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Machine Learning and Computational Chemistry for the Endocannabinoid System.

Authors :
Atz K
Guba W
Grether U
Schneider G
Source :
Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2023; Vol. 2576, pp. 477-493.
Publication Year :
2023

Abstract

Computational methods in medicinal chemistry facilitate drug discovery and design. In particular, machine learning methodologies have recently gained increasing attention. This chapter provides a structured overview of the current state of computational chemistry and its applications for the interrogation of the endocannabinoid system (ECS), highlighting methods in structure-based drug design, virtual screening, ligand-based quantitative structure-activity relationship (QSAR) modeling, and de novo molecular design. We emphasize emerging methods in machine learning and anticipate a forecast of future opportunities of computational medicinal chemistry for the ECS.<br /> (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Details

Language :
English
ISSN :
1940-6029
Volume :
2576
Database :
MEDLINE
Journal :
Methods in molecular biology (Clifton, N.J.)
Publication Type :
Academic Journal
Accession number :
36152211
Full Text :
https://doi.org/10.1007/978-1-0716-2728-0_39