Xiaomin Luo

This study shows that resistance to different WRN inhibitors in MSI-H cancers creates orthogonal vulnerabilities, enabling alternating therapy and mutation-tolerant inhibitor design.

This paper introduces MolGenBench, a benchmark for evaluating molecular generative models in drug discovery, highlighting gaps between current models and real-world pharmaceutical workflows.

PBCNet2.0 predicts protein-ligand binding affinity using a cartesian tensor-based Siamese Neural Network, achieving high accuracy and efficiency compared to physics-based simulations.

EquiScore is a protein-ligand interaction scoring method that outperforms existing methods on external test sets and shows potential for lead compound optimization.

This paper introduces a sequence-to-drug paradigm using TransformerCPI2.0 to discover drug-like molecules from protein sequences and validates its effectiveness for three targets.

The paper introduces a Siamese spectral-based graph convolutional network to infer protein targets of chemical compounds from gene expression data, achieving better performance than existing methods.