Protein Design

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23 papers in the last four weeks, up 188% on the four weeks before. 0.1% of all new papers.

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Rigidity-Aware Geometric Pretraining for Protein Design and Conformational Ensembles
Mar 2, 2026Zhanghan Ni, Yanjing Li, Zeju Qiu +4Generative Modeling Protein Design
University of Illinois Urbana-Champaign · University of Washington · MPI for Intelligent Systems, Tübingen +3
AAVGen: Precision Engineering of Adeno-associated Viral Capsids for Renal Selective Targeting
Feb 21, 2026Mohammadreza Ghaffarzadeh-Esfahani, Yousof GheisariProtein Design
Regenerative Medicine Research Center, Isfahan University of Medical Sciences, Isfahan, Iran · Department of Genetics and Molecular Biology, Isfahan University of Medical Sciences, Isfahan, Iran
Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability
Feb 16, 2026Ana F. Rodrigues, Lucas Ferraz, Laura Balbi +2Protein Representation Learning Protein Language Models
LASIGE, Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal
SPID: Distilled Protein Backbone Generation
Oct 3, 2025Liyang Xie, Haoran Zhang, Zhendong Wang +2Diffusion Model Distillation Few-Step Diffusion Sampling
The University of Texas at Austin · The Memorial Sloan Kettering Cancer Center
ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning
Jun 9, 2025Ziwen Wang, Jiajun Fan, Ruihan Guo +3Protein Language Models Molecular Generation
Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, IL, USA · DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, IL, USA
PDFBench: A Benchmark for De novo Protein Design from Function
May 25, 2025Jiahao Kuang, Nuowei Liu, Jie Wang +3Benchmark Design Molecular Generation
School of Computer Science and Technology, East China Normal University · Institute of Artificial Intelligence (TeleAI), China Telecom · College of Foreign Languages and Literatures, Fudan University
Expected flow networks in stochastic environments and two-player zero-sum games
Oct 4, 2023Marco Jiralerspong, Bilun Sun, Danilo Vucetic +4Game-Playing Agents Zero-Sum Games
Mila – Qu´ebec AI Institute, Universit´e de Montr´eal

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Rigidity-Aware Geometric Pretraining for Protein Design and Conformational Ensembles

Zhanghan Ni, Yanjing Li, Zeju Qiu, Bernhard Schölkopf+3

University of Illinois Urbana-Champaign · University of Washington · MPI for Intelligent Systems, Tübingen+3

Generative Modeling Protein Design Self-Supervised Pre-Training Protein Representation Learning Geometric Representation Learning

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Generative models have recently advanced

\textit{de novo}

protein design by learning the statistical regularities of natural structures. However, current approaches face three key limitations: (1) Existing methods cannot jointly learn protein geometry and design tasks, where pretraining can be a solution; (2) Current pretraining methods mostly rely on local, non-rigid atomic representations for property prediction downstream tasks, limiting global geometric understanding for protein generation tasks; and (3) Existing approaches have yet to effectively model the rich dynamic and conformational information of protein structures. To overcome these issues, we introduce

\textbf{RigidSSL}

(

\textit{Rigidity-Aware Self-Supervised Learning}

), a geometric pretraining framework that front-loads geometry learning prior to generative finetuning. Phase I (RigidSSL-Perturb) learns geometric priors from 432K structures from the AlphaFold Protein Structure Database with simulated perturbations. Phase II (RigidSSL-MD) refines these representations on 1.3K molecular dynamics trajectories to capture physically realistic transitions. Underpinning both phases is a bi-directional, rigidity-aware flow matching objective that jointly optimizes translational and rotational dynamics to maximize mutual information between conformations. Empirically, RigidSSL variants improve designability by up to 43% while enhancing novelty and diversity in unconditional generation. Furthermore, RigidSSL-Perturb improves the success rate by 5.8% in zero-shot motif scaffolding and RigidSSL-MD captures more biophysically realistic conformational ensembles in G protein-coupled receptor modeling.