Zixu Ran

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.

DOMINO identifies spatial domains in spatial transcriptomic data with improved accuracy and scalability, outperforming existing methods on large datasets.

OmniCleave models protease-substrate specificity using a geometric graph framework, outperforming existing methods and identifying novel substrates.

GRASP uses a graph neural network to predict RNA subcellular localization by modeling RNA structure, outperforming existing methods across RNA types and transcript lengths.

This paper introduces Kinhibit, a framework that uses pre-trained molecular encoders and protein language models to predict kinase-inhibitor binding affinity, achieving high accuracy on MAPK pathway kinases and a large kinase dataset.