Identification of dual active agents targeting 5-HT1A and SERT by combinatorial virtual screening methods.
Selective serotonin reuptake inhibitors (SSRIs) are most adopted therapeutics marketed for major depression, and the efficacy of which are greatly reduced by their delayed onset of action and undesirable side effects. 5-HT1A receptor partial agonist and SERT inhibitor (SPARI) was proposed as a novel strategy to overcome the shortage of efficacy by a negative feedback control of 5-HT1A receptor. However, only one SPARI (vilazodone) has been approved for clinical use, and none is currently in clinical trial, which demonstrates a strong need for searching more novel SPARIs to facilitate antidepressants discovery. This work applied a combinatorial virtual screening method (CVSM) by integrating multiple tools. Statistic analysis reveals that CVSM surpasses single virtual screening methods in terms of hit rates and enrichment factors. By adopting optimized CVSM, 91 promising dual target leads form 15 scaffolds were identified, and 40% of these scaffolds have already been reported to show antidepressant related therapeutic effects. In sum, CVSM is capable in identifying novel SPARIs from large chemical libraries with extremely low false hit rate.
['Wang|Panpan|P|', 'Yang|Fengyuan|F|', 'Yang|Hong|H|', 'Xu|Xiaofei|X|', 'Liu|Duo|D|', 'Xue|Weiwei|W|', 'Zhu|Feng|F|']
D000928:Antidepressive Agents D015195:Drug Design D006801:Humans D062105:Molecular Docking Simulation D044282:Receptor, Serotonin, 5-HT1A D058825:Serotonin 5-HT1 Receptor Agonists D017367:Serotonin Uptake Inhibitors
Virtual screening SERT inhibitor 5-HT1A receptor agonist molecular docking support vector machines