Novel Aspects of Molecular Targets for Antidepressant Drugs
A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Medicinal Chemistry".
Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 6279
Special Issue Editor
Interests: antidepressants; drug discovery based artificial intelligence; epigenetics of depression; genetics of psychiatric disorder; neuropharmacology
Special Issue Information
Dear Colleagues,
Major depressive disorder (MDD) is the third leading cause of disability worldwide and can have a tremendous impact on quality of life. Even with successful treatment, 80 percent of patients relapse in the five years following remission. More than 30 percent fail to respond to at least two antidepressant treatments and are diagnosed with treatment-resistant depression. For half a century, clinical antidepressants have acted with therapeutic effect by regulating monoamine transmitters, inhibiting the reuptake of 5- HT and NE, and increasing the levels of 5- HT, NE, or DA in the presynaptic membrane. Nevertheless, these drugs have some problems, such as the long onset of action, suboptimal efficacy, and work for all types of patients. In recent years, rapid-acting antidepressants, represented by ketamine, have received much attention. The possible mechanism is related to blocking NMDA receptors and activating AMPA receptors. This research topic investigates glutamatergic receptors such as NMDA, APMA, and GluR and the related fast-acting antidepressant mechanisms. The related contents mainly include the following three aspects: (1) use of artificial intelligence and computational design approaches to discover powerful fast-acting compounds that bind to the glutamate receptor; (2) novel approaches, including CRISPR/cas9 and multi-omics methods, have been used to explore the biological mechanism; and (3) new multi-target antidepressants (including melatonin receptor agonists, GABA receptor modulators, etc.), medicinal plants and their extracts.
Dr. Qingzhong Wang
Guest Editor
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