • Liangzhen Zheng, Jingrong Fan, Yuguang Mu

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.

Biological Physics Computational Physics Biomolecules