Publikation: Molecular Fragment Mining for Drug Discovery
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GODO, Lluís, ed.. Symbolic and Quantitative Approaches to Reasoning with Uncertainty. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005, pp. 1002-1013. Lecture Notes in Computer Science. 3571. ISBN 978-3-540-27326-4. Available under: doi: 10.1007/11518655_84
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The main task of drug discovery is to find novel bioactive molecules, i.e., chemical compounds that, for example, protect human cells against a virus. One way to support solving this task is to analyze a database of known and tested molecules in order to find structural properties of molecules that determine whether a molecule will be active or inactive, so that future chemical tests can be focused on the most promising candidates. A promising approach to this task was presented in [2]: an algorithm for finding molecular fragments that discriminate between active and inactive molecules. In this paper we review this approach as well as two extensions: a special treatment of rings and a method to find fragments with wildcards based on chemical expert knowledge.
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BORGELT, Christian, Michael R. BERTHOLD, David E. PATTERSON, 2005. Molecular Fragment Mining for Drug Discovery. In: GODO, Lluís, ed.. Symbolic and Quantitative Approaches to Reasoning with Uncertainty. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005, pp. 1002-1013. Lecture Notes in Computer Science. 3571. ISBN 978-3-540-27326-4. Available under: doi: 10.1007/11518655_84BibTex
@inproceedings{Borgelt2005Molec-24073,
year={2005},
doi={10.1007/11518655_84},
title={Molecular Fragment Mining for Drug Discovery},
number={3571},
isbn={978-3-540-27326-4},
publisher={Springer Berlin Heidelberg},
address={Berlin, Heidelberg},
series={Lecture Notes in Computer Science},
booktitle={Symbolic and Quantitative Approaches to Reasoning with Uncertainty},
pages={1002--1013},
editor={Godo, Lluís},
author={Borgelt, Christian and Berthold, Michael R. and Patterson, David E.}
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