Affiliations: [a] Institute of Mathematics and Computer Science, University of Armed Forces Munich, Werner-Heisenberg-Weg 39, 85577 Neubiberg, Germany. E-mail: heiko.hofer@unibw-muenchen.de | [b] School of Computer Science, Otto-von-Guericke-University of Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany. E-mail: borgelt@iws.cs.uni-magdeburg.de | [c] Department of Computer and Information Science, University of Konstanz, Universitätsstraße 10, 78457 Konstanz, Germany. E-mail: berthold@inf.uni-konstanz.de
Abstract: 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.
