Jianhua Yao

Spatial transcriptomics reveal developmental dynamics of the human cerebral cortex and striatum.

R1-32-like public antibodies acquire tolerance to SARS-CoV-2 antigenic drift through somatic hypermutation.

R1-32-like public antibodies, characterized by shared IGHV1-69/IGLV1-40 usage, are elicited in more than 50% of individuals with COVID-19 and have been implicated in driving recurrent mutations at L452SARS2 and F490SARS2 within their convergent epitope in the SARS-CoV-2 spike receptor-binding domain. These mutations ef...

TCRTSdesign: End-to-End Co-Design of Antigen-Specific TCR Sequences and Structures

This work introduces TCRTSdesign, a framework that concurrently generates novel TCR sequences with specific binding capabilities to target pMHC molecules and predicts the full-atom structures of the TCR-pMHC complex, while optimizing their binding affinity.

TCRTSdesign: End-to-End Co-Design of Antigen-Specific TCR Sequences and Structures

Designing functional T cell receptors (TCRs) for a given peptide presented by MHC (pMHC) is an emerging yet highly challenging problem in computational immunology. While recent approaches have achieved initial progress, they face two major limitations: (1) the lack of structural information from TCR–pMHC complexes in t...

IgGM2: An All-Atom Foundation Model for Adaptive Immune Receptor Design

IgGM2 follows a structure-to-design strategy: it first learns how immune receptors are positioned around fixed target structures, and then transfers this target-conditioned structural prior to CDR design, allowing frame-work geometry to adapt to designed CDRs without separate inverse folding or external sidechain packi...

A blinded, prospective benchmark of in silico antibody discovery anchored to experimental affinity and developability.

The AIntibody challenge shows that AI can optimize antibodies in defined, biologically grounded regimes, in addition to highlighting critical gaps including affinity prediction and library-inspired antibody design and cross-task generalization.

Branch-JEPA: Finite-Support Predictive Distributions for JEPA World Models

Branch-JEPA is introduced, which replaces this point-valued transition with a context-weighted finite set of latent successors, and preserves more distinct futures, while full-set scoring improves the quality of the resulting predictive distribution.