3 citations · 5 across the 6 of their papers we have counts for
6 papers
AgentFold: Closed-Loop Agentic Search for Protein Folding Model Design
Mingquan Liu, Jiangyu Chen, Hanqun Cao +9
Scientific LLM agents have shown promise in literature reasoning, tool use, and experiment planning, but it remains unclear whether they can autonomously improve large, tightly cou…
SurfDesign: Effective Protein Design on Molecular Surfaces
Fang Wu, Shuting Jin, Xiangru Tang +5
Protein function is largely determined by molecular surface geometry and physicochemical complementarity, yet most protein design methods condition only on backbone structure. We i…
Surface-based Molecular Design with Multi-modal Flow Matching
Fang Wu, Zhengyuan Zhou, Shuting Jin +3
Therapeutic peptides show promise in targeting previously undruggable binding sites, with recent advancements in deep generative models enabling full-atom peptide co-design for spe…
D-Flow: Multi-modality Flow Matching for D-peptide Design
Fang Wu, Shuting Jin, Xiangru Tang +3
Among these, D-peptides are resistant to proteolysis, exhibit greater in vivo stability, and are easier to synthesize. Despite advances in deep learning for peptide discovery, the…
Instructor-inspired Machine Learning for Robust Molecular Property Prediction
Fang Wu, Shuting Jin, Siyuan Li +1
Machine learning catalyzes a revolution in chemical and biological science. However, its efficacy heavily depends on the availability of labeled data, and annotating biochemical da…
Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport
Fang Wu, Nicolas Courty, Shuting Jin +1
Training data are usually limited or heterogeneous in many chemical and biological applications. Existing machine learning models for chemistry and materials science fail to consid…