Persistent local Laplacian prediction of protein-ligand binding affinities

TL;DR

This paper introduces the persistent local Laplacian (PLL), a novel mathematical framework for creating molecular descriptors that improve the accuracy of protein-ligand binding affinity predictions in drug discovery.

Contribution

The work develops the PLL framework to better capture local structural features, addressing limitations of existing topological methods, and integrates it with machine learning for enhanced predictive performance.

Findings

PLL-based descriptors outperform existing methods in benchmarks

Models show strong predictive accuracy for binding affinities

Framework effectively captures local geometric and topological features

Abstract

Accurate prediction of protein-ligand binding affinity remains a central challenge in structure-based drug discovery. The effectiveness of machine learning models critically depends on the quality of molecular descriptors, for which advanced mathematical frameworks provide powerful tools. In this work, we employ a novel mathematical theory, termed the persistent local Laplacian (PLL), to construct molecular descriptors that capture localized geometric and topological features of biomolecular structures. The PLL framework addresses key limitations of traditional topological data analysis methods, such as persistent homology and the persistent Laplacian, which are often insensitive to local structural variations, while maintaining high computational efficiency. The resulting molecular descriptors are integrated with advanced machine learning algorithms to develop accurate predictive models for protein-ligand binding affinity. The proposed models are systematically evaluated on three well-established benchmark datasets, demonstrating consistently strong and competitive predictive performance. Computational results show that the PLL-based models outperform existing approaches, highlighting their potential as a powerful tool for drug discovery, protein engineering, and broader applications in science and engineering.