Cheminformatic and assay-performance profiling of small-molecule screening collections

Clemons, Paul A .

Title:

Cheminformatic and assay-performance profiling of small-molecule screening collections

dc.contributor.author	Clemons, Paul A .	en_US
dc.contributor.corporatename	Broad Institute of Harvard and MIT	en_US
dc.date.accessioned	2011-05-27T16:06:46Z
dc.date.available	2011-05-27T16:06:46Z
dc.date.issued	2011-05-17
dc.description	Paul Clemons, Director of Computational Chemical Biology Research in the Chemical Biology Program at the Broad Institute presented a lecture on May 17, 2011 from 11:00 AM to 12:00 PM in the Klaus Advanced Computing Building, Room 1116E.	en_US
dc.description	Runtime: 60:54 minutes.	en_US
dc.description.abstract	Quantitative decisions about properties and behavior of compound sets are important in building screening collections for smallmolecule probes and drugs. Decisions about individual compounds typically dominate such discussions: individual compounds pass or fail filtering rules, individual compounds hit or not in assays, etc. In this presentation, we focus on analyses directed at sets of compounds rather than individual members. We start with bioinformatic analysis of natural product and drug targets that motivates the need for new sources of synthetic small molecules. Next, we use sets of molecules from 3 sources (commercial, natural, academic) to show that different computed chemical properties (cheminformatic profiles) provide different chemical intuition about diversity of compound sets, and how quantifying these relationships can provide guidance to synthetic chemists. In the second part, we show that arrays of biological performance measurements (assay-performance profiles) can be used, instead of chemical structure, as a basis for small-molecule similarity, with implications for target identification and lead hopping. To illustrate connections between computed and measured properties, we describe a structured small-molecule profiling experiment in which 15,000 compounds were exposed to 100 different protein-binding assays. We show how different computed molecular complexity and shape descriptors accord with specificity of performance in protein-binding assays. Finally, using the same dataset, we introduce a measure of assay-performance diversity based on information entropy, and show how it might be used to judge relationships between computed properties and performance diversity of compound collections.	en_US
dc.format.extent	60:54 minutes
dc.identifier.uri	http://hdl.handle.net/1853/39023
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.subject	Cheminformatics	en_US
dc.subject	Drug discovery	en_US
dc.subject	Small-molecue probes	en_US
dc.subject	Small-molecule profiling	en_US
dc.title	Cheminformatic and assay-performance profiling of small-molecule screening collections	en_US
dc.type	Moving Image
dc.type.genre	Lecture
dspace.entity.type	Publication
local.contributor.corporatename	College of Sciences
local.contributor.corporatename	School of Biological Sciences
local.contributor.corporatename	Center for the Study of Systems Biology
local.relation.ispartofseries	Distinguished Lecture Series in Systems Biology
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