Cheminformatics aspects of high throughput screening: from robots to models: symposium summary
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The “Cheminformatics aspects of high throughput screening (HTS): from robots to models” symposium was part of the computers in chemistry technical program at the American Chemical Society National Meeting in Denver, Colorado during the fall of 2011. This symposium brought together researchers from high throughput screening centers and molecular modelers from academia and industry to discuss the integration of currently available high throughput screening data and assays with computational analysis. The topics discussed at this symposium covered the data-infrastructure at various academic, hospital, and National Institutes of Health-funded high throughput screening centers, the cheminformatics and molecular modeling methods used in real world examples to guide screening and hit-finding, and how academic and non-profit organizations can benefit from current high throughput screening cheminformatics resources. Specifically, this article also covers the remarks and discussions in the open panel discussion of the symposium and summarizes the following talks on “Accurate Kinase virtual screening: biochemical, cellular and selectivity”, “Selective, privileged and promiscuous chemical patterns in high-throughput screening” and “Visualizing and exploring relationships among HTS hits using network graphs”.
KeywordsCheminformatics High throughput screening Molecular modeling Data-infrastructure
Y. Jane Tseng would like to acknowledge the travel funding from the Taiwan National Science Council, grant numbers 100-2627-B-002-016, and 100-2325-B-009-001-. Eric Martin would like to acknowledge David Sullivan, Prasenjit Mukherjee, and Li Tian for their work developing protein-family virtual screening and applying it to dozens of projects. Cristian Bologa would like to acknowledge Jeremy J Yang for the implementation of the scaffold extraction algorithm, and the development of the BADAPPLE system; and Oleg Ursu for the implementation, analysis of results, and preparing the slides for the molecular matching pairs part of the presentation. Work on the identification of promiscuous patterns has been supported by the NIH U54MH084690 and NCRR P20 RR016480 grants. Anang Shelat would like to acknowledge Armand Guiguemde and David Smithson for their contributions to the network graph algorithm, and Cindy Nelson and Heather Ross for their work as part of the Compound Management group at SJCRH.
- 19.Guiguemde W, Shelat A, Bouck D, Duffy S, Crowther G, Davis P, Smithson D, Connelly M, Clark J, Zhu F, Jiménez-Díaz M, Martinez M, Wilson E, Tripathi A, Gut J, Sharlow E, Bathurst I, El Mazouni F, Fowble J, Forquer I, McGinley P, Castro S, Angulo-Barturen I, Ferrer S, Rosenthal P, Derisi J, Sullivan D, Lazo J, Roos D, Riscoe M, Phillips M, Rathod P, Van Voorhis W, Avery V, Guy R (2010) Nature 465(7296):311CrossRefGoogle Scholar
- 20.Bemis GW, Murcko MA (1996) J Med Chem 39(15):2887Google Scholar
- 21.R: A Language and Environment for Statistical Computing (2013) R Core Team, R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org
- 24.Structure and principles Accessed, http://www.whocc.no/atc/structure_and_principles/
- 25.Smoot M, Ono K, Ruscheinski J, Wang P-L, Ideker T (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics (Oxford, England) 27(3):431–432. doi: 10.1093/bioinformatics/btq675