An approach combining deep learning and molecule docking for drug discovery of cathepsin L.
Expert Opin Drug Discov
; 18(3): 347-356, 2023 03.
Artículo
en Inglés
| MEDLINE | ID: covidwho-2268789
ABSTRACT
OBJECTIVES:
Cathepsin L (CTSL) is a promising therapeutic target for metabolic disorders and COVID-19. However, there are still no clinically available CTSL inhibitors. Our objective is to develop an approach for the discovery of potential reversible covalent CTSL inhibitors.METHODS:
The authors combined Chemprop, a deep learning-based strategy, and the Schrödinger CovDock algorithm to identify potential CTSL inhibitors. First, they used Chemprop to train a deep learning model capable of predicting whether a molecule would inhibit the activity of CTSL and performed predictions on ZINC20 in-stock librarie (~9.2 million molecules). Then, they selected the top-200 predicted molecules and performed the Schrödinger covalent docking algorithm to explore the binding patterns to CTSL (PDB 5MQY). The authors then calculated the binding energies using Prime MM/GBSA and examined the stability between the best two molecules and CTSL using 100ns molecular dynamics simulations.RESULTS:
The authors found five molecules that showed better docking results than the well-known cathepsin inhibitor odanacatib. Notably, two of these molecules, ZINC-35287427 and ZINC-1857528743, showed better docking results with CTSL compared to other cathepsins.CONCLUSION:
Our approach enables drug discovery from large-scale databases with little computational consumption, which will save the cost and time required for drug discovery.Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Aprendizaje Profundo
/
COVID-19
Tipo de estudio:
Estudio pronóstico
Límite:
Humanos
Idioma:
Inglés
Revista:
Expert Opin Drug Discov
Año:
2023
Tipo del documento:
Artículo
País de afiliación:
17460441.2023.2174522
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