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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Ligand-Conditioned Discrete Diffusion for Protein Sequence-Structure Co-Design
May 15, 2026Chen Wei, Fanding Xu, Minghao Sun +5Protein Language Models Diffusion Language Model Decoding
Xi’an University of Posts & Telecommunications · National University of Singapore · Xi’an Jiaotong University +1
Deep Learning for Protein Complex Prediction and Design
May 11, 2026Ziwei XieProtein Structure Prediction Molecular Generation
TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO, Chicago, Illinois
ProteinOPD: Towards Effective and Efficient Preference Alignment for Protein Design
May 11, 2026Yulin Zhang, He Cao, Zihao Jiang +6Protein Language Models Multi-Teacher Knowledge Distillation
Tsinghua University · International Digital Economy Academy · Hong Kong University of2026 Science and Technology (Guangzhou) +1
TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation
May 10, 2026Hanqun Cao, Aastha Pal, Sophia Tang +4Molecular Generation Protein Design
Department of Computer Science and Engineering, The Chinese University of Hong Kong · Department of Bioengineering, University of Pennsylvania · Department of Computer and Information Science, University of Pennsylvania +1
Conditional generation of antibody sequences with classifier-guided germline-absorbing discrete diffusion
May 7, 2026Justin Sanders, Luca Giancardo, Lan Guo +4Diffusion Model Guidance Protein Language Models
Paul G. Allen School of Computer Science and Engineering, University of Washington · ∗Work performed during internship at Amazon Web Services · Life Sciences, Amazon Web Services
ProtDBench: A Unified Benchmark of Protein Binder Design and Evaluation
May 5, 2026Cong Liu, Milong Ren, Jiaqi Guan +4Protein Structure Prediction Benchmark Construction
AMLab, AI4Science Lab, University of Amsterdam, Amsterdam, The Netherlands · Work done at ByteDance Seed · ByteDance Seed
Expanding functional protein sequence space using high entropy generative models
May 5, 2026Roberto Netti, Emily Hinds, Francesco Calvanese +3Molecular Generation Protein Design
Sorbonne Universit´e, CNRS, Department of Computational, Quantitative and Synthetic Biology—CQSB, 75005 Paris, France · Center for Physics of Evolving Systems, University of Chicago, Chicago, IL, USA · Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, USA +4
A-CODE: Fully Atomic Protein Co-Design with Unified Multimodal Diffusion
May 5, 2026Chaoran Cheng, Jiaqi Guan, Milong Ren +5Generative Modeling Molecular Generation
University of Illinois Urbana-Champaign · 2ByteDance Seed
Co-Generative De Novo Functional Protein Design
May 1, 2026Xinrui Chen, Yizhen Luo, Siqi Fan +1Protein Language Models Molecular Generation
Institute for AI Industry Research (AIR), Tsinghua University · Department of Computer Science and Technology, Tsinghua University · PharMolix Inc.
Proteo-R1: Reasoning Foundation Models for De Novo Protein Design
May 1, 2026Fang Wu, Weihao Xuan, Heli Qi +26Generative Modeling 3D Molecular Generation
Stanford University
RosettaSearch: Multi-Objective Inference-Time Search for Protein Sequence Design
Apr 19, 2026Meghana Kshirsagar, Allen Nie, Ching-An Cheng +5Inference-Time Search Large Language Model-Guided Optimization
AI for Good, Microsoft Redmond · Google DeepMind · Google Research +3
ProtoCycle: Reflective Tool-Augmented Planning for Text-Guided Protein Design
Apr 18, 2026Yutang Ge, Guojiang Zhao, Sihang Li +7LLM Self-Refinement LLM Tool Use
Shanghai Jiao Tong University, School of Mathematical Sciences · DP Technology · University of Science and Technology of China +1
BOAT: Navigating the Sea of In Silico Predictors for Antibody Design via Multi-Objective Bayesian Optimization
Apr 15, 2026Jackie Rao, Ferran Gonzalez Hernandez, Leon Gerard +1Protein Design Surrogate-Assisted Optimization
MRC Biostatistics Unit, University of Cambridge, UK · Centre for AI, Data Science and Artificial Intelligence, R&D, AstraZeneca, Barcelona, Spain
Biologically-Grounded Multi-Encoder Architectures as Developability Oracles for Antibody Design
Apr 10, 2026Simon J. CrouzetMolecular Property Prediction Protein Design
General Multimodal Protein Design Enables DNA-Encoding of Chemistry
Apr 6, 2026Jarrid Rector-Brooks, Théophile Lambert, Marta Skreta +15Protein Design Molecular Generation
California Institute of Technology · Mila – Québec AI Institute · Université de Montréal +9
Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute
Mar 30, 2026Kieran Didi, Zuobai Zhang, Guoqing Zhou +11Protein Binder Design Protein Design
NVIDIA · University of Oxford · Mila - Québec AI Institute +8
Self-evolving AI agents for protein discovery and directed evolution
Mar 28, 2026Yang Tan, Lingrong Zhang, Mingchen Li +5Autonomous Research Agents Self-Evolving Agents
Shanghai National Center for Applied Mathematics (SJTU Center) & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China. · Shanghai Innovation Institute, Shanghai, 200231, China. · School of Computer Science, Shanghai Jiao Tong University, Shanghai, 200240, China. +1
Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Evaluation Standards
Mar 27, 2026Senura Hansaja Wanasekara, Minh-Duong Nguyen, Xiaochen Liu +2Generative Modeling Protein Design
Biomedical Engineering, The University of Sydney, Darlington, NSW 2006, Australia. · the College of Engineering and Computer Science, VinUniversity, Vietnam. · School of Computer Science and School, The University of Sydney, Darlington, NSW 2006, Australia.
Conditioning Protein Generation via Hopfield Pattern Multiplicity
Mar 20, 2026Jeffrey D. VarnerConditional Generative Modeling Conditional Generation
Robert Frederick Smith School of Chemical and Biomolecular Engineering Cornell University, Ithaca, NY 14850
Reinforcement-guided generative protein language models enable de novo design of highly diverse AAV capsids
Mar 19, 2026Lucas Ferraz, Ana F. Rodrigues, Pedro Giesteira Cotovio +5Protein Design Constrained Generative Modeling
LASIGE, Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal · iBET – Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal · BioISI, Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal
Binary Latent Protein Fitness Landscapes for Quantum Annealing Optimization
Mar 18, 2026Truong-Son HyProtein Design Latent Space Optimization
University of Alabama at Birmingham, Birmingham, Alabama, USA
PI-Mamba: Linear-Time Protein Backbone Generation via Spectrally Initialized Flow Matching
Mar 17, 2026Tianyu Wu, Lin ZhuProtein Design Molecular Generation
Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, 600 S Mathews Ave., 61801, IL, USA · School of Information Science, University of Illinois Urbana-Champaign, 501 E Daniel St., 61820, IL, USA
Protein Design with Agent Rosetta: A Case Study for Specialized Scientific Agents
Mar 16, 2026Jacopo Teneggi, S. M. Bargeen A. Turzo, Tanya Marwah +4Tool-Augmented Language Model Agents AI Agents for Scientific Discovery
Polymathic AI · Johns Hopkins University · Center for Computational Biology, Flatiron Institute +3
Trust-Region Noise Search for Black-Box Alignment of Diffusion and Flow Models
Mar 15, 2026Niklas Schweiger, Daniel Cremers, Karnik RamDiffusion Model Alignment Black-Box Optimization
Technical University of Munich, Germany · Munich Center for Machine Learning, Germany
Refold: Refining Protein Inverse Folding with Efficient Structural Matching and Fusion
Mar 15, 2026Yiran Zhu, Changxi Chi, Hongxin Xiang +3Protein Design
AIMS Lab, HKUST (GZ), Guangzhou, China · North China Electric Power University, Baoding, China · Westlake University, Hangzhou, China +4
EvoFlows: Evolutionary Edit-Based Flow-Matching for Protein Engineering
Mar 12, 2026Nicolas Deutschmann, Constance Ferragu, Jonathan D. Ziegler +2Protein Design Sequence Modeling
Cradle
Protein Counterfactuals via Diffusion-Guided Latent Optimization
Mar 11, 2026Weronika Kłos, Sidney Bender, Lukas KadesCounterfactual Explanations Protein Design
Machine Learning Group, Technische Universität Berlin, Berlin, Germany · Berlin Institute for the Foundations of Learning and Data (BIFOLD) · BASF Digital Solutions GmbH, Ludwigshafen am Rhein, Germany
How to make the most of your masked language model for protein engineering
Mar 11, 2026Calvin McCarter, Nick Bhattacharya, Sebastian W. Ober +1Protein Language Models Inference-Time Search
BigHat Biosciences
Property-driven Protein Inverse Folding With Multi-Objective Preference Alignment
Mar 6, 2026Xiaoyang Hou, Junqi Liu, Chence Shi +3Protein Design Preference Alignment
BioGeometry · School of Computer Science, Peking University · Mila - Québec AI Institute +3
Deep learning-guided evolutionary optimization for protein design
Mar 3, 2026Erik Hartman, Di Tang, Johan MalmströmProtein Design Surrogate-Assisted Optimization
Division of Infection Medicine, Faculty of Medicine, Lund University, Sweden

May 15, 2026J/K move · Enter open · S save

Ligand-Conditioned Discrete Diffusion for Protein Sequence-Structure Co-Design

Chen Wei, Fanding Xu, Minghao Sun, Zhiyuan Liu+4

Xi’an University of Posts & Telecommunications · National University of Singapore · Xi’an Jiaotong University+1

Protein Language Models Diffusion Language Model Decoding Molecular Generation Diffusion Language Models Protein Design

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Proteins perform their biological functions through three-dimensional structures encoded by amino acid sequences, and ligand-binding protein co-design requires models that generate sequence-structure compatible proteins under explicit ligand constraints. Although continuous diffusion and flow-based models support ligand-aware design in coordinate or latent spaces, existing discrete diffusion protein language models mainly operate over sequence or structure tokens without direct small-molecule conditioning. We introduce \textbf{ProtLiD

^2

}, a Protein Ligand-conditioned Discrete Diffusion model for protein sequence-structure co-design. ProtLiD

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jointly generates amino-acid sequence and discrete structure tokens while incorporating ligand chemical and geometric information through geometry-aware cross-attention. Trained on over one million ligand-protein complexes, ProtLiD

^2

extends masked discrete diffusion to ligand-aware functional protein design. We further propose maximum confidence-margin guided ReMask decoding, an inference-time self-correction strategy that retains confident predictions and remasks uncertain tokens. ProtLiD

^2

improves global fold confidence over Complexa in whole-protein design, increasing TM-score from 0.672 to 0.802 and pLDDT from 64.55 to 73.00. In pocket co-design, ProtLiD

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reduces active-site BB-RMSD from 3.46/3.40Å for FAIR/PocketGen to 1.97Å, and improves ligand-aware pass rates over PocketGen from 14.86% to 59.73% and from 6.08% to 23.49% under stricter docking thresholds. These results support ligand-conditioned discrete diffusion as an effective token-space framework for functional protein co-design. Code will be available at https://github.com/auroua/ProtLiD.