Shirui Pan

PSICHIC+ is a bioactivity foundation model that predicts binding affinities from sequence data, improving hit identification and hit-to-lead optimization using heterogeneous bioactivity data.

ERFMTDA predicts tsRNA-disease associations by modeling complex feature interactions and using a biologically informed negative sampling strategy, outperforming existing methods.

MolX is a geometric foundation model that jointly learns representations of protein pockets and ligands from 3D structural data, achieving state-of-the-art performance in predicting molecular interactions.

The paper introduces Allocator, a deep learning framework for predicting mRNA subcellular localization by integrating sequence and structure information, showing improved performance over existing methods.

PSICHIC predicts protein-ligand interactions from sequence data using a physicochemical graph neural network, achieving state-of-the-art accuracy and interpretability in binding affinity and functional effect prediction.

The paper introduces NCPNet, a graph neural network for inferring neuron-level brain connections using limited training data, achieving high accuracy on Drosophila and C. elegans connectomes.