Rubo Wang

siProGenA: Generative siRNA Candidate Construction via Position Proposal and Guide Generation

siProGenA generates siRNA candidates by proposing positions and generating guide sequences, achieving high Hit@1 and Hit@5 scores in controlled evaluations.

Optimizing broadly neutralizing antibodies via all-atom interaction modeling and pre-trained language models

This paper introduces InterAb and InterAb-Opt, computational frameworks that predict and optimize broadly neutralizing antibodies using all-atom modeling and pre-trained language models, showing significant improvements in binding affinity and neutralization activity.

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

IgGM2 is an all-atom foundation model for immune receptor design that jointly predicts CDR sequences and full-atom structures, outperforming existing methods in structure prediction and sequence design benchmarks.

A synergistic generative-ranking framework for tailored design of therapeutic single-domain antibodies

This paper introduces TFDesign-sdAb, a deep learning framework for designing therapeutic single-domain antibodies with tailored functionalities, achieving high success rates in generating Protein A-binding sdAbs.

A Generative Foundation Model for Antibody Design

IgGM is a generative model for antibody design that produces high-affinity antibodies through tasks like structure prediction and affinity maturation, validated on multiple antigens.

IgGM: A Generative Model for Functional Antibody and Nanobody Design

IgGM is a generative model that designs functional antibodies and nanobodies by predicting sequences and structures simultaneously for a given antigen.

Fast and accurate modeling and design of antibody-antigen complex using tFold

This paper introduces tFold-Ab and tFold-Ag for fast and accurate prediction of antibody and antibody-antigen complex structures, showing improved speed and performance over AlphaFold-Multimer.