Xutong Li

Functional orthogonality of WRN inhibitor resistance enables alternating therapy and mutation-tolerant inhibitor design in MSI-H cancers

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

MetaReact: A Reaction-Aware Transformer for End-to-End Prediction of Drug Metabolism

MetaReact predicts drug metabolites and enzyme selectivity using a reaction-aware Transformer, outperforming existing methods in accuracy and generalizability.

Advancing Ligand Binding Affinity Prediction with Cartesian Tensor-Based Deep Learning

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.

SurfDock is a Surface-Informed Diffusion Generative Model for Reliable and Accurate Protein-ligand Complex Prediction

SurfDock improves protein-ligand complex prediction by integrating multiple protein representations into a diffusion model, achieving higher accuracy and physical plausibility than existing methods.

Identify compound-protein interaction with knowledge graph embedding of perturbation transcriptomics

PertKGE improves compound-protein interaction prediction using knowledge graph embedding of perturbation transcriptomics, outperforming baselines in cold-start settings and identifying novel therapeutic targets.

TranSiGen: Deep representation learning of chemical-induced transcriptional profile

TranSiGen denoises and reconstructs chemical-induced transcriptional profiles using deep generative modeling, effectively predicting differential expression genes and serving as alternative phenotype information for drug discovery.

EquiScore: A generic protein-ligand interaction scoring method integrating physical prior knowledge with data augmentation modeling

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

Automated design and optimization of multitarget schizophrenia drug candidates by deep learning

This paper develops a deep learning system to design multitarget antipsychotic drugs, generating novel compounds with activity against multiple receptors and demonstrating efficacy in animal models.

Computational target fishing by mining transcriptional data using a novel Siamese spectral-based graph convolutional network

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.