LCP: Simple Representation of Docking Poses for Machine Learning: A Case Study on Xanthine Oxidase Inhibitors
| Autor: | Yoshitaka Asanuma, Kentaro Kawai, Kazuyuki Sato, Masaaki Omote, Toshiki Kato, Yukiko Karuo, Atsushi Tarui |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Xanthine Oxidase
business.industry Computer science Organic Chemistry Ligands Ligand (biochemistry) Machine learning computer.software_genre Computer Science Applications Random forest Machine Learning Molecular Docking Simulation Support vector machine Improved performance Docking (dog) Structural Biology Simple (abstract algebra) Drug Discovery Molecular Medicine Artificial intelligence Enzyme Inhibitors business Representation (mathematics) computer Protein Binding |
| Zdroj: | Molecular Informatics. 41:2100245 |
| ISSN: | 1868-1751 1868-1743 |
| Popis: | In this paper, we propose a simple descriptor called the ligand coordinate profile (LCP) for describing docking poses. The LCP descriptor is generated from the coordinates of the polar hydrogen and heavy atoms of the docked ligand. We hypothesize that the prediction of binding poses can be enhanced through the combination of machine learning methods with the LCP descriptor. Two docking programs were used to predict ligand docking against xanthine oxidase. Four machine learning methods-k-nearest neighbors, random forest, support vector machine, and LightGBM-were used to determine whether machine learning-based models could be used to accurately identify the correct binding poses. Regardless of the machine learning method employed, the LCP descriptor demonstrated improved performance compared to the existing descriptor. The results of the leave-one-pdb-out approach revealed that the influence of the pose descriptor was also significant, as demonstrated through cross-validation. When evaluated using top-N metrics, the machine learning models were generally more effective than the docking programs. In addition, the LCP-based models outperformed those based on the existing descriptor. The results obtained in this study suggest that our proposed binding pose descriptor is effective for improving the docking accuracy of xanthine oxidase inhibitors. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text