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Authors: Leo Ghemtio ; Malika Smaïl-Tabbone ; Marie-Dominique Devignes ; Michel Souchet and Bernard Maigret

Affiliation: Nancy-Université and INRIA Research Centre Nancy Grand-Est, France

ISBN: 978-989-674-011-5

Keyword(s): KDD, Heterogeneous data integration, Data retrieval, Data mining, Protein-ligand interaction, 3D structure, Virtual screening.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; BioInformatics & Pattern Discovery ; Computational Intelligence ; Evolutionary Computing ; Integration of Data Warehousing and Data Mining ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: Virtual screening has become an essential step in the early drug discovery process. Generally speaking, it consists in using computational techniques for selecting compounds from chemical libraries in order to identify drug-like molecules acting on a biological target of therapeutic interest. In the present study we consider virtual screening as a particular form of the KDD (Knowledge Discovery from Databases) approach. The knowledge to be discovered concerns the way a compound can be considered as a consistent ligand for a given target. The data from which this knowledge has to be discovered derive from diverse sources such as chemical, structural, and biological data related to ligands and their cognate targets. More precisely, we aim to extract filters from chemical libraries and protein-ligand interactions. In this context, the three basic steps of a KDD process have to be implemented. Firstly, a model-driven data integration step is applied to appropriate heterogeneous data found in public databases. This facilitates subsequent extraction of various datasets for mining. In a second step, mining algorithms are applied to the datasets and finally the most accurate knowledge units are eventually proposed as new filters. We present here this KDD approach and the experimental results we obtained with a set of ligands of the hormone receptor LXR. (More)

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Paper citation in several formats:
Ghemtio L.; Smaïl-Tabbone M.; Devignes M.; Souchet M.; Maigret B. and (2009). A KDD APPROACH FOR DESIGNING FILTERING STRATEGIES TO IMPROVE VIRTUAL SCREENING.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009) ISBN 978-989-674-011-5, pages 146-151. DOI: 10.5220/0002292301460151

@conference{kdir09,
author={Leo Ghemtio and Malika Smaïl{-}Tabbone and Marie{-}Dominique Devignes and Michel Souchet and Bernard Maigret},
title={A KDD APPROACH FOR DESIGNING FILTERING STRATEGIES TO IMPROVE VIRTUAL SCREENING},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)},
year={2009},
pages={146-151},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002292301460151},
isbn={978-989-674-011-5},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)
TI - A KDD APPROACH FOR DESIGNING FILTERING STRATEGIES TO IMPROVE VIRTUAL SCREENING
SN - 978-989-674-011-5
AU - Ghemtio, L.
AU - Smaïl-Tabbone, M.
AU - Devignes, M.
AU - Souchet, M.
AU - Maigret, B.
PY - 2009
SP - 146
EP - 151
DO - 10.5220/0002292301460151

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