NS-Pep: De novo Peptide Design with Non-Standard Amino Acids
Center of Excellence for Smart Health（智能健康卓越中心）；Syneron Technology（Syneron技术公司）；King Abdullah University of Science and Technology (KAUST)（国王 Abdullah 科学与技术大学（KAUST））
Peptide drugs incorporating non-standard amino acids (NSAAs) offer improved binding affinity and improved pharmacological properties. However, existing peptide design methods are limited to standard amino acids, leaving NSAA-aware design largely unexplored. We introduce NS-Pep, a unified framework for co-designing peptide sequences and structures with NSAAs. The main challenge is that NSAAs are extremely underrepresented-even the most frequent one, SEP, accounts for less than 0.4% of residues-resulting in a severe long-tailed distribution. To improve generalization to rare amino acids, we propose Residue Frequency-Guided Modification (RFGM), which mitigates over-penalization through frequency-aware logit calibration, supported by both theoretical and empirical analysis. Furthermore, we identify that insufficient side-chain modeling limits geometric representation of NSAAs. To address this, we introduce Progressive Side-chain Perception (PSP) for coarse-to-fine torsion and location prediction, and Interaction-Aware Weighting (IAW) to emphasize pocket-proximal residues. Moreover, NS-Pep generalizes naturally to the peptide folding task with NSAAs, addressing a major limitation of current tools. Experiments show that NS-Pep improves sequence recovery rate and binding affinity by 6.23% and 5.12%, respectively, and outperforms AlphaFold3 by 17.76% in peptide folding success rate.
Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension
Tsinghua University（清华大学）；University of Washington（华盛顿大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；ByteDance Inc.（字节跳动公司）；Helixon Inc.（Helixon公司）
PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided 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）；Center of Computational Biology, Duke-NUS Medical School（计算生物学中心，杜克-新加坡医学学校）；Department of Bioengineering, University of Pennsylvania（生物工程系，宾夕法尼亚大学）
Full-Atom Peptide Design based on Multi-modal Flow Matching
Helixon Research（Helixon研究院）；Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）；Department of Computer Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校计算机科学系）
Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing
Cheriton School of Computer Science, University of Waterloo（滑铁卢大学计算机科学学院）；University of Waterloo（滑铁卢大学）；Department of Statistical and Actuarial Science, University of Waterloo（滑铁卢大学统计与精算科学系）；Bioinformatics Solutions Inc.（生物信息解决方案公司）
Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A
Centre for Cognitive Machines and Computational Health (CMaCH)（认知机器与计算健康中心）；School of Computer Science（计算机科学学院）；Shanghai Jiao Tong University（上海交通大学）；QuietD Biotech（QuietD生物科技）
Morphology-Aware Peptide Discovery via Masked Conditional Generative Modeling
Technical University of Munich（慕尼黑技术大学）；TUM School of Engineering and Design（慕尼黑技术大学工程与设计学院）；Department of Engineering Physics and Computation（工程物理与计算系）；Munich Data Science Institute（慕尼黑数据科学研究所）；AMC（原子模型中心）
