Rongqing Yuan

TriFlow, discrete flow-matching sequence designer with global three-track structure attention, raises in silico binder success over ProteinMPNN in RFdiffusion and BindCraft-style pipelines on 500 targets and flags functional sites.

The paper develops TriCyP, a model to predict cysteine states in protein structures, achieving high accuracy and identifying new biological patterns and protein families.

This paper evaluates progress in predicting protein complex structures in CASP16, finding moderate improvements but ongoing challenges in model ranking, stoichiometry prediction, and handling difficult targets.

Across 42 CASP16 nucleic acid targets, accuracy stayed tied to template availability; human experts (Vfold first) led servers and AlphaFold 3, though servers now improve on templates on average.

The paper predicts 18,316 human protein-protein interactions with 90% precision using enhanced coevolutionary analysis and a new deep learning model.

This paper assesses CASP16 monomer structure prediction, showing near-solution for single-domain folds but highlighting challenges in truncated sequences and model ranking.

This paper introduces MIDI, a model that predicts cancer drug response and explains drug-target interactions using an attention mechanism, outperforming existing methods in prediction and interpretability.

The paper introduces a Bayesian framework to map cell-cell interactions from spatial transcriptomics data, revealing new biological insights in cancer and immune responses.

This paper introduces PLAN, a machine learning model for antibody humanization, showing improved humanness scores and mutation overlap with experimental validation.

The paper compares newly born orphan proteins to never born proteins using three deep learning algorithms to predict their structural properties and assess their similarity.