Lipophilicity prediction of peptides and peptide derivatives by consensus machine learning
| Autor: | Gisbert Schneider, Jan A. Hiss, Francesca Grisoni, Jens A. Fuchs, Michael Kossenjans |
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| Přispěvatelé: | Fuchs, J, Grisoni, F, Kossenjans, M, Hiss, J, Schneider, G |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
In silico Pharmaceutical Science Peptide Machine learning computer.software_genre Biochemistry Synthetic drugs 03 medical and health sciences CHIM/01 - CHIMICA ANALITICA Drug Discovery Machine learning lipophilicity prediction Model development Pharmacology chemistry.chemical_classification Genetically engineered business.industry Organic Chemistry Small molecule 3. Good health Chemistry 030104 developmental biology chemistry Lipophilicity Molecular Medicine Artificial intelligence business computer |
| Zdroj: | MedChemComm, 9 (9) |
| ISSN: | 2040-2511 2040-2503 |
| DOI: | 10.1039/c8md00370j |
| Popis: | Lipophilicity prediction is routinely applied to small molecules and presents a working alternative to experimental log P or log D determination. For compounds outside the domain of classical medicinal chemistry these predictions lack accuracy, advocating the development of bespoke in silico approaches. Peptides and their derivatives and mimetics fill the structural gap between small synthetic drugs and genetically engineered macromolecules. Here, we present a data-driven machine learning method for peptide log D(7.4) prediction. A model for estimating the lipophilicity of short linear peptides consisting of natural amino acids was developed. In a prospective test, we obtained accurate predictions for a set of newly synthesized linear tri- to hexapeptides. Further model development focused on more complex peptide mimetics from the AstraZeneca compound collection. The results obtained demonstrate the applicability of the new prediction model to peptides and peptide derivatives in a log D(7.4) range of approximately –3 to 5, with superior accuracy to established lipophilicity models for small molecules. |
| Databáze: | OpenAIRE |
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