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

• Main Authors: Le, Thi Thu Huong, Gerardo, M. Casañola-Martin, Facundo, Pérez-Giménez, Yovani, Marrero-Ponce
• Other Authors: Matilde, Merino-Sanjuán
• Format: Article
• Language: English
• Published: 2016
• Subjects: Ubiquitin–proteasome pathway inhibitors; CHEMBL; Multi-target; Multi-scale and multi-output models; Moving averages; QSAR
• Online Access: http://repository.vnu.edu.vn/handle/VNU_123/11504
• Institution: Vietnam National University, Hanoi

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 dif-ferent 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 con-tains >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 activ-ity 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 repre-sents 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 val-idation series. Finally, this model is considered multi-target andmulti-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