arXiv Machine Learning

SPID: Distilled Protein Backbone Generation

arXiv Machine Learning

Jun 11

ProHiFlo: Hierarchical Flow Matching with Functional Guidance for De Novo Protein Generation

arXiv:2606. 11243v1 Announce Type: new Abstract: De novo protein generation has transformative potential in therapeutic design, enzyme engineering, and synthetic biology.

By Chuanzhen Wang, Meade Cleti, Pete Jano

diffusion

arXiv Machine Learning

Jul 13

Variable-Length Generative Protein Design via Generalized Poisson Flow

arXiv:2607. 09039v1 Announce Type: new Abstract: The ability to generate variable-length proteins is crucial in protein design, where the optimal length is often unknown and tightly coupled to designability.

By Chaoran Cheng, Zhanghan Ni, Yanru Qu, Yuxin Chen, Ruihan Guo, Jiajun Fan, Ge Liu

diffusion multimodal

arXiv AI

Jun 9

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

arXiv:2507. 08920v4 Announce Type: replace-cross Abstract: 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, emergent capability analysis, in-context learning mechanism, and test-time scaling algorithm.

By Changze Lv, Jiang Zhou, Siyu Long, Lihao Wang, Jiangtao Feng, Dongyu Xue, Yu Pei, Hao Wang, Zherui Zhang, Yuchen Cai, Zhiqiang Gao, Ziyuan Ma, Jiakai Hu, Chaochen Gao, Jingjing Gong, Yuxuan Song, Shuyi Zhang, Xiaoqing Zheng, Deyi Xiong, Lei Bai, Wanli Ouyang, Ya-Qin Zhang, Wei-Ying Ma, Bowen Zhou, Hao Zhou

llms safety

arXiv AI

Jun 19

Emyx: Fast and efficient all-atom protein generation

arXiv:2606. 19377v1 Announce Type: cross Abstract: Computational enzyme design requires generating proteins that scaffold catalytic residues and ligands, a task that demands both geometric accuracy and structural diversity from the underlying generative model.

By Nicholas J. Williams, Ward Haddadin, Matteo P. Ferla, Constantin Schneider, Nicholas B. Woodall, Ruby Sedgwick, Christian D. Madsen, Andrew L. Hopkins, Edward O. Pyzer-Knapp

llms rag diffusion benchmarks

arXiv Machine Learning

Aug 4

Refine Drugs, Don't Complete Them: Uniform-Source Discrete Flows for Fragment-Based Drug Discovery

arXiv:2509. 26405v2 Announce Type: replace Abstract: We introduce InVirtuoGen, a discrete flow generative model for fragmented SMILES for de novo and fragment-constrained generation, and target-property/lead optimization of small molecules.

By Benno Kaech, Luis Wyss, Karsten Borgwardt, Gianvito Grasso

fine-tuning benchmarks

arXiv Machine Learning

Jun 9

Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules

arXiv:2606. 08802v1 Announce Type: new Abstract: Standard flow and diffusion pre-training matches the distribution of available data (e.

By Riccardo De Santi, Bruce Lee, Cristian Perez Jensen, Kimon Protopapas, Sophia Tang, Cheng-Hao Liu, Pranam Chatterjee, Yisong Yue, Andreas Krause

diffusion

arXiv Machine Learning

Jun 9

Few-step Cofolding with All-Atom Flow Maps

arXiv:2606. 08375v1 Announce Type: new Abstract: All-atom generative modeling of 3D biomolecular complexes has emerged as the dominant paradigm for predicting the structure of proteins and protein-ligand systems.

By Gianluca Scarpellini, Ron Shprints, Peter Holderrieth, Juno Nam, Pranav Murugan, Rafael G\'omez-Bombarelli, Tommi Jaakola, Maruan Al-Shedivat, Nicholas Matthew Boffi, Avishek Joey Bose

diffusion efficiency benchmarks safety

arXiv AI

Aug 12

Proteo-R1: Reasoning Foundation Models for De Novo Protein Design

arXiv:2605. 02937v2 Announce Type: replace-cross Abstract: Deep learning in de novo protein design has achieved atomic-level fidelity.

By Fang Wu, Weihao Xuan, Heli Qi, Hanqun Cao, Heng-Jui Chang, Zeqi Zhou, Haokai Zhao, Ma Jian, Carl Ma, Yu-Chi Cheng, Kuan Pang, Xiangru Tang, Zehong Wang, Guanlue Li, Hanchen Wang, Kejun Ying, Pan Lu, Chiho Im, Seungju Han, Peng Xia, Tinson Xu, Yinxi Li, Deyao Zhu, Pheng-Ann Heng, Naoto Yokoya, Masashi Sugiyama, Li Erran Li, Jure Leskovec, Yejin Choi

llms diffusion multimodal benchmarks safety

arXiv Machine Learning

Jun 4

Towards A Generative Protein Evolution Machine with DPLM-Evo

arXiv:2605. 00182v3 Announce Type: replace Abstract: Proteins are shaped by gradual evolution under biophysical and functional constraints.

By Xinyou Wang, Liang Hong, Jiasheng Ye, Zaixiang Zheng, Yu Li, Shujian Huang, Quanquan Gu

llms diffusion benchmarks safety

arXiv Machine Learning

Aug 17

READ: A Retrieval-Alignment Diffusion Framework for Structure-based Drug Design

arXiv:2506. 14488v2 Announce Type: replace-cross Abstract: Structure-based drug design (SBDD) models are central to modern pharmaceutical research, enabling the rational exploration of protein-ligand interactions at atomic resolution.

By Dong Xu, Zhangfan Yang, Junchuang Cai, Sisi Yuan, Zexuan Zhu, Jianqiang Li, Junkai Ji

diffusion benchmarks safety

arXiv Machine Learning

Jun 30

Inference-time optimization for experiment-grounded protein ensemble generation

arXiv:2602. 24007v3 Announce Type: replace-cross Abstract: Protein function relies on dynamic conformational ensembles, yet current generative models like AlphaFold3 often fail to produce ensembles that match experimental data.

By Advaith Maddipatla, Anar Rzayev, Marco Pegoraro, Martin Pacesa, Paul Schanda, Ailie Marx, Sanketh Vedula, Alex M. Bronstein

rag diffusion benchmarks safety

arXiv Machine Learning

Jun 26

Training-Free Generation of Protein Sequences from Small Family Alignments via Stochastic Attention

arXiv:2603. 14717v2 Announce Type: replace Abstract: Generating novel protein sequences that respect a family's statistical constraints typically requires training deep generative models on thousands to millions of examples.

By Jeffrey D. Varner

llms safety

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