7 citations · 8 across the 5 of their papers we have counts for
5 papers
Unlocking Multimodal Protein Language Models at Inference Time
Yi Zhou, Qipeng Wang, Yunqing Liu +3
Multimodal protein language models (pLMs) learn joint protein sequence-structure distributions, and their generation performance should also depend critically on inference-time sam…
Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactions
Yunqing Liu, Yi Zhou, Wenqi Fan
Molecule representation learning is crucial for understanding and predicting molecular properties. However, conventional atom-centric models, which treat chemical bonds merely as p…
SeedProteo: Accurate De Novo All-Atom Design of Protein Binders
Wei Qu, Yiming Ma, Fei Ye +6
We present SeedProteo, a diffusion-based model for de novo all-atom protein design. We demonstrate how to repurpose a cutting-edge folding architecture into a powerful generative d…
HD-Prot: A Protein Language Model for Joint Sequence-Structure Modeling with Continuous Structure Tokens
Yi Zhou, Haohao Qu, Yunqing Liu +3
Proteins inherently possess a consistent sequence-structure duality. The abundance of protein sequence data, which can be readily represented as discrete tokens, has driven fruitfu…
Computational Protein Science in the Era of Large Language Models (LLMs)
Wenqi Fan, Yi Zhou, Shijie Wang +5
Considering the significance of proteins, computational protein science has always been a critical scientific field, dedicated to revealing knowledge and developing applications wi…