Protein Domain-Based Prediction of Compound–Target Interactions And
bioRxiv preprint doi: https://doi.org/10.1101/2021.06.14.448307; this version posted June 14, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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 4.0 International license. 1 Protein Domain-Based Prediction of Compound–Target Interactions and 2 Experimental Validation on LIM Kinases 3 Tunca Doğan1,2,3,*, Ece Akhan Güzelcan3,4, Marcus Baumann5, Altay Koyas3, Heval Atas3, Ian 4 Baxendale6, Maria Martin7 and Rengul Cetin-Atalay3,8,* 5 1 Department of Computer Engineering, Hacettepe University, 06800 Ankara, Turkey 6 2 Institute of Informatics, Hacettepe University, 06800 Ankara, Turkey 7 3 CanSyL, Graduate School of Informatics, Middle East Technical University, 06800 Ankara, Turkey 8 4 Center for Genomics and Rare Diseases & Biobank for Rare Diseases, Hacettepe University, 06230 9 Ankara, Turkey 10 5 School of Chemistry, University College Dublin, D04 N2E2 Dublin, Ireland 11 6 Department of Chemistry, University of Durham, DH1 3LE Durham, UK 12 7 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome 13 Trust Genome Campus, CB10 1SD Hinxton, Cambridge, UK 14 8 Section of Pulmonary and Critical Care Medicine, University of Chicago, Chicago IL, 60637, USA 15 * To whom correspondence should be addressed. 16 E-mail: tuncadogan@hacettepe.edu.tr & rengul@uchicago.edu 17 Abstract 18 Predictive approaches such as virtual screening have been used in drug discovery with the 19 objective of reducing developmental time and costs. Current machine learning and network- 20 based approaches have issues related to generalization, usability, or model interpretability, 21 especially due to the complexity of target proteins’ structure/function, and bias in system 22 training datasets.
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