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Combining Structure‐Based Pharmacophore and In Silico Approaches to Discover Novel Selective Serotonin Reuptake Inhibitors
Author(s) -
Zhou ZhengLi,
Liu HsuanLiang,
Wu Josephine W.,
Tsao ChengWen,
Chen WeiHsi,
Liu KungTien,
Ho Yih
Publication year - 2013
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/cbdd.12192
Subject(s) - pharmacophore , virtual screening , in silico , chemistry , docking (animal) , serotonin transporter , binding affinities , computational biology , reuptake inhibitor , paroxetine , serotonin plasma membrane transport proteins , stereochemistry , combinatorial chemistry , biochemistry , serotonin , biology , gene , medicine , receptor , nursing
Inhibition of human serotonin transporter (h SERT ) has been reported to be a potent strategy for the treatment for depression. To discover novel selective serotonin reuptake inhibitors ( SSRI s), a structure‐based pharmacophore model ( SBPM ) was developed using the docked conformations of six highly active SSRI s. The best SBPM , consisting of four chemical features: two ring aromatics ( RA s), one hydrophobic ( HY ), and one positive ionizable ( PI ), was further validated using G unner‐ H enry ( GH ) scoring and receiver operating characteristic ( ROC ) curve methods. This well‐validated SBPM was then used as a 3 D ‐query in virtual screening to identify potential hits from N ational C ancer I nstitute ( NCI ) database. These hits were subsequently filtered by absorption, distribution, metabolism, excretion, and toxicity ( ADMET ) prediction and molecular docking, and their binding stabilities were validated by 20‐ns MD simulations. Finally, only two compounds ( NSC 175176 and NSC 705841) were identified as potential leads, which exhibited higher binding affinities in comparison with the paroxetine. Our results also suggest that cation–π interaction plays a crucial role in stabilizing the h SERT ‐inhibitor complex. To our knowledge, the present work is the first structure‐based virtual screening study for new SSRI discovery, which should be a useful guide for the rapid identification of novel therapeutic agents from chemical database.