ArticleBriefings in bioinformatics2024
CycPeptMP: enhancing membrane permeability prediction of cyclic peptides with multi-level molecular features and data augmentation.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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The trial behind it
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Who cites it
17 citing papers in PubMed.
- Flavonoids fromJournal of enzyme inhibition and medicinal chemistry · 2026Article
- Similarity-Enhanced Representation Learning of Non-Canonical Amino Acids for Therapeutic Peptide Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Cyclic Peptides in Modern Drug Discovery: Trends and Therapeutic Directions.Journal of medicinal chemistry · 2026Review
- Article
- Multiscale Modeling of the Bacterial Ribosome to Identify Potential Peptide Modulators and Their Allosteric Effects.Biochemistry · 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
- Predictive machine learning models for rational permeability design in de novo macrocycle engineering: a review.Journal of cheminformatics · 2026Review
- Linker Engineering in Stapled Peptides for Enhanced Membrane Permeability: Screening and Optimization Strategies.International journal of molecular sciences · 2026Review
- CycloPepper: a machine learning platform for predicting cyclization outcomes and optimizing synthesis of therapeutic cyclopeptides.Nature communications · 2026Article
- Enhancing the Predictive Power of Macrocyclic Drug Permeability by Knowledge Distillation from Analogous Pretraining Data.Journal of medicinal chemistry · 2026Article
- Overcoming Oral Cavity Barriers for Peptide Delivery Using Advanced Pharmaceutical Techniques and Nano-Formulation Platforms.Biomedicines · 2025Review
- MultiCycPermea: accurate and interpretable prediction of cyclic peptide permeability using a multimodal image-sequence model.BMC biology · 2025Article
- Peptide-Aware Chemical Language Model Successfully Predicts Membrane Diffusion of Cyclic Peptides.Journal of chemical information and modeling · 2025Article
- Cyclic peptide membrane permeability prediction using deep learning model based on molecular attention transformer.Frontiers in bioinformatics · 2025Article
- MSF-CPMP: A novel multi-source feature fusion model for prediction of cyclic peptide membrane permeability.Computational and structural biotechnology journal · 2025Article
- Overcoming Challenges in Small-Molecule Drug Bioavailability: A Review of Key Factors and Approaches.International journal of molecular sciences · 2024Review
- Peptide-aware chemical language model successfully predicts membrane diffusion of cyclic peptides.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Cyclic peptides are versatile therapeutic agents that boast high binding affinity, minimal toxicity, and the potential to engage challenging protein targets. However, the pharmaceutical utility of cyclic peptides is limited by their low membrane permeability-an essential indicator of oral bioavailability and intracellular targeting. Current machine learning-based models of cyclic peptide permeability show variable performance owing to the limitations of experimental data. Furthermore, these methods use features derived from the whole molecule that have traditionally been used to predict small molecules and ignore the unique structural properties of cyclic peptides. This study presents CycPeptMP: an accurate and efficient method to predict cyclic peptide membrane permeability. We designed features for cyclic peptides at the atom-, monomer-, and peptide-levels and seamlessly integrated these into a fusion model using deep learning technology. Additionally, we applied various data augmentation techniques to enhance model training efficiency using the latest data. The fusion model exhibited excellent prediction performance for the logarithm of permeability, with a mean absolute error of $0.355$ and correlation coefficient of $0.883$. Ablation studies demonstrated that all feature levels contributed and were relatively essential to predicting membrane permeability, confirming the effectiveness of augmentation to improve prediction accuracy. A comparison with a molecular dynamics-based method showed that CycPeptMP accurately predicted peptide permeability, which is otherwise difficult to predict using simulations.
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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 OpenQuestion graph.