0000000000178712
AUTHOR
Efrain Chaluisa Quishpe
<strong>Predicting Proteasome Inhibition using Atomic Weighted Vector and Machine Learning</strong>
Ubiquitin/Proteasome System (UPS) is a highly regulated mechanism of intracellular protein degradation and turnover. Through the concerted actions of a series of enzymes, proteins are marked for proteasomal degradation by being linked to the polypeptide co-factor, ubiquitin. The UPS participates in a wide array of biological functions such as antigen presentation, regulation of gene transcription and the cell cycle, and activation of NF-κB. Some researchers have applied QSAR method and machine learning in the study of proteasome inhibition (EC50(µmol/L)), such as: the analysis of proteasome inhibition prediction, in the prediction of multi-target inhibitors of UPP and in the prediction of p…
<strong>New tool useful for drug discovery validated through benchmark datasets</strong>
Atomic Weighted Vectors (AWVs) are vectors that contain the codified information of molecular structures, which can apply to a set of Aggregation Operators (AOs) to calculate total and local molecular descriptors (MDs). This article presents an exploratory study of a new tool useful for drug discovery using different datasets, such as DRAGON and Sutherland’s datasets, as well as their comparison with other well-known approaches. In order to evaluate the performance of the tool, several statistics and QSAR/QSPR experiments were performed. Variability analyses are used to quantify the information content of the AWVs obtained from the tool, by the way of an information theory-based algorithm. …