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PTML Knowledge-Based System for Multi-Output Prediction of Anti-Melanoma Compounds
1  Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain Medicine, Benemerita Universidad Autonoma de Puebla BUAP, 72000, Puebla Mexico


Defining the target proteins of new anti-melanoma compounds is a crucial task in Medicinal Chemistry. In this sense, chemists carry out preclinical assays with a high number of combinations of experimental conditions (cj). In fact, ChEMBL database contains outcomes of 327480 different anti-melanoma activity preclinical assays for 1031 different chemical compounds (317,6 assays per compound). These assays cover different combinations of cj of biological activity parameters (c0), proteins (c1), drug targets (c2), cells (c3) and 5 organisms of assay (c4) and/or organisms of the target (c4), etc. In this work, we report a PTML for this data set with high Specificity and Sensitivity .

Keywords: ChEMBL; Anti-cancer compounds; Perturbation Theory; Machine Learning; Artificial Neural Networks; Big data; Multi-target models; Melanoma; Cancer, Activity