Development and application of distributed computing tools for virtual screening of large compound libraries

Yogesh Aher
<span title="">2012</span> <span class="release-stage">unpublished</span>
In the current drug discovery process, the identification of new target proteins and potential ligands is very tedious, expensive and time-consuming. Thus, use of in silico techniques is of utmost importance and proved to be a valuable strategy in detecting complex structural and bioactivity relationships. Increased demands of computational power for tremendous calculations in scientific fields and timely analysis of generated piles of data require innovative strategies for efficient
more &raquo; ... of distributed computing resources in the form of computational grids. Such grids add a new aspect to the emerging information technology paradigm by providing and coordinating the heterogeneous resources such as various organizations, people, computing, storage and networking facilities as well as data, knowledge, software and workflows. The aim of this study was to develop a university-wide applicable grid infrastructure, UVieCo (University of Vienna Condor pool) which can be used for implementation of standard structure- and ligand-based drug discovery applications using freely available academic software. Firewall and security issues were resolved with a virtual private network setup whereas virtualization of computer hardware was done using the CoLinux concept in a way to run Linux-executable jobs inside Windows machines. The effectiveness of the grid was assessed by performance measurement experiments using sequential and parallel tasks. Subsequently, the association of expression/sensitivity profiles of ABC transporters with activity profiles of anticancer compounds was analyzed by mining the data from NCI (National Cancer Institute). The datasets generated in this analysis were utilized with ligand-based computational methods such as shape similarity and classification algorithms to identify and separate P-gp substrates from non-substrates. While developing predictive classification models, the problem of imbalanced class distribution was proficiently addressed using the cost-sensitive bagging approach. Applicabil [...]
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.25365/thesis.23872</a> <a target="_blank" rel="external noopener" href="">fatcat:5jnf2ijkbzbf3htwh5avtm22ju</a> </span>
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