scholarly journals OnionNet: a Multiple-Layer Intermolecular-Contact-Based Convolutional Neural Network for Protein–Ligand Binding Affinity Prediction

ACS Omega ◽  
2019 ◽  
Vol 4 (14) ◽  
pp. 15956-15965 ◽  
Author(s):  
Liangzhen Zheng ◽  
Jingrong Fan ◽  
Yuguang Mu
Keyword(s):  
Neural Network ◽  
Ligand Binding ◽  
Convolutional Neural Network ◽  
Binding Affinity ◽  
Intermolecular Contact ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Multiple Layer

scholarly journals DeepFrag: A Deep Convolutional Neural Network for Fragment-based Lead Optimization

2021 ◽  
Author(s):  
Harrison Green ◽  
David Ryan Koes ◽  
Jacob D Durrant
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Convolutional Neural Network ◽  
Binding Affinity ◽  
Lead Optimization ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Computer Aided

Machine learning has been increasingly applied to the field of computer-aided drug discovery in recent years, leading to notable advances in binding-affinity prediction, virtual screening, and QSAR. Surprisingly, it is...

scholarly journals AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using the Ensemble of 3D-Convolutional Neural Network

2020 ◽  
Author(s):  
Yongbeom Kwon ◽  
Woong-Hee Shin ◽  
Junsu Ko ◽  
Juyong Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Binding Affinity ◽  
Structural Information ◽  
Complex Structure ◽  
Rational Drug Design ◽  
Binding Affinities ◽  
Ligand Complex ◽  
Binding Affinity Prediction ◽  
Affinity Prediction

Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our new model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3D convolutional neural network layers. Our model was trained using the 3740 protein-ligand complexes from the refined set of the PDBbind database and tested using the 270 complexes from the core set. The benchmark results show that the correlation coefficient between the predicted binding affinities by our model and the experimental data is higher than 0.72, which is comparable with the state-of-the-art binding affinity prediction methods. In addition, our method also ranks the relative binding affinities of possible multiple binders of a protein quite accurately. Last, we measured which structural information is critical for predicting binding affinity.

scholarly journals Prediction of drug–target binding affinity using similarity-based convolutional neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jooyong Shim ◽  
Zhen-Yu Hong ◽  
Insuk Sohn ◽  
Changha Hwang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Binding Affinity ◽  
Drug Target ◽  
Binary Classification ◽  
Binding Affinities ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Target Binding ◽  
Public Datasets

AbstractIdentifying novel drug–target interactions (DTIs) plays an important role in drug discovery. Most of the computational methods developed for predicting DTIs use binary classification, whose goal is to determine whether or not a drug–target (DT) pair interacts. However, it is more meaningful but also more challenging to predict the binding affinity that describes the strength of the interaction between a DT pair. If the binding affinity is not sufficiently large, such drug may not be useful. Therefore, the methods for predicting DT binding affinities are very valuable. The increase in novel public affinity data available in the DT-related databases enables advanced deep learning techniques to be used to predict binding affinities. In this paper, we propose a similarity-based model that applies 2-dimensional (2D) convolutional neural network (CNN) to the outer products between column vectors of two similarity matrices for the drugs and targets to predict DT binding affinities. To our best knowledge, this is the first application of 2D CNN in similarity-based DT binding affinity prediction. The validation results on multiple public datasets show that the proposed model is an effective approach for DT binding affinity prediction and can be quite helpful in drug development process.

scholarly journals AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using the Ensemble of 3D-Convolutional Neural Network

2020 ◽  
Author(s):  
Yongbeom Kwon ◽  
Woong-Hee Shin ◽  
Junsu Ko ◽  
Juyong Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Binding Affinity ◽  
Structural Information ◽  
Complex Structure ◽  
Rational Drug Design ◽  
Binding Affinities ◽  
Ligand Complex ◽  
Binding Affinity Prediction ◽  
Affinity Prediction

Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our new model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3D convolutional neural network layers. Our model was trained using the 3740 protein-ligand complexes from the refined set of the PDBbind database and tested using the 270 complexes from the core set. The benchmark results show that the correlation coefficient between the predicted binding affinities by our model and the experimental data is higher than 0.72, which is comparable with the state-of-the-art binding affinity prediction methods. In addition, our method also ranks the relative binding affinities of possible multiple binders of a protein quite accurately. Last, we measured which structural information is critical for predicting binding affinity.

scholarly journals Development and evaluation of a deep learning model for protein–ligand binding affinity prediction

2018 ◽  
Vol 34 (21) ◽  
pp. 3666-3674 ◽  
Author(s):  
Marta M Stepniewska-Dziubinska ◽  
Piotr Zielenkiewicz ◽  
Pawel Siedlecki
Keyword(s):  
Deep Learning ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Learning Model ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Deep Learning Model

scholarly journals Assessment of programs for ligand binding affinity prediction

2008 ◽  
Vol 29 (8) ◽  
pp. 1316-1331 ◽  
Author(s):  
Ryangguk Kim ◽  
Jeffrey Skolnick
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Binding Affinity Prediction ◽  
Affinity Prediction

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

2021 ◽  
Vol 9 ◽  
Author(s):  
Zechen Wang ◽  
Liangzhen Zheng ◽  
Yang Liu ◽  
Yuanyuan Qu ◽  
Yong-Qiang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Ligand Binding ◽  
Convolutional Neural Network ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Computational Cost ◽  
Scoring Function ◽  
Quality Data ◽  
Great Success ◽  
Data Set

One key task in virtual screening is to accurately predict the binding affinity (△G) of protein-ligand complexes. Recently, deep learning (DL) has significantly increased the predicting accuracy of scoring functions due to the extraordinary ability of DL to extract useful features from raw data. Nevertheless, more efforts still need to be paid in many aspects, for the aim of increasing prediction accuracy and decreasing computational cost. In this study, we proposed a simple scoring function (called OnionNet-2) based on convolutional neural network to predict △G. The protein-ligand interactions are characterized by the number of contacts between protein residues and ligand atoms in multiple distance shells. Compared to published models, the efficacy of OnionNet-2 is demonstrated to be the best for two widely used datasets CASF-2016 and CASF-2013 benchmarks. The OnionNet-2 model was further verified by non-experimental decoy structures from docking program and the CSAR NRC-HiQ data set (a high-quality data set provided by CSAR), which showed great success. Thus, our study provides a simple but efficient scoring function for predicting protein-ligand binding free energy.

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