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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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:
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[email protected] 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.