The ubiquitin-proteasome pathway (UPP) is the primary degradation system of short-lived regulatory proteins.
Cellular processes such as the cell cycle, signal transduction, gene expression, DNA repair and apoptosis are regulated by
this UPP and dysfunctions in this system have important implications in the development of cancer, neurodegenerative,
cardiac and other human pathologies. UPP seems also to be very important in the function of eukaryote cells of the human
parasites like Plasmodium falciparum, the causal agent of the neglected disease Malaria. Hence, the UPP could be considered
as an attractive target for the development of compounds with Anti-Malarial or Anti-cancer properties. Recent online
databases like ChEMBL contains a larger quantity of information in terms of pharmacological assay protocols and compounds
tested as UPP inhibitors under many different conditions. This large amount of data give new openings for the
computer-aided identification of UPP inhibitors, but the intrinsic data diversity is an obstacle for the development of successful
classifiers. To solve this problem here we used the Bob-Jenkins moving average operators and the atom-based
quadratic molecular indices calculated with the software TOMOCOMD-CARDD (TC) to develop a quantitative model for
the prediction of the multiple outputs in this complex dataset. Our multi-target model can predict results for drugs against
22 molecular or cellular targets of different organisms with accuracies above 70% in both training and validation sets.
Keywords: UPP inhibitor, Cancer, Malaria, CHEMBL, multi-target, multi-scale and multi-output model, moving average;
QSAR, atom-based quadratic indices.
Rights & PermissionsPrintExport