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

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.

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Deep Learning in Drug Design: Protein-Ligand Binding Affinity Prediction

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2020.3046945 ◽

2021 ◽

pp. 1-1

Author(s):

Mohammad Ali Rezaei ◽

Yanjun Li ◽

Dapeng Wu ◽

Xiaolin Li ◽

Chenglong Li

Keyword(s):

Deep Learning ◽

Drug Design ◽

Ligand Binding ◽

Binding Affinity ◽

Binding Affinity Prediction ◽

Affinity Prediction

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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 ◽

Binding Affinity Prediction ◽

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.

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

Bioinformatics ◽

10.1093/bioinformatics/bty374 ◽

2018 ◽

Vol 34 (21) ◽

pp. 3666-3674 ◽

Cited By ~ 62

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

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Rational Drug Design

International Journal of Computational Models and Algorithms in Medicine ◽

10.4018/ijcmam.2014010104 ◽

2014 ◽

Vol 4 (1) ◽

pp. 59-85 ◽

Cited By ~ 3

Author(s):

Khaled H. Barakat ◽

Michael Houghton ◽

D. Lorne Tyrrel ◽

Jack A. Tuszynski

Keyword(s):

Drug Design ◽

Rational Drug Design ◽

Dimensional Structure ◽

Computational Tools ◽

Structure Based Drug Design ◽

Practical Applications ◽

3 Dimensional ◽

Drug Candidates ◽

The Past ◽

Rational Drug

For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used.

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Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges

Journal of Computer-Aided Molecular Design ◽

10.1007/s10822-018-0146-6 ◽

2018 ◽

Vol 33 (1) ◽

pp. 71-82 ◽

Cited By ~ 35

Author(s):

Duc Duy Nguyen ◽

Zixuan Cang ◽

Kedi Wu ◽

Menglun Wang ◽

Yin Cao ◽

...

Keyword(s):

Deep Learning ◽

Binding Affinity ◽

Binding Affinity Prediction ◽

Affinity Prediction ◽

Grand Challenges

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A Deep-Learning Proteomic-Scale Approach for Drug Design

Pharmaceuticals ◽

10.3390/ph14121277 ◽

2021 ◽

Vol 14 (12) ◽

pp. 1277

Author(s):

Brennan Overhoff ◽

Zackary Falls ◽

William Mangione ◽

Ram Samudrala

Keyword(s):

Deep Learning ◽

Drug Design ◽

Protein Structures ◽

Holistic Approach ◽

Rational Drug Design ◽

P Value ◽

Experimental Drugs ◽

Rational Drug ◽

Drug Compound ◽

Novel Drug

Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug–proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded “objective” signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication.

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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 ◽

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. 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.

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Persistent spectral–based machine learning (PerSpect ML) for protein-ligand binding affinity prediction

Science Advances ◽

10.1126/sciadv.abc5329 ◽

2021 ◽

Vol 7 (19) ◽

pp. eabc5329

Author(s):

Zhenyu Meng ◽

Kelin Xia

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Ligand Binding ◽

Binding Affinity ◽

Molecular Descriptors ◽

Biological Data ◽

Learning Models ◽

Binding Affinity Prediction ◽

Affinity Prediction ◽

Spectral Models

Molecular descriptors are essential to not only quantitative structure-activity relationship (QSAR) models but also machine learning–based material, chemical, and biological data analysis. Here, we propose persistent spectral–based machine learning (PerSpect ML) models for drug design. Different from all previous spectral models, a filtration process is introduced to generate a sequence of spectral models at various different scales. PerSpect attributes are defined as the function of spectral variables over the filtration value. Molecular descriptors obtained from PerSpect attributes are combined with machine learning models for protein-ligand binding affinity prediction. Our results, for the three most commonly used databases including PDBbind-2007, PDBbind-2013, and PDBbind-2016, are better than all existing models, as far as we know. The proposed PerSpect theory provides a powerful feature engineering framework. PerSpect ML models demonstrate great potential to significantly improve the performance of learning models in molecular data analysis.

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Rational Drug Design Rational Drug Design

Pharmaceutical Sciences ◽

10.4018/978-1-5225-1762-7.ch044 ◽

2017 ◽

pp. 1144-1174

Author(s):

Khaled H. Barakat ◽

Michael Houghton ◽

D. Lorne Tyrrel ◽

Jack A. Tuszynski

Keyword(s):

Drug Design ◽

Rational Drug Design ◽

The Other ◽

Dimensional Structure ◽

Design Tools ◽

Structure Based Drug Design ◽

Practical Applications ◽

3 Dimensional ◽

Drug Candidates ◽

Rational Drug

For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used.

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