2 citations · 2 across the 4 of their papers we have counts for
6 papers
Trion-1: Unifying Language, Protein Sequence, and Structure with Diffusion Language Models
Tianyu Lu, Kaicheng Yang, Jiangtao Feng +7
We envision a general-purpose protein foundation model as a unified, interactive model of sequence, structure, and function, capable of understanding, generation, and cross-modal t…
QProtFuse: Query-Guided Layer Fusion for Retrieval-Augmented Protein Function Prediction
Mianzhi Pan, Peishuo Liu, Yawen Ouyang +4
Predicting protein functions from amino acid sequences is essential to understanding biological processes. Large language models (LLMs) offer flexible function descriptions to natu…
AMix-2: Establishing Protein as a Native Modality in Large Language Models
Keyue Qiu, Yixin Wu, Lihao Wang +19
We present AMix-2, a protein-text foundation model that establishes protein as a native modality in large language models (LLMs), unifying protein understanding and sequence design…
Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction
Mianzhi Pan, JianFei Li, Peishuo Liu +5
Metal-organic frameworks (MOFs) are porous crystalline materials with broad applications such as carbon capture and drug delivery, yet accurately predicting their 3D structures rem…
A Periodic Bayesian Flow for Material Generation
Hanlin Wu, Yuxuan Song, Jingjing Gong +6
Generative modeling of crystal data distribution is an important yet challenging task due to the unique periodic physical symmetry of crystals. Diffusion-based methods have shown e…
MoMa: A Modular Deep Learning Framework for Material Property Prediction
Botian Wang, Yawen Ouyang, Yaohui Li +8
Deep learning methods for material property prediction have been widely explored to advance materials discovery. However, the prevailing pre-train then fine-tune paradigm often fai…