activity
20232025
most citedMolCRAFT: Structure-Based Drug Design in Continuous Parameter Space

10 citations · 23 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG2025

ShortListing Model: A Streamlined SimplexDiffusion for Discrete Variable Generation

Yuxuan Song, Zhe Zhang, Yu Pei +7

Generative modeling of discrete variables is challenging yet crucial for applications in natural language processing and biological sequence design. We introduce the Shortlisting M…

q-bio.BM2025

AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model

Changze Lv, Jiang Zhou, Siyu Long +22

We introduce AMix-1, a powerful protein foundation model built on Bayesian Flow Networks and empowered by a systematic training methodology, encompassing pretraining scaling laws,…

q-bio.BM2025★ 1 cited

Steering Protein Family Design through Profile Bayesian Flow

Jingjing Gong, Yu Pei, Siyu Long +7

Protein family design emerges as a promising alternative by combining the advantages of de novo protein design and mutation-based directed evolution.In this paper, we propose Profi…

cs.LG2025★ 2 cited

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…

q-bio.BM2024★ 10 cited

MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space

Yanru Qu, Keyue Qiu, Yuxuan Song +5

Generative models for structure-based drug design (SBDD) have shown promising results in recent years. Existing works mainly focus on how to generate molecules with higher binding…

physics.chem-ph2024★ 3 cited

Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks

Yuxuan Song, Jingjing Gong, Yanru Qu +4

Advanced generative model (e.g., diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to appl…

cs.LG2023★ 2 cited

Equivariant Flow Matching with Hybrid Probability Transport

Yuxuan Song, Jingjing Gong, Minkai Xu +5

The generation of 3D molecules requires simultaneously deciding the categorical features~(atom types) and continuous features~(atom coordinates). Deep generative models, especially…

q-bio.BM2023★ 5 cited

Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D

Bo Qiang, Yuxuan Song, Minkai Xu +5

Generating desirable molecular structures in 3D is a fundamental problem for drug discovery. Despite the considerable progress we have achieved, existing methods usually generate m…