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Deep Convolutional Neural Networks for the Prediction of Molecular Properties: Challenges and Opportunities Connected to the Data
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2128-7090
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0001-8884-2154
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2973-3112
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0001-7106-0025
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2018 (English)In: Journal of Integrative Bioinformatics, E-ISSN 1613-4516, Vol. 16, no 1Article in journal (Refereed) Published
Abstract [en]

We present a flexible deep convolutional neural network method for the analysis of arbitrary sized graph structures representing molecules. This method, which makes use of the Lipinski RDKit module, an open-source cheminformatics software, enables the incorporation of any global molecular (such as molecular charge and molecular weight) and local (such as atom hybridization and bond orders) information. In this paper, we show that this method significantly outperforms another recently proposed method based on deep convolutional neural networks on several datasets that are studied. Several best practices for training deep convolutional neural networks on chemical datasets are also highlighted within the article, such as how to select the information to be included in the model, how to prevent overfitting and how unbalanced classes in the data can be handled.

Place, publisher, year, edition, pages
De Gruyter Open, 2018. Vol. 16, no 1
Keywords [en]
Molecular property prediction, Deep learning, Unbalanced data, Side effects prediction
National Category
Computer Sciences Medicinal Chemistry
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
URN: urn:nbn:se:his:diva-16480DOI: 10.1515/jib-2018-0065ISI: 000459384300004PubMedID: 30517077Scopus ID: 2-s2.0-85061622864OAI: oai:DiVA.org:his-16480DiVA, id: diva2:1269149
Available from: 2018-12-09 Created: 2018-12-09 Last updated: 2019-06-13Bibliographically approved

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Ståhl, NiclasFalkman, GöranKarlsson, AlexanderMathiason, Gunnar

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