Article-Journal

This research investigates how auxiliary information from suboptimal RNA formations can improve secondary structure prediction accuracy using novel topological features.

Introduces AGL-EAT-Score, an algebraic graph-based scoring function for ligand–receptor binding affinity prediction; shows strong benchmark performance.

This paper proposes the Neumann-Cayley orthogonal GRU (NC-GRU), which utilizes orthogonal matrices to prevent exploding gradients and enhance long-term memory in recurrent neural networks.

This study combines computational docking and experimental screening to identify and validate novel TrkB binders as potential therapeutic agents for neurodegenerative diseases.

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.

Introduces sybylGGL-Score and ecifGGL-Score, graph-based learning models integrating extensive atom types for state-of-the-art protein-ligand binding affinity prediction.

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

This study proposes a new molecular fingerprinting method using Neumann-Cayley Gated Recurrent Units (NC-GRU) within an autoencoder to enhance molecular property 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.

Introduces AweGNN, a graph neural network with auto-parametrized weighted element-specific features for molecular property prediction.