Machine-Learning

MMGNN: Multi-level, multi-color graph neural networks for molecular property prediction

Introduces MMGNN, a multi-level multi-color graph neural network framework for molecular property prediction.

Trung Nguyen Duc Nguyen

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.

Masud Rana Duc Nguyen

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.

Masud Rana Duc Nguyen

Multiscale laplacian learning

Proposes a Multiscale Laplacian Learning framework to improve machine learning performance on small or diverse datasets with limited labeled samples.

Ekaterina Merkurjev Duc Nguyen Guo-Wei Wei

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.

Masud Rana Duc Nguyen

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.

Kaifu Gao Duc Nguyen Meihua Tu Guo-Wei Wei