Chang‐Yu Hsieh

SubcellPert benchmarks subcellular perturbation prediction using imaging data, showing Gaussian transport achieves low spatial error in out-of-distribution datasets.

SynPROTAC generates novel PROTAC molecules with feasible synthetic routes and favorable properties using a synthesis-constrained generative model and reinforcement learning.

ProtFlow generates high-quality protein sequences by learning the full semantic distribution of protein space, outperforming existing methods in generating antimicrobial peptides with broad activity.

Ophiuchus-Ab is a generative foundation model for antibody design that captures inter-chain dependencies and predicts antibody properties like developability and binding affinity.

ECloudGen improves molecular design by using electron clouds as latent variables, generating more potent binders and exploring broader chemical space than existing methods.

This paper introduces PROTAC-STAN, a deep learning framework for interpretable PROTAC degradation prediction, achieving improved accuracy and model interpretability.

This paper introduces ProtET, a method for CLIP-informed protein editing using multi-modality learning, achieving significant improvements in protein stability and functionality.

MdrDB is a database of mutation-induced drug resistance information, combining experimental data and computational predictions to study protein-ligand binding changes.

This paper introduces MolCode, a generative framework for molecule design that integrates 2D graph and 3D geometry information, achieving high validity and affinity in generated molecules.

Deep-BGCpred improves BGC prediction by reducing false positives using deep learning and customized strategies, achieving state-of-the-art results on benchmarks.