Multi-output model with Box–Jenkins operators of linear indices to predict multi-target inhibitors of ubiquitin–proteasome pathway

Abstract

The ubiquitin-proteasome pathway (UPP) plays an important role in the degradation of cellular proteins and regulation of different cellular processes that include cell cycle control, proliferation, differentiation, and apoptosis. In this sense, the disruption of proteasome activity leads to different pathological states linked to clinical disorders such as inflammation, neurodegeneration, and cancer. The use of UPP inhibitors is one of the proposed approaches to manage these alterations. On other hand, the ChEMBL database contains >5,000 experimental outcomes for >2,000 compounds tested as possible proteasome inhibitors using a large number of pharmacological assay protocols. All these assays report a large number of experimental parameters of biological activity like EC50, IC50 percent of inhibition, and many others that have been determined under many different conditions, targets, organisms, etc. Although this large amount of data offers new opportunities for the computational discovery of proteasome inhibitors, the complexity of these data represents a bottleneck for the development of predictive models. In this work, we used linear molecular indices calculated with the software TOMOCOMD-CARDD and Box-Jenkins moving average operators to develop a multi-output model that can predict outcomes for 20 experimental parameters in >450 assays carried out under different conditions. This generated multi-output model showed values of accuracy, sensitivity, and specificity above 70% for training and validation series. Finally, this model is considered multi-target and multi-scale, because it predicts the inhibition of the UPP for drugs against 22 molecular or cellular targets of different organisms contained in the ChEMBL database.

<strong>3D hierarchically scaffolds for bone repair: at the crossroads of experimental and computational outlooks</strong>

“…The model uses as input covariance PT operators (CPTOs). CPTOs are calculated as the difference in covariance ΔCov( i μk) functions before and after multiple perturbations in the binary system [9]. In turn, we can calculate the covariances calculated as the product of two Box−Jenkins operators (BJO) operators [10].…”

Latest advances in molecular topology applications for drug discovery

“…To overcome this drawback, a group of researchers introduced a new QSAR approach that uses a single equation for multiple targets known as 'multitarget' or 'multi-tasking QSAR' [57,58]. The pioneers are the group of Speck-Planche and Cordeiro along with the one of Gonzalez-Diaz [59,60]. Their latest results are collected here as a clear example of the effectiveness of MT-QSAR.…”

“…The same year, Gonzalez-Diaz et al published an innovative article [60], in which they introduced the first 'multi-target and multi-scale' QSAR model. It dealt with the prediction of inhibition of the ubiquitin--proteasome pathway for drugs acting on different biological targets of various organisms in the ChEMBL database.…”

Recent Advances in Multi-Task QSAR Modeling for Drug Design

“…4 Generally, a specific cut-off value for each system is fixed by the user to classify the data into 'sensitive, and 'non-sensitive'. Sometimes special statistical treatments, like averaging the descriptor values for specific system 11,12 and calculation of moving average, 18,21 are required for handling the complicated data. Some attempts were taken to develop regression based multi-target models.…”

“…Multi-task QSAR has been utilized for the developing models for anti-cancer, 11,12 anti-bacterial 13-17 and neuroprotective agents. [18][19][20] In addition, reports on multi-target modeling on kinase 6 and ubiquitineproteosome pathways 21 are also found. Furthermore, multi-task agents are also useful for understanding structural patterns required for toxicities of nanoparticles, environmental pollutants etc.…”