3D-ALMOND-QSAR Models to Predict the Antidepressant Effect of Some Natural Compounds.

ABSTRACT: The current treatment of depression involves antidepressant synthetic drugs that have a variety of side effects. In searching for alternatives, natural compounds could represent a solution, as many studies reported that such compounds modulate the nervous system and exhibit antidepressant effects. We used bioinformatics methods to predict the antidepressant effect of ten natural compounds with neuroleptic activity, reported in the literature. For all compounds we computed their drug-likeness, absorption, distribution, metabolism, excretion (ADME), and toxicity profiles. Their antidepressant and neuroleptic activities were predicted by 3D-ALMOND-QSAR models built by considering three important targets, namely serotonin transporter (SERT), 5-hydroxytryptamine receptor 1A (5-HT1A), and dopami

SUBMITTER: Avram S 

PROVIDER: S-EPMC8470101 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature