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What is Persistent Local Laplacian Prediction of Protein-Ligand Binding Affinities about?

This document presents a novel approach using persistent local Laplacian (PLL) theory to predict protein-ligand binding affinities, addressing limitations of traditional methods by capturing local geometric and topological features. The proposed models, evaluated on three benchmark datasets, demonstrate superior predictive performance compared to existing methods, achieving high Pearson correlation coefficients and low root-mean-square errors. The integration of PLL with advanced machine learning algorithms

Author

nguyenchinhmmo

Language

EN