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Machine Learning and Computational Chemistry for the Endocannabinoid System

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Endocannabinoid Signaling

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2576))

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.

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Acknowledgments

This research was supported by the Swiss National Science Foundation (SNSF, grant no. 205321 182176) and the ETH RETHINK initiative.

Conflict of Interest

G.S. is a cofounder of inSili.com LLC, Zurich, and a consultant to the pharmaceutical industry. W.G. and U.G. are employees of F. Hoffmann-La Roche Ltd.

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Author notes

  1. Equally senior authors: Uwe Grether and Gisbert Schneider.

Authors and Affiliations

  1. ETH Zurich, Department of Chemistry and Applied Biosciences, Zurich, Switzerland

    Kenneth Atz & Gisbert Schneider

  2. Roche Pharma Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland

    Wolfgang Guba & Uwe Grether

  3. ETH Singapore SEC Ltd, Singapore, Singapore

    Gisbert Schneider

Authors
  1. Kenneth Atz
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  2. Wolfgang Guba
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  3. Uwe Grether
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  4. Gisbert Schneider
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Corresponding author

Correspondence to Uwe Grether .

Editor information

Editors and Affiliations

  1. Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, L’Aquila, Italy

    Mauro Maccarrone

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Atz, K., Guba, W., Grether, U., Schneider, G. (2023). Machine Learning and Computational Chemistry for the Endocannabinoid System. In: Maccarrone, M. (eds) Endocannabinoid Signaling. Methods in Molecular Biology, vol 2576. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2728-0_39

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  • DOI: https://doi.org/10.1007/978-1-0716-2728-0_39

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2727-3

  • Online ISBN: 978-1-0716-2728-0

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