bioRxiv preprint doi: https://doi.org/10.1101/491365; this version posted December 9, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. DEEPScreen: High Performance Drug-Target Interaction Prediction with Convolutional Neural Networks Using 2-D Structural Compound Representations† Ahmet Sureyya Rifaioglu1,2, Volkan Atalay1,3,*, Maria Jesus Martin4, Rengul Cetin-Atalay3, Tunca Doğan3,4,* 1 Department of Computer Engineering, METU, Ankara, 06800, Turkey 2 Department of Computer Engineering, İskenderun Technical University, Hatay, 31200, Turkey 3 KanSiL, Department of Health Informatics, Graduate School of Informatics, METU, Ankara, 06800, Turkey 4 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK * Corresponding authors Contact information of the corresponding authors:
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[email protected] Abstract The identification of physical interactions between drug candidate chemical substances and target biomolecules is an important step in the process of drug discovery, where the standard procedure is the systematic screening of chemical compounds against pre-selected target proteins. However, experimental screening procedures are expensive and time consuming, therefore, it is not possible to carry out comprehensive tests. Within the last decade, computational approaches have been developed with the objective of aiding experimental studies by predicting novel drug-target interactions (DTI), via the construction and application of statistical models. In this study, we propose a large-scale DTI interaction prediction system, DEEPScreen, for early stage drug discovery, using convolutional deep neural networks.