Unravelling cyclic peptide membrane permeability prediction: a study on data augmentation, architecture choices, and representation schemes
A machine learning approach integrating cyclic structure modeling and data augmentation improves cyclic peptide membrane permeability prediction. The best model is deployed in CYCLOPS, a web tool for rapid permeability assessment in drug discovery.
Authors
- Alfonso Cabezón (ORCID: https://orcid.org/0000-0003-2777-3217)
- Erik Otović (ORCID: https://orcid.org/0000-0001-5713-5879)
- Ángel Piñeiro (ORCID: https://orcid.org/0000-0001-9350-8468)
- Goran Mauša (ORCID: https://orcid.org/0000-0002-0643-4577)
- Daniela Kalafatović (ORCID: https://orcid.org/0000-0002-9685-1162)
Institutions
- University of Rijeka (HR)
- Universidade de Santiago de Compostela (ES)
- Center for Research in Molecular Medicine and Chronic Diseases (ES)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d4dd00375f
- Citations
- 4
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 3.22
Funders
- European Commission
- Hrvatska Zaklada za Znanost
- Xunta de Galicia
- European Regional Development Fund
- Agencia Estatal de Investigación