Enabling the hypothesis-driven prioritization of ligand candidates in big databases: Screenlamp and its application to GPCR inhibitor discovery for invasive species control

Sebastian Raschka, Anne M. Scott, Nan Liu, Santosh Gunturu, Mar Huertas, Weiming Li, Leslie A. Kuhn
<span title="2018-01-30">2018</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/eqrkonq3ofesljyzeksgdlfeb4" style="color: black;">Journal of Computer-Aided Molecular Design</a> </i> &nbsp;
While the advantage of screening vast databases of molecules to cover greater molecular diversity is often mentioned, in reality, only a few studies have been published demonstrating inhibitor discovery by screening more than a million compounds for features that mimic a known three-dimensional ligand. Two factors contribute: the general difficulty of discovering potent inhibitors, and the lack of free, userfriendly software to incorporate project-specific knowledge and user hypotheses into 3D
more &raquo; ... igand-based screening. The Screenlamp modular toolkit presented here was developed with these needs in mind. We show Screenlamp's ability to screen more than 12 million commercially available molecules and identify potent in vivo inhibitors of a G protein-coupled bile acid receptor within the first year of a discovery project. This pheromone receptor governs sea lamprey reproductive behavior, and to our knowledge, this project is the first to establish the efficacy of computational screening in discovering lead compounds for aquatic invasive species control. Significant enhancement in activity came from selecting compounds based on one of the hypotheses: that matching two distal oxygen groups in the three-dimensional structure of the pheromone is crucial for activity. Six of the 15 most active compounds met these criteria. A second hypothesis -that presence of an alkyl sulfate side chain results in high activity -identified another 6 compounds in the top 10, demonstrating the significant benefits of hypothesis-driven screening. 14. Zoete V, Daina A, Bovigny C, Michielin O (2016) SwissSimilarity: a web tool for low to ultra high throughput ligand-based virtual screening. J Chem Inf Model 56:1399-1404. 15. Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA (2006) PHASE: A new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening:
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