Predicting protein-ligand binding affinities using novel geometrical descriptors and machine-learning methods.
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.
Deng
Wei
W
Departments of Chemistry and Decision Sciences and Engineering Systems, Rensselaer Polytechnic Institute, Troy, New York 12180, USA.
Breneman
Curt
C
Embrechts
Mark J
MJ
D000465:Algorithms; D001185:Artificial Intelligence; D030562:Databases, Protein; D008024:Ligands; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry*
D008024:Ligands; D011506:Proteins