Machine learning vs. field 3D-QSAR models for serotonin 2A receptor psychoactive substances identification-Reference-Cited by-同舟云学术

Machine learning vs. field 3D-QSAR models for serotonin 2A receptor psychoactive substances identification

Published:2021 Issue:24 Volume:11 Page:14587-14595
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Floresta Giuseppe¹²³⁴^ORCID,Abbate Vincenzo¹²³⁴^ORCID

Affiliation:

1. Department of Analytical, Environmental and Forensic Sciences

2. King's College London

3. London

4. UK

Abstract

Five QSAR models for predicting the affinity of 5-HT2AR ligands have been developed. The resulting models generate a useful tool for the investigation and identification of unclassified new psychoactive substances (NPS).

Funder

European Commission

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/RA/D1RA01335A

Reference53 articles.

1. Primary Structure of the Human Platelet Serotonin 5-HT2A Receptor: Identity with Frontal Cortex Serotonin 5-HT2A Receptor

2. The neuropharmacology of sleep paralysis hallucinations: serotonin 2A activation and a novel therapeutic drug

3. Mirtazapine-induced arthralgia

4. Role of the Serotonin 5-HT2A Receptor in Learning

5. Serotonin 5-HT2A Receptor Activation Blocks TNF-α Mediated Inflammation In Vivo

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