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

2021 ◽  
Author(s):  
Harrison Green ◽  
David R Koes ◽  
Jacob D Durrant
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Binding Affinity ◽  
Deep Convolutional Neural Network ◽  
Lead Optimization ◽  
Ligand Complex ◽  
Receptor Ligand ◽  
Computer Aided ◽  
Version 2.0

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 less often applied to lead optimization, the process of identifying chemical fragments that might be added to a known ligand to improve its binding affinity. We here describe a deep convolutional neural network that predicts appropriate fragments given the structure of a receptor/ligand complex. In an independent benchmark of known ligands with missing (deleted) fragments, our DeepFrag model selected the known (correct) fragment from a set over 6,500 about 58% of the time. Even when the known/correct fragment was not selected, the top fragment was often chemically similar and may well represent a valid substitution. We release our trained DeepFrag model and associated software under the terms of the Apache License, Version 2.0. A copy can be obtained free of charge from http://durrantlab.com/deepfragmodel.

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 ISLAND: in-silico proteins binding affinity prediction using sequence information

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Wajid Arshad Abbasi ◽  
Adiba Yaseen ◽  
Fahad Ul Hassan ◽  
Saiqa Andleeb ◽  
Fayyaz Ul Amir Afsar Minhas
Keyword(s):  
Machine Learning ◽  
Protein Binding ◽  
Binding Affinity ◽  
State Of The Art ◽  
Protein Complexes ◽  
Protein Structures ◽  
Sequence Information ◽  
Binding Affinity Prediction ◽  
Generalization Performance ◽  
Affinity Prediction

Abstract Background Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore sequence-based protein binding affinity prediction using machine learning. Method We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the protein binding affinity. Results We present our findings that the true generalization performance of even the state-of-the-art sequence-only predictor is far from satisfactory and that the development of machine learning methods for binding affinity prediction with improved generalization performance is still an open problem. We have also proposed a sequence-based novel protein binding affinity predictor called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its python code are available at https://sites.google.com/view/wajidarshad/software. Conclusion This paper highlights the fact that the true generalization performance of even the state-of-the-art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem.

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