Nonlinear Scoring Functions for Similarity-Based Ligand Docking and Binding Affinity Prediction

Protein–ligand docking is a computational method to identify the binding mode of a ligand and a target protein, and predict the corresponding binding affinity using a scoring function. This method has great value in drug design. After decades of development, scoring functions nowadays typically can identify the true binding mode, but the prediction of binding affinity still remains a major problem. Here we present CScore, a data-driven scoring function using a modified Cerebellar Model Articulation Controller (CMAC) learning architecture, for accurate binding affinity prediction. The performance of CScore in terms of correlation between predicted and experimental binding affinities is benchmarked under different validation approaches. CScore achieves a prediction with R = 0.7668 and RMSE = 1.4540 when tested on an independent dataset. To the best of our knowledge, this result outperforms other scoring functions tested on the same dataset. The performance of CScore varies on different clusters under the leave-cluster-out validation approach, but still achieves competitive result. Lastly, the target-specified CScore achieves an even better result with R = 0.8237 and RMSE = 1.0872, trained on a much smaller but more relevant dataset for each target. The large dataset of protein–ligand complexes structural information and advances of machine learning techniques enable the data-driven approach in binding affinity prediction. CScore is capable of accurate binding affinity prediction. It is also shown that CScore will perform better if sufficient and relevant data is presented. As there is growth of publicly available structural data, further improvement of this scoring scheme can be expected.

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Role of polarization in ligand docking and binding affinity prediction for inhibitors of dengue virus

Medicinal Chemistry Research ◽

10.1007/s00044-011-9617-1 ◽

2011 ◽

Vol 21 (7) ◽

pp. 1030-1038 ◽

Cited By ~ 3

Author(s):

Kshatresh Dutta Dubey ◽

Amit Kumar Chaubey ◽

Rajendra Prasad Ojha

Keyword(s):

Dengue Virus ◽

Binding Affinity ◽

Ligand Docking ◽

Binding Affinity Prediction ◽

Affinity Prediction

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Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening

Wiley Interdisciplinary Reviews Computational Molecular Science ◽

10.1002/wcms.1225 ◽

2015 ◽

Vol 5 (6) ◽

pp. 405-424 ◽

Cited By ~ 101

Author(s):

Qurrat Ul Ain ◽

Antoniya Aleksandrova ◽

Florian D. Roessler ◽

Pedro J. Ballester

Keyword(s):

Machine Learning ◽

Virtual Screening ◽

Binding Affinity ◽

Scoring Functions ◽

Binding Affinity Prediction ◽

Affinity Prediction

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The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction

Biomolecules ◽

10.3390/biom8010012 ◽

2018 ◽

Vol 8 (1) ◽

pp. 12 ◽

Cited By ~ 24

Author(s):

Hongjian Li ◽

Jiangjun Peng ◽

Yee Leung ◽

Kwong-Sak Leung ◽

Man-Hon Wong ◽

...

Keyword(s):

Machine Learning ◽

Protein Structure ◽

Binding Affinity ◽

Sequence Similarity ◽

Scoring Functions ◽

Binding Affinity Prediction ◽

Affinity Prediction ◽

The Impact

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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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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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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 ◽

Binding Affinity Prediction ◽

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.

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