CBSF: A New Empirical Scoring Function for Docking Parameterized by Weights of Neural Network
Author:
Syrlybaeva Raulia R.1, Talipov Marat R.2
Affiliation:
1. Department of Chemistry and Biochemistry , New Mexico State University , Las Cruces, New Mexico 88003 , United States ; College of Pharmacy , University of Georgia , Athens , Georgia 30602 , United States 2. Department of Chemistry and Biochemistry , New Mexico State University , Las Cruces , New Mexico 88003 , United States
Abstract
Abstract
A new CBSF empirical scoring function for the estimation of binding energies between proteins and small molecules is proposed in this report. The final score is obtained as a sum of three energy terms calculated using descriptors based on a simple counting of the interacting protein-ligand atomic pairs. All the required weighting coefficients for this method were derived from a pretrained neural network. The proposed method demonstrates a high accuracy and reproduces binding energies of protein-ligand complexes from the CASF-2016 test set with a standard deviation of 2.063 kcal/mol (1.511 log units) and an average error of 1.682 kcal/mol (1.232 log units). Thus, CBSF has a significant potential for the development of rapid and accurate estimates of the protein-ligand interaction energies.
Publisher
Walter de Gruyter GmbH
Subject
Applied Mathematics,Computational Mathematics,Mathematical Physics,Molecular Biology,Biophysics
Reference42 articles.
1. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., . .. Zheng, X. (2016). TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (pp. 266–284). Savannah: Berkeley: USENIX Association. 2. Baek, M., Shin, W. H., Chung, H. W., & Seok, C. (2017). GalaxyDock BP2 score: a hybrid scoring function for accurate protein–ligand docking. Journal of Computer-Aided Molecular Design, 31(7), 653–666. https://doi.org/10.1007/s10822-017-0030-9 3. Ballester, P. J., & Mitchell, J. B. O. (2010). A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics, 26(9), 1169–1175. https://doi.org/10.1093/bioinformatics/btq112 4. Boyle, N. M. O., Banck, M., James, C. A., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011). Open Babel: An open chemical toolbox. Journal of Cheminformatics, 3(33), 1–14. https://doi.org/10.1186/1758-2946-3-33 5. Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241–1250. https://doi.org/10.1016/j.drudis.2018.01.039
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