CIRCE: Web-Based Platform for the Prediction of Cannabinoid Receptor Ligands Using Explainable Machine Learning-Reference-Cited by-同舟云学术

CIRCE: Web-Based Platform for the Prediction of Cannabinoid Receptor Ligands Using Explainable Machine Learning

Published:2023-09-07 Issue:18 Volume:63 Page:5916-5926
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Gambacorta Nicola¹²,Ciriaco Fulvio³,Amoroso Nicola¹,Altomare Cosimo Damiano¹,Bajorath Jürgen²^ORCID,Nicolotti Orazio¹^ORCID

Affiliation:

1. Dipartimento di Farmacia Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Via E. Orabona, 4, I-70125 Bari, Italy

2. Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, D-53115 Bonn, Germany

3. Dipartimento di Chimica, Università degli Studi di Bari “Aldo Moro”, Via E. Orabona, 4, I-70125 Bari, Italy

Funder

Universita degli Studi di Bari Aldo Moro

HORIZON EUROPE Excellent Science

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.3c00914

Reference57 articles.

1. Recent Advances on Type-2 Cannabinoid (CB2) Receptor Agonists and their Therapeutic Potential

2. Assay of CB1 Receptor Binding

3. Cannabinoid Receptor 1 Blockade Attenuates Obesity and Adipose Tissue Type 1 Inflammation Through miR-30e-5p Regulation of Delta-Like-4 in Macrophages and Consequently Downregulation of Th1 Cells

4. Role of Cannabinoids in Obesity

5. Targeting the endocannabinoid/CB1 receptor system for treating obesity in Prader–Willi syndrome

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