Improving the binding affinity estimations of protein-ligand complexes using machine-learning facilitated force field method
| Autor: | Anjali Soni, Bhyravabhotla Jayaram, Ruchika Bhat |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
Complex formation Computational biology Ligands 01 natural sciences Force field (chemistry) Machine Learning symbols.namesake 0103 physical sciences Drug Discovery Physical and Theoretical Chemistry Databases Protein Binding affinities 010304 chemical physics Drug discovery Computational Biology Proteins Pearson product-moment correlation coefficient 0104 chemical sciences Computer Science Applications 010404 medicinal & biomolecular chemistry Metals Drug Design symbols Quantum Theory Protein ligand Protein Binding |
| Zdroj: | Journal of computer-aided molecular design. 34(8) |
| ISSN: | 1573-4951 |
| Popis: | Scoring functions are routinely deployed in structure-based drug design to quantify the potential for protein–ligand (PL) complex formation. Here, we present a new scoring function Bappl+ that is designed to predict the binding affinities of non-metallo and metallo PL complexes. Bappl+ outperforms other state-of-the-art scoring functions, achieving a high Pearson correlation coefficient of up to ~ 0.76 with low standard deviations. The biggest contributors to the increased performance are the use of a machine-learning model and the enlarged training dataset. We have also evaluated the performance of Bappl+ on target-specific proteins, which highlighted the limitations of our function and provides a way for further improvements. We believe that Bappl+ methodology could prove valuable in ranking candidate molecules against a target metallo or non-metallo protein by reliably predicting their binding affinities, thus helping in the drug discovery process. |
| Databáze: | OpenAIRE |
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