Gaili Li

3D Geometric Equivariant Graph Neural Networks with Contrastive Learning for Protein-Ligand Binding Affinity Prediction.

3D-GEGCL is a framework that integrates 3D geometrically equivariant graph neural networks with self-supervised contrastive learning for robust and accurate PLBA prediction and achieves superior performance over existing leading methods across a range of assessment criteria.

A geometric rotation-equivariant spherical convolutional and gaussian radial basis network for predicting protein-ligand binding affinity