ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
CPL-Diff: A Diffusion Model for De Novo Design of Functional Peptide Sequences with Fixed Length.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- ProtSeqGen: a novel deep learning model for protein sequence design.BMC bioinformatics · 2026Article
- MsgaBpred: A B-cell epitope predictor integrating AlphaFold3-predicted structures with multi-scale GCNs and pre-trained language model ESM-C.PLoS computational biology · 2026Article
- Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 2026Review
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- AI-Guided Dual Strategy for Peptide Inhibitor Design Targeting Structural Polymorphs of α-Synuclein Fibrils.Cells · 2025Article
- Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules Versus Therapeutic Peptides.Biology · 2025Review
- CPL-Diff: A Diffusion Model for De Novo Design of Functional Peptide Sequences with Fixed Length.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Peptides are recognized as next-generation therapeutic drugs due to their unique properties and are essential for treating human diseases. In recent years, a number of deep generation models for generating peptides have been proposed and have shown great potential. However, these models cannot well control the length of the generated sequence, while the sequence length has a very important impact on the physical and chemical properties and therapeutic effects of peptides. Here, a diffusion model is introduced, capable of controlling the length of generated functional peptide sequences, named CPL-Diff. CPL-Diff can control the length of generated polypeptide sequences using only attention masking. Additionally, CPL-Diff can generate single-functional polypeptide sequences based on given conditional information. Experiments demonstrate that the peptides generated by CPL-Diff exhibit lower perplexity and similarity compared to those produced by the current state-of-the-art models, and further exhibit relevant physicochemical properties similar to real sequences. The interpretability analysis is also performed on CPL-Diff to understand how it controls the length of generated sequences and the decision-making process involved in generating polypeptide sequences, with the aim of providing important theoretical guidance for polypeptide design. The code for CPL-Diff is available at https://github.com/luozhenjie1997/CPL-Diff.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.