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

Binding Affinity ◽

Structural Information ◽

Complex Structure ◽

Rational Drug Design ◽

Binding Affinities ◽

Ligand Complex ◽

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.

AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks

International Journal of Molecular Sciences ◽

10.3390/ijms21228424 ◽

2020 ◽

Vol 21 (22) ◽

pp. 8424

Author(s):

Yongbeom Kwon ◽

Woong-Hee Shin ◽

Junsu Ko ◽

Juyong Lee

Keyword(s):

Neural Network ◽

Binding Affinity ◽

Pearson Correlation ◽

Complex Structure ◽

Rational Drug Design ◽

Scoring Functions ◽

Binding Affinities ◽

Ligand Complex ◽

Affinity Prediction

Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to predict affinity. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3-D convolutional neural network layers. Our model was trained using the 3772 protein-ligand complexes from the refined set of the PDBbind-2016 database and tested using the core set of 285 complexes. The benchmark results show that the Pearson correlation coefficient between the predicted binding affinities by our model and the experimental data is 0.827, which is higher than the state-of-the-art binding affinity prediction scoring functions. Additionally, our method ranks the relative binding affinities of possible multiple binders of a protein quite accurately, comparable to the other scoring functions. Last, we measured which structural information is critical for predicting binding affinity and found that the complementarity between the protein and ligand is most important.

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

Scientific Reports ◽

10.1038/s41598-021-83679-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jooyong Shim ◽

Zhen-Yu Hong ◽

Insuk Sohn ◽

Changha Hwang

Keyword(s):

Neural Network ◽

Binding Affinity ◽

Drug Target ◽

Binary Classification ◽

Binding Affinities ◽

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.

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

ACS Omega ◽

10.1021/acsomega.9b01997 ◽

2019 ◽

Vol 4 (14) ◽

pp. 15956-15965 ◽

Cited By ~ 12

Author(s):

Liangzhen Zheng ◽

Jingrong Fan ◽

Yuguang Mu

Keyword(s):

Neural Network ◽

Ligand Binding ◽

Binding Affinity ◽

Intermolecular Contact ◽

Affinity Prediction ◽

Multiple Layer

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

Chemical Science ◽

10.1039/d1sc00163a ◽

2021 ◽

Author(s):

Harrison Green ◽

David Ryan Koes ◽

Jacob D Durrant

Keyword(s):

Neural Network ◽

Machine Learning ◽

Drug Discovery ◽

Virtual Screening ◽

Binding Affinity ◽

Lead Optimization ◽

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...

DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction

Scientific Reports ◽

10.1038/s41598-018-37214-1 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 11

Author(s):

Zhonghao Liu ◽

Yuxin Cui ◽

Zheng Xiong ◽

Alierza Nasiri ◽

Ansi Zhang ◽

...

Keyword(s):

Neural Network ◽

Binding Affinity ◽

Network Model ◽

Neural Network Model ◽

Peptide Binding ◽

Specific Class ◽

Affinity Prediction ◽

Peptide Binding Affinity

Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

10.26434/chemrxiv.9866912 ◽

2019 ◽

Author(s):

Mohammad Rezaei ◽

Yanjun Li ◽

Xiaolin Li ◽

Chenglong Li

Keyword(s):

Deep Learning ◽

Drug Design ◽

Binding Affinity ◽

Benchmark Dataset ◽

Rational Drug Design ◽

Learning Models ◽

Structure Based Drug Design ◽

Affinity Prediction ◽

Rational Drug

Introduction: The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results. Objectives: The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters. Methods: The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models. Results: The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model. Conclusions: The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

A Cascade Graph Convolutional Network for Predicting Protein–Ligand Binding Affinity

International Journal of Molecular Sciences ◽

10.3390/ijms22084023 ◽

2021 ◽

Vol 22 (8) ◽

pp. 4023

Author(s):

Huimin Shen ◽

Youzhi Zhang ◽

Chunhou Zheng ◽

Bing Wang ◽

Peng Chen

Keyword(s):

Neural Network ◽

Ligand Binding ◽

Binding Affinity ◽

Network Architecture ◽

Original Data ◽

Ligand Complex ◽

Convolutional Network ◽

Data Set ◽

Irregular Data

Accurate prediction of binding affinity between protein and ligand is a very important step in the field of drug discovery. Although there are many methods based on different assumptions and rules do exist, prediction performance of protein–ligand binding affinity is not satisfactory so far. This paper proposes a new cascade graph-based convolutional neural network architecture by dealing with non-Euclidean irregular data. We represent the molecule as a graph, and use a simple linear transformation to deal with the sparsity problem of the one-hot encoding of original data. The first stage adopts ARMA graph convolutional neural network to learn the characteristics of atomic space in the protein–ligand complex. In the second stage, one variant of the MPNN graph convolutional neural network is introduced with chemical bond information and interactive atomic features. Finally, the architecture passes through the global add pool and the fully connected layer, and outputs a constant value as the predicted binding affinity. Experiments on the PDBbind v2016 data set showed that our method is better than most of the current methods. Our method is also comparable to the state-of-the-art method on the data set, and is more intuitive and simple.

Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions

BMC Bioinformatics ◽

10.1186/s12859-021-04466-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Sangmin Seo ◽

Jonghwan Choi ◽

Sanghyun Park ◽

Jaegyoon Ahn

Keyword(s):

Deep Learning ◽

Binding Affinity ◽

Prediction Models ◽

Attention Mechanism ◽

Scoring Functions ◽

Ligand Complex ◽

Structure Based Drug Design ◽

Affinity Prediction ◽

Proposed Model

Abstract Background Accurate prediction of protein–ligand binding affinity is important for lowering the overall cost of drug discovery in structure-based drug design. For accurate predictions, many classical scoring functions and machine learning-based methods have been developed. However, these techniques tend to have limitations, mainly resulting from a lack of sufficient energy terms to describe the complex interactions between proteins and ligands. Recent deep-learning techniques can potentially solve this problem. However, the search for more efficient and appropriate deep-learning architectures and methods to represent protein–ligand complex is ongoing. Results In this study, we proposed a deep-neural network model to improve the prediction accuracy of protein–ligand complex binding affinity. The proposed model has two important features, descriptor embeddings with information on the local structures of a protein–ligand complex and an attention mechanism to highlight important descriptors for binding affinity prediction. The proposed model performed better than existing binding affinity prediction models on most benchmark datasets. Conclusions We confirmed that an attention mechanism can capture the binding sites in a protein–ligand complex to improve prediction performance. Our code is available at https://github.com/Blue1993/BAPA.

Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions

10.1101/2021.03.18.436020 ◽

2021 ◽

Author(s):

Sangmin Seo ◽

Jonghwan Choi ◽

Sanghyun Park ◽

Jaegyoon Ahn

Keyword(s):

Deep Learning ◽

Binding Affinity ◽

Attention Mechanism ◽

Accurate Prediction ◽

Scoring Functions ◽

Ligand Complex ◽

Affinity Prediction ◽

Local Structures ◽

Proposed Model

AbstractAccurate prediction of protein-ligand binding affinity is important in that it can lower the overall cost of drug discovery in structure-based drug design. For more accurate prediction, many classical scoring functions and machine learning-based methods have been developed. However, these techniques tend to have limitations, mainly resulting from a lack of sufficient interactions energy terms to describe complex interactions between proteins and ligands. Recent deep-learning techniques show strong potential to solve this problem, but the search for more efficient and appropriate deep-learning architectures and methods to represent protein-ligand complexes continues. In this study, we proposed a deep-neural network for more accurate prediction of protein-ligand complex binding affinity. The proposed model has two important features, descriptor embeddings that contains embedded information about the local structures of a protein-ligand complex and an attention mechanism for highlighting important descriptors to binding affinity prediction. The proposed model showed better performance on most benchmark datasets than existing binding affinity prediction models. Moreover, we confirmed that an attention mechanism was able to capture binding sites in a protein-ligand complex and that it contributed to improvement in predictive performance. Our code is available at https://github.com/Blue1993/BAPA.Author summaryThe initial step in drug discovery is to identify drug candidates for a target protein using a scoring function. Existing scoring functions, however, lack the ability to accurately predict the binding affinity of protein-ligand complexes. In this study, we proposed a deep learning-based approach to extract patterns from the local structures of protein-ligand complexes and to highlight the important local structures via an attention mechanism. The proposed model showed good performance for various benchmark datasets compared to existing models.