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Liang Hong

8 accepted papers

2026

Fast and Interpretable Protein Substructure Alignment via Optimal Transport

ICLR 2026poster

Proteins are essential biological macromolecules that execute life functions. Local motifs within protein structures, such as active sites, are the most critical components for linking structure to function and are key to understanding protein evolution and enabling protein engineering. Existing com…

Cited by 0SourceScholar
2026

RMSAGen: Integrating Multiple Sequence Alignment for Function RNA Design

AAAI 2026technical

Biological sequences, including RNAs and proteins, share similarities with natural languages, enabling the application of advanced language models to various biological tasks. However, due to its flexibility and lack of experimental data, RNA is a particularly challenging biological ``language

Cited by 0SourcePDFScholar
2026

Towards A Generative Protein Evolution Machine with DPLM-Evo

ICML 2026poster

Proteins are shaped by gradual evolution under biophysical and functional constraints. Protein language models learn rich evolutionary constraints from large-scale sequence data, and discrete diffusion–based protein language models (e.g., DPLMs) have emerged as a promising framework for both underst…

Cited by 0SourceScholar
2026

VenusX: Unlocking Fine-Grained Functional Understanding of Proteins

ICLR 2026poster

Deep learning models have driven significant progress in predicting protein function and interactions at the protein level. While these advancements have been invaluable for many biological applications such as enzyme engineering and function annotation, a more detailed perspective is essential for…

Cited by 0SourcecodeScholar
2025

Immunogenicity Prediction with Dual Attention Enables Vaccine Target Selection

ICLR 2025poster

Immunogenicity prediction is a central topic in reverse vaccinology for finding candidate vaccines that can trigger protective immune responses. Existing approaches typically rely on highly compressed features and simple model architectures, leading to limited prediction accuracy and poor generaliza…

2025

Venus-MAXWELL: Efficient Learning of Protein-Mutation Stability Landscapes using Protein Language Models

NeurIPS 2025poster

In-silico prediction of protein mutant stability, measured by the difference in Gibbs free energy change ($\Delta \Delta G$), is fundamental for protein engineering. Current sequence-to-label methods typically employ two-stage pipelines: (i) encoding mutant sequences using neural networks (e.g., tra…

Cited by 0SourcecodeScholar
2024

ProSST: Protein Language Modeling with Quantized Structure and Disentangled Attention

NeurIPS 2024poster

Protein language models (PLMs) have shown remarkable capabilities in various protein function prediction tasks. However, while protein function is intricately tied to structure, most existing PLMs do not incorporate protein structure information. To address this issue, we introduce ProSST, a Transfo…

Cited by 0SourcePDFScholar
2024

ReactZyme: A Benchmark for Enzyme-Reaction Prediction

NeurIPS 2024poster

Enzymes, with their specific catalyzed reactions, are necessary for all aspects of life, enabling diverse biological processes and adaptations. Predicting enzyme functions is essential for understanding biological pathways, guiding drug development, enhancing bioproduct yields, and facilitating evol…