Li Xie

ProMoNet improves drug discovery by predicting protein-ligand binding affinity and kinetics using sequence-based pre-training without 3D structural data.

The paper introduces eMOSAIC, a method for uncertainty quantification in multi-target ligand binding affinity predictions, showing it outperforms existing methods in out-of-distribution settings.

The paper develops PrePROTAC, an interpretable machine learning model to predict PROTAC-induced protein degradation targets, identifying over 600 novel understudied proteins and proposing PROTAC compounds for Alzheimer's disease targets.

The paper introduces MMAPLE, a semi-supervised meta-learning framework that improves prediction of out-of-distribution molecular interactions by leveraging unlabeled data, achieving significant improvements over baselines.

PortalCG predicts chemical-protein interactions for dark proteins using a deep learning framework that integrates binding site information and meta-learning, outperforming existing methods in out-of-distribution scenarios.

The paper investigates the potential of Posaconazole to inhibit Tiam1 and disrupt Rho GTPase activity, offering a new approach for treating Chagas Disease.