ArticleJournal of chemical information and modeling2025
Peptide-Aware Chemical Language Model Successfully Predicts Membrane Diffusion of Cyclic Peptides.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A decoupled alignment kernel for peptide membrane permeability predictions.Journal of cheminformatics · 2026Article
- HELM-BERT: Topology-Aware Representations for Chemically Modified Peptides.Journal of chemical information and modeling · 2026Article
- PeptiVerse: A unified platform for therapeutic peptide property prediction.Nature communications · 2026Article
- Cyclic Peptides as Modulators of Protein-Protein Interactions: A Survival Guide from Discovery Platforms to AI-Driven Design.International journal of molecular sciences · 2026Review
- Scaling SMILES-based chemical language models for therapeutic peptide engineering.bioRxiv : the preprint server for biology · 2026Article
- Predictive machine learning models for rational permeability design in de novo macrocycle engineering: a review.Journal of cheminformatics · 2026Review
- Entropy-based byte patching transformer for self-supervised pretraining of SMILES data.iScience · 2026Article
- PeptiVerse: A Unified Platform for Therapeutic Peptide Property Prediction.bioRxiv : the preprint server for biology · 2026Article
- Optimizing SMILES token sequences via trie-based refinement and transition graph filtering.Journal of cheminformatics · 2026Article
- How to build machine learning models able to extrapolate from standard to modified peptides.Journal of cheminformatics · 2025Article
- C2PO: an ML-powered optimizer of the membrane permeability of cyclic peptides through chemical modification.Journal of cheminformatics · 2025Article
- Article
- LassoESM a tailored language model for enhanced lasso peptide property prediction.Nature communications · 2025Article
- PepTune:ArXiv · 2025Article
- Advances of deep Neural Networks (DNNs) in the development of peptide drugs.Future medicinal chemistry · 2025Review
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2 authors.
Funding
Abstract
Language modeling applied to biological data has significantly advanced the prediction of membrane penetration for small-molecule drugs and natural peptides. However, accurately predicting membrane diffusion for peptides with pharmacologically relevant modifications remains a substantial challenge. Here, we introduce PeptideCLM, a peptide-focused chemical language model capable of encoding peptides with chemical modifications, unnatural or noncanonical amino acids, and cyclizations. We assess this model by predicting membrane diffusion of cyclic peptides, demonstrating greater predictive power than existing chemical language models. Our model is versatile and can be extended beyond membrane diffusion predictions to other target values. Its advantages include the ability to model macromolecules using chemical string notation, a largely unexplored domain, and a simple, flexible architecture that allows for adaptation to any peptide or other macromolecule data set.
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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 OpenQuestion graph.