Designing, folding, and predicting protein structure and function with generative models.
48 papers in this topic (ICML 2026).
Hengyuan Cao, Shizhuo Cheng, Mingxuan Liu, Weicheng Huang +4
The rapid evolution of generative models has unlocked new potentials in protein binder design, a pivotal task in structural biology, by facilitating end-to-end generation via…
Minghan Li, Fengji Li, Yilin Tao, Yue Deng
Computational protein design typically employs a sequential workflow of structure generation followed by sequence (re)design. While structure generators can be explicitly…
Kieran Didi, Sarah Alamdari, Alex Xijie Lu, Bruce James Wittmann +4
Machine learning methods that predict protein fitness from sequence remain sensitive to changes in data distributions, limiting generalization across common conditions encountered…
Yanru Qu, Cheng-Yen Hsieh, Zaixiang Zheng, Ge Liu +1
We present protein autoregressive modeling (PAR), the first multi-scale autoregressive framework for protein backbone generation via coarse-to-fine next-scale prediction. Using…
Dexiong Chen, Andrei Manolache, Mathias Niepert, Karsten Borgwardt
Classifying protein topology is essential for deciphering biological function, but progress is held back by the lack of large-scale benchmarks that avoid duplicates and by models…
Hanqun Cao, Aastha Pal, Sophia Tang, Yinuo Zhang +3
Protein function is often controlled by ligands that bias the direction of state transitions, such as agonists and antagonists, rather than stabilizing a single conformation. This…
Kexin Zhang, Weichen Qin, Yue Teng, Jiale Yu +4
Agent-centered scientific discovery is turning scientific models into always-on computational infrastructure. In this paradigm, AI agents coordinate tools, interpret feedback, and…
Mujie Lin, Yutian Liu, Yudi Guo, Yanzhen Hou +4
Generating long-horizon molecular dynamics (MD) is difficult due to error accumulation in time-domain autoregressive models, which causes drift, and fixed step-size constraints on…
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
Deep protein structure predictors such as AlphaFold provide confidence estimates (e.g., pLDDT) that are not calibrated and degrade under distribution shifts across experimental…
Xinrui Chen, Yizhen Luo, Siqi Fan, Zaiqing Nie
*De novo* functional protein design aims to generate protein sequences that realize specified biochemical functions without relying on evolutionary templates, enabling broad…
Stephen Zhewen Lu, Aakarsh Vermani, Kohei Sanno, Jiarui Lu +3
Common deep learning approaches for antibody engineering focus on modeling the marginal distribution of sequences. By treating sequences as independent samples, however, these…
Ziyi Yang, Zitong Tian, Yinjun Jia, Tianyi Zhang +4
D-peptide binders targeting L-proteins have promising therapeutic potential. Despite rapid advances in machine learning-based target-conditioned peptide design, generating…
Keyue Qiu, Xintong Wang, Zhilong Zhang, Hao Zhou +1
Biomolecules such as proteins and small-molecule ligands play a central role in biological systems, arising from the tight interplay between sequence and three-dimensional…
An Zeyu, Wanyu Lin
$\textit{De novo}$ peptide sequencing is a pivotal technique that directly reconstructs amino acid sequences from tandem mass spectrometry (MS/MS) data; it enables the…
Zaifei YANG, Samuel Ping-Man Choi, James Kwok
Protein-protein interactions (PPIs) are essential for many biological processes. However, existing PPI prediction approaches suffer from two major limitations: they overlook the…
Haoyang Luan, Gufeng Yu, Letian Chen, Zhenran Xiao +3
The *de novo* generation of high-affinity epitopes tailored to specific major histocompatibility complex (MHC) proteins is a pivotal challenge in computational immunotherapy.…
Jeonghyeon Kim, Nathaniel Blalock, Ameya Kulkarni, Kensuke Nakamura +1
Antibodies originate from germline templates and are diversified by somatic hypermutation, producing sequences in which conserved germline residues scaffold structure while rare…
Martin Jankowiak, Yerdos Ordabayev, Rudraksh Tuwani, Henry Neil Ward +3
Despite its importance to applications in protein design, predicting protein properties like binding affinity and thermostability from sparse experimental data remains a…
Riccardo Tedoldi, Ola Engkvist, Patrick Bryant, Hossein Azizpour +2
Sampling useful three-dimensional molecular structures along with their most favorable conformations is a key challenge in drug discovery. Current state-of-the-art 3D de-novo…
Yiyuan Zhang, Cailong Hua, VINITENDRA SINGH, Joseph M. Muretta +2
Many fundamental biological processes are governed by mechanical forces, with proteins acting as the key molecular mediators. Elucidating how protein unfolding responds to force…
Haoran liu, Xiaoli Lin, Jing Hu, Yu Zou +1
Polypharmacology provides a powerful strategy for treating complex diseases, but identifying molecules that simultaneously satisfy coupled constraints across multiple biological…
Xiang Fang, Wanlong Fang, Wei Ji
Large Vision-Language Models have achieved unprecedented success in zero-shot recognition by aligning visual features with broad semantic concepts. However, this semantic…
Sai Advaith Maddipatla, Anar Rzayev, Marco Pegoraro, Martin Pacesa +4
Protein function relies on dynamic conformational ensembles, yet current generative models like AlphaFold3 (AF3) often fail to produce ensembles that match experimental data.…
Mingqing Wang, Zhiwei Nie, ATHANASIOS V. VASILAKOS, Yonghong He +1
Proteins encode diverse functions within complex three-dimensional structures, yet most deep learning representations remain highly entangled, obscuring the biophysical signals…
Ziqi Gao, Chenyi Zi, Zijing Liu, Ziqiao Meng +2
Protein-protein interactions (PPIs) are fundamental to cellular function, disease mechanisms, and drug discovery. Current learning-based PPI predictors focus on learning powerful…
Yuxing Wang, Wenyi Zhang, Yilong Zou, Jing Huang
Computational identification of lipid-binding proteins is critical for both fundamental research and therapeutic development. Existing models are typically trained in a fully…
Alicja Maksymiuk, Alexandre Duplessis, Michael M. Bronstein, Alexander Tong +2
Macrocycles are ring-shaped molecules that offer a promising alternative to small-molecule drugs due to their enhanced selectivity and binding affinity against difficult targets.…
Yitian Wang, Fanmeng Wang, Angxiao Yue, Wentao Guo +2
Modeling peptide cyclization is critical for the virtual screening of candidate peptides with desirable physical and pharmaceutical properties. This task is challenging because a…
Ruihan Guo, Chaoran Cheng, Zhanghan Ni, Neil He +2
Protein mutation effect prediction is fundamental to protein engineering and disease variant interpretation, yet experimentally measured mutation data remain accurate but…
Annabel Large, Ian Holmes
Most statistical phylogenetics analyses use simple continuous-time finite-state Markov models of point substitution to describe molecular evolution. These models enforce…
Song Ouyang, Zhijie Dong, Yong Luo, Kehua Su +3
Computational protein design holds immense promise across diverse domains, but existing approaches face significant challenges: traditional physics-based methods require…
Jiahao Kuang, Nuowei Liu, Changzhi Sun, Jie Wang +2
Function-guided protein design is a crucial task with significant applications in drug discovery and enzyme engineering. However, the field lacks a unified and comprehensive…
Marcin Możejko, Adam Bielecki, Jurand Prądzyński, Hyun-Su Lee +4
Antimicrobial peptide discovery is challenged by the astronomical size of peptide space and the relative scarcity of active peptides. While generative models provide latent maps…
Maria Emilia Russo, Federico Di Valerio, Alessia Borghini, Alessio Ragno +1
Computational approaches have become central to Protein–Protein Interaction (PPI) research, complementing experimental techniques that remain costly and incomplete. While modern…
Josh Qixuan Sun, Morteza Babaie, Wenyang hou, Mark Crowley +1
Antibody expression ranking is a critical task in antibody design, yet its modelling is severely hindered by the scarcity of labeled experimental data. To address this, we propose…
Bonjae Ku, Seeun Kim, Yubeen Kim, Hahnbeom Park +1
Proteins are inherently dynamic, with biological functions often emerging from transitions between multiple conformational states. While recent breakthroughs have largely…
Cong Liu, Milong Ren, Jiaqi Guan, Chengyue Gong +3
Recent advances in $\textit{de novo}$ protein binder design have enabled increasing experimental validation, yet reported $\textit{in silico}$ metrics remain difficult to…
Darin Tsui, Kunal Talreja, Daniel Saeedi, Amirali Aghazadeh
Protein language models (pLMs) have emerged as powerful predictors of protein structure and function. However, the computational circuits underlying their predictions remain…
Jacopo Teneggi, SM Bargeen Alam Turzo, Tanya Marwah, Alberto Bietti +3
Large language models (LLMs) are capable of emulating reasoning and using tools, creating opportunities for autonomous agents that execute complex scientific tasks. Protein design…
Panagiotis Antoniadis, Beatrice Pavesi, Simon Olsson, Ole Winther
Molecular dynamics (MD) is a central computational tool in physics, chemistry, and biology, enabling quantitative prediction of experimental observables as expectations over…
Fang Wu, Weihao Xuan, Heli Qi, Hanqun Cao +4
Deep learning in de novo protein design has achieved atomic-level fidelity. However, existing models remain largely non-deliberative: they directly synthesize molecular geometries…
Jiawei Zhang, Kangjun Jin, Shuai Xiao, Jiachen Yang
Measuring single-cell protein abundance is essential for resolving biological mechanisms and disease progression with high resolution. However, due to the high costs and antibody…
YuCheng Yuan, Yuanfeng Ji, Zhongxiao Li, Ruijiang Li
Spatial proteomics enables single-cell-resolution characterization of protein expression within tissue architecture, playing a critical role in understanding tumor…
Daiheng Zhang, Shiyang Zhang, Sizhuang He, Yangtian Zhang +2
Discrete biological sequence optimization often requires goal-directed, parser-valid edits to an existing protein or molecule. Diffusion models support iterative refinement but do…
Bowen Jing, Mihir Bafna, Anisha Parsan, Heyuan Michael Ni +4
Multistate mechanisms underlie many of the complex functions observed in natural proteins. The ability to rationally design multistate proteins would have transformative…
Jin Gao, Juntu Zhao, Zirui Zeng, Jiaqi Shen +4
AI for scientific discovery is entering an agentic era, where protein-engineering systems are expected to prioritize future wet-lab experiments rather than merely fit static…
Matteo Rossi, Ryan Pederson, Miles Wang-Henderson, Ben Kaufman +4
We present TerraBind, a foundation model for protein-ligand structure and binding affinity prediction that achieves 26$\times$ faster inference than state-of-the-art methods while…
Xinyou Wang, Liang Hong, Jiasheng Ye, Zaixiang Zheng +2
Proteins are shaped by gradual evolution under biophysical and functional constraints. Protein language models learn rich evolutionary constraints from large-scale sequences, and…
Metadata from BioTender-max/icml2026-ai-bio (CC0-1.0).