Publikation: Large Scale Mining of Molecular Fragments with Wildcards
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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 with the aim to build a classifier that predicts whether a novel molecule will be active or inactive, so that future chemical tests can be focused on the most promising candidates. In [1] an algorithm for constructing such a classifier was proposed that uses molecular fragments to discriminate between active and inactive molecules. In this paper we present two extensions of this approach: A special treatment of rings and a method that finds fragments with wildcards based on chemical expert knowledge.
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HOFER, Heiko, Christian BORGELT, Michael R. BERTHOLD, 2003. Large Scale Mining of Molecular Fragments with Wildcards. In: R. BERTHOLD, Michael, ed., Hans-Joachim LENZ, ed., Elizabeth BRADLEY, ed., Rudolf KRUSE, ed., Christian BORGELT, ed.. Advances in Intelligent Data Analysis V. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003, pp. 376-385. Lecture Notes in Computer Science. 2810. ISBN 978-3-540-40813-0. Available under: doi: 10.1007/978-3-540-45231-7_35BibTex
@inproceedings{Hofer2003Large-5554, year={2003}, doi={10.1007/978-3-540-45231-7_35}, title={Large Scale Mining of Molecular Fragments with Wildcards}, number={2810}, isbn={978-3-540-40813-0}, publisher={Springer Berlin Heidelberg}, address={Berlin, Heidelberg}, series={Lecture Notes in Computer Science}, booktitle={Advances in Intelligent Data Analysis V}, pages={376--385}, editor={R. Berthold, Michael and Lenz, Hans-Joachim and Bradley, Elizabeth and Kruse, Rudolf and Borgelt, Christian}, author={Hofer, Heiko and Borgelt, Christian and Berthold, Michael R.} }
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