New York University（纽约大学）；Prescient Design（Prescient设计公司）；Genentech（基因泰克）
Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hotzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson
A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling. The generative model samples plausible sequences while the discriminative model guides a search for sequences with high fitness. Given its broad success in conditional sampling, classifier-guided diffusion modeling is a promising foundation for protein design, leading many to develop guided diffusion models for structure with inverse folding to recover sequences. In this work, we propose diffusioN Optimized Sampling (NOS), a guidance method for discrete diffusion models that follows gradients in the hidden states of the denoising network. NOS makes it possible to perform design directly in sequence space, circumventing significant limitations of structure-based methods, including scarce data and challenging inverse design. Moreover, we use NOS to generalize LaMBO, a Bayesian optimization procedure for sequence design that facilitates multiple objectives and edit-based constraints. The resulting method, LaMBO-2, enables discrete diffusions and stronger performance with limited edits through a novel application of saliency maps. We apply LaMBO-2 to a real-world protein design task, optimizing antibodies for higher expression yield and binding affinity to several therapeutic targets under locality and developability constraints, attaining a 99% expression rate and 40% binding rate in exploratory in vitro experiments.
A 3D pocket-aware and evolutionary conserved interaction guided diffusion model for molecular optimization
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Pharmaceutical Science, Sun Yat-sen University（中山大学药学院）；West China School of Pharmacy, Sichuan University（四川大学华西药学院）
Protein Counterfactuals via Diffusion-Guided Latent Optimization
Machine Learning Group, Technische Universität Berlin（技术大学柏林机器学习组）；Berlin Institute for the Foundations of Learning and Data (BIFOLD)（柏林学习与数据基础研究所（BIFOLD））；BASF Digital Solutions GmbH（巴斯夫数字解决方案有限公司）
A 3D pocket-aware and affinity-guided diffusion model for lead optimization
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；West China School of Pharmacy, Sichuan University（四川大学华西药学院）；School of Pharmaceutical Science, Sun Yat-sen University（中山大学药学院）；Global Institute of Future Technology, Shanghai Jiao Tong University（上海交通大学未来技术全球研究院）
Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction
Université de Montréal（蒙特利尔大学）；Mila（米拉计算科学研究所）；Dreamfold（Dreamfold公司）；Duke University（杜克大学）；McGill University（麦吉尔大学）；Allen Institute for AI（艾伦人工智能研究所）；Oxford University（牛津大学）；Aithyra（Aithyra公司）
Mask prior-guided denoising diffusion improves inverse protein folding
School of Computer Science, University of Sheffield（谢菲尔德大学计算机科学学院）；Biologics Engineering, Oncology R&D, AstraZeneca（阿斯利康生物制药工程与肿瘤研发部）；Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca（阿斯利康医药化学与研发部）；School of Biological Sciences, University of Southampton（南安普顿大学生物科学学院）；Centre for Machine Intelligence, University of Sheffield（谢菲尔德大学智能中心）
Reinforcement-guided generative protein language models enable de novo design of highly diverse AAV capsids
LASIGE, Faculdade de Ciências da Universidade de Lisboa（里斯本大学科学学院LASIGE实验室）；BioISI, Faculdade de Ciências da Universidade de Lisboa（里斯本大学科学学院BioISI实验室）；iBET – Instituto de Biologia Experimental e Tecnológica（生物实验与技术研究所）
University of California Berkeley（加州大学伯克利分校）；California Institute of Technology（加州理工学院）；University of Toronto（多伦多大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Morgridge Institute for Research（莫里格研究所）；Université de Montréal（蒙特利尔大学）；Mila-Québec Artificial Intelligence Institute（魁北克人工智能研究所）；Texas A&M University（德克萨斯农工大学）；Nvidia Research（Nvidia研究部）；Argonne National Laboratory（阿贡国家实验室）；National Research Council Canada（加拿大国家研究委员会）；HEC Montréal（蒙特利尔HEC商学院）
ProtInvTree: Deliberate Protein Inverse Folding with Reward-guided Tree Search
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；AI Lab, Research Center for Industries of the Future, Westlake University（未来产业研究院人工智能实验室）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高陵人工智能学院）
Curriculum-guided multimodal representation learning enables generalizable prediction of nanomaterial-protein interactions
School of Engineering, Westlake University（西湖大学工程学院）；Institute of Advanced Technology, Westlake Institute for Advanced Study（西湖研究所在先进科技研究院）；Centre for BioNano Interactions, School of Chemistry, University College Dublin（都柏林大学学院化学系生物纳米相互作用中心）；School of Biomolecular and Biomedical Science, UCD Conway Institute of Biomolecular and Biomedical Research（都柏林大学学院生物分子与生物医学科学系，康沃利斯生物分子与生物医学研究学院）
InstructPro: Natural Language Guided Ligand-Binding Protein Design
Language Technologies Institute, School of Computer Science, Carnegie Mellon University（语言技术研究所，计算机科学学院，卡内基梅隆大学）；Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University（Ray和Stephanie Lane计算生物学系，计算机科学学院，卡内基梅隆大学）；Lambda Inc（Lambda公司）
TR2-D2: Tree Search Guided Trajectory-Aware Fine-Tuning for Discrete Diffusion
Department of Computer and Information Science, University of Pennsylvania（计算机与信息科学系，宾夕法尼亚大学）；School of Mathematics, Georgia Institute of Technology（数学学院，佐治亚理工学院）；Department of Bioengineering, University of Pennsylvania（生物工程系，宾夕法尼亚大学）
Linear Fitness Subspace in Protein Language Models Enables Sample-Efficient Directed Evolution
Nanyang Technological University（南洋理工大学）；Sun Yat-sen University（中山大学）；Zhejiang University（浙江大学）；Carnegie Mellon University（卡内基梅隆大学）；Shanghai Institute of Optics and Fine Mechanics（上海光学精密机械研究所）；Zhongnan Hospital, Wuhan University（武汉大学中南医院）；University of Southern California（南加州大学）
EGRL: Edge generation-guided relation-aware learning for RNA-protein interaction prediction
Macau University of Science and Technology（澳门科技大学）；Macau University of Science and Technology Zhuhai Research Institute（澳门科技大学珠海研究院）；Wuhan University of Technology（武汉理工大学）；Jiangsu University（江苏大学）；Harbin Institute of Technology（哈尔滨工业大学）
