Article-Journal

Integrates algebraic topology and deep learning (MathDL) to predict binding affinities and rank 137 SARS-CoV-2 main protease (Mpro) inhibitor structures, revealing key binding sites and interactions.

The AGL-Score models proposed in this study utilize algebraic graph learning to encode molecular information, significantly outperforming existing scoring functions in protein-ligand binding tasks.

Proposes a differential geometry-based geometric learning (DG-GL) strategy to encode molecular structures into low-dimensional manifolds for accurate prediction of drug discovery-related properties.

Proposes a Generative Network Complex (GNC) platform that combines deep learning models to design novel compounds, predict their properties, and evaluate druggability.

Integrates advanced mathematics and deep learning to construct models that achieved top rankings in pose prediction and binding affinity estimation in D3R Grand Challenges.

This research demonstrates that protein rigidity strengthening is a key mechanism in protein-ligand binding and utilizes this insight to improve binding affinity predictions.

This study investigates the grid dependence of the MIBPB solver, determining that a 0.6 Å grid spacing ensures accurate and reliable calculations for electrostatic solvation and binding free energies.

This paper presents the Feature Functional Theory–Binding Predictor (FFT–BP), which combines microscopic feature vectors with machine learning to accurately predict protein-ligand binding affinities.

This work explores the impact of geometric features and Lennard–Jones potential on solvation free energy, constructing robust nonpolar solvation models that incorporate surface curvature.

This work introduces generalized flexibility-rigidity index (gFRI) methods that utilize new rigidity and flexibility formulations to significantly outperform classic models in protein B-factor prediction.