Machine-Learning

Geometric Graph Learning to Predict Changes in Binding Free Energy and Protein Thermodynamic Stability upon Mutation
This study introduces GGL-PPI, a geometric graph learning approach that accurately predicts mutation-induced binding free energy changes and protein stability using atom-level structural features.
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Geometric graph learning with extended atom-types features for protein-ligand binding affinity prediction
Introduces sybylGGL-Score and ecifGGL-Score, graph-based learning models integrating extensive atom types for state-of-the-art protein-ligand binding affinity prediction.
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Multiscale laplacian learning
Proposes a Multiscale Laplacian Learning framework to improve machine learning performance on small or diverse datasets with limited labeled samples.
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EISA-Score: Element Interactive Surface Area Score for Protein–Ligand Binding Affinity Prediction
This paper introduces EISA-Score, a novel scoring function that uses element interactive surface area representations to significantly improve protein-ligand binding affinity predictions.
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Generative Network Complex for the Automated Generation of Drug-like Molecules
This work develops a generative network complex (GNC) that optimizes multiple chemical properties in a latent space to automatically generate novel, drug-like molecules.
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