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Updated: Aug 25, 2026

Protein Target Prediction and Validation of Small Molecule Compound
Published on: February 23, 2024
Predicting protein-ligand binding affinities using novel geometrical descriptors and machine-learning methods
Wei Deng1, Curt Breneman, Mark J Embrechts
1Departments 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.
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