Learned Feature Generation for Molecules

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DUIVESTEIJN, Wouter, ed., Arno SIEBES, ed., Antti UKKONEN, ed.. Advances in Intelligent Data Analysis XVII : 17th International Symposium, IDA 2018, ’s-Hertogenbosch, The Netherlands, October 24-26, 2018, proceedings. Cham: Springer, 2018, pp. 380-391. Lecture Notes in Computer Science. 11191. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-030-01767-5. Available under: doi: 10.1007/978-3-030-01768-2_31
Zusammenfassung

When classifying molecules for virtual screening, the molecular structure first needs to be converted into meaningful features, before a classifier can be trained. The most common methods use a static algorithm that has been created based on domain knowledge to perform this generation of features. We propose an approach where this conversion is learned by convolutional neural network finding features that are useful for teh task at hand based on the available data. Preliminary results indicate that our current approach can already come up with fetaures that perform similarly well as common methods. Since this approach does not jet use any chemiocal properties, results could be improved in future versions

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004 Informatik
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Convolutional neural networks; Feature generation; Molecular fetaures; Virtual screening
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17th International Symposium, IDA 2018, 24. Okt. 2018 - 26. Okt. 2018, ’s-Hertogenbosch, The Netherlands
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ISO 690WINTER, Patrick, Christian BORGELT, Michael R. BERTHOLD, 2018. Learned Feature Generation for Molecules. 17th International Symposium, IDA 2018. ’s-Hertogenbosch, The Netherlands, 24. Okt. 2018 - 26. Okt. 2018. In: DUIVESTEIJN, Wouter, ed., Arno SIEBES, ed., Antti UKKONEN, ed.. Advances in Intelligent Data Analysis XVII : 17th International Symposium, IDA 2018, ’s-Hertogenbosch, The Netherlands, October 24-26, 2018, proceedings. Cham: Springer, 2018, pp. 380-391. Lecture Notes in Computer Science. 11191. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-030-01767-5. Available under: doi: 10.1007/978-3-030-01768-2_31
BibTex
@inproceedings{Winter2018-10-05Learn-44691,
  year={2018},
  doi={10.1007/978-3-030-01768-2_31},
  title={Learned Feature Generation for Molecules},
  number={11191},
  isbn={978-3-030-01767-5},
  issn={0302-9743},
  publisher={Springer},
  address={Cham},
  series={Lecture Notes in Computer Science},
  booktitle={Advances in Intelligent Data Analysis XVII : 17th International Symposium, IDA 2018, ’s-Hertogenbosch, The Netherlands, October 24-26, 2018, proceedings},
  pages={380--391},
  editor={Duivesteijn, Wouter and Siebes, Arno and Ukkonen, Antti},
  author={Winter, Patrick and Borgelt, Christian and Berthold, Michael R.}
}
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