bioRxiv preprint doi: https://doi.org/10.1101/2020.03.12.988600; this version posted June 27, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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. Uncovering new drug properties in target-based drug-drug similarity networks Lucret¸iaUdrescu1, Paul Bogdan2, Aimee´ Chis¸ 3, Ioan Ovidiu Sˆırbu3,6, Alexandru Topˆırceanu4, Renata-Maria Varut¸˘ 5, and Mihai Udrescu4,6,* 1”Victor Babes¸” University of Medicine and Pharmacy Timis¸oara, Department of Drug Analysis, Timis¸oara 300041, Romania 2University of Southern California, Ming Hsieh Department of Electrical Engineering, Los Angeles, CA 90089-2563, USA 3”Victor Babes¸” University of Medicine and Pharmacy Timis¸oara, Department of Biochemistry, Timis¸oara 300041, Romania 4University Politehnica of Timis¸oara, Department of Computer and Information Technology, Timis¸oara 300223, Romania 5University of Medicine and Pharmacy of Craiova, Faculty of Pharmacy, Craiova 200349, Romania 6Timis¸oara Institute of Complex Systems, Timis¸oara 300044, Romania *
[email protected] ABSTRACT Despite recent advances in bioinformatics, systems biology, and machine learning, the accurate prediction of drug properties remains an open problem. Indeed, because the biological environment is a complex system, the traditional approach – based on knowledge about the chemical structures – cannot fully explain the nature of interactions between drugs and biological targets. Consequently, in this paper, we propose an unsupervised machine learning approach that uses the information we know about drug-target interactions to infer drug properties.