Predicting protein-ligand binding affinities using novel geometrical descriptors and machine-learning methods |
| |
Authors: | Deng Wei Breneman Curt Embrechts Mark J |
| |
Affiliation: | Departments of Chemistry and Decision Sciences and Engineering Systems, Rensselaer Polytechnic Institute, Troy, New York 12180, USA. |
| |
Abstract: | Inspired by the concept of knowledge-based scoring functions, a new quantitative structure-activity relationship (QSAR) approach is introduced for scoring protein-ligand interactions. This approach considers that the strength of ligand binding is correlated with the nature of specific ligand/binding site atom pairs in a distance-dependent manner. In this technique, atom pair occurrence and distance-dependent atom pair features are used to generate an interaction score. Scoring and pattern recognition results obtained using Kernel PLS (partial least squares) modeling and a genetic algorithm-based feature selection method are discussed. |
| |
Keywords: | |
本文献已被 PubMed 等数据库收录! |
|