An In silico Investigation to Identify Promising Inhibitors for SARS-CoV-2 Mpro Target.

BACKGROUND: A limited number of small molecules against SARS-CoV-2 has been discovered since the epidemic commenced in November 2019. The conventional medicinal chemistry approach demands more than a decade of the year of laborious research and development and a substantial financial commitment, which is not achievable in the face of the current epidemic. OBJECTIVE: This study aims to discover and recognize the most effective and promising small molecules by interacting SARS-CoV-2 Mpro target through computational screening of 39 phytochemicals from five different Ayurvedic medicinal plants. METHODS: The phytochemicals were downloaded from Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB) PubChem, and the SARS-CoV-2 protein (PDB ID: 6LU7; Mpro) was taken from the PDB. The molecular interactions, binding energy, and ADMET properties were analyzed. RESULTS: The binding affinities were studied using a structure-based drug design of molecular docking, divulging 21 molecules possessing greater to equal affinity towards the target than the reference standard. Molecular docking analysis identified 13 phytochemicals, sennoside-B (-9.5 kcal/mol), isotrilobine (-9.4 kcal/mol), trilobine (-9.0 kcal/mol), serratagenic acid (-8.1 kcal/mol), fistulin (-8.0 kcal/mol), friedelin (-7.9 kcal/mol), oleanolic acid (-7.9 kcal/mol), uncinatone (-7.8 kcal/mol), 3,4-di- O-caffeoylquinic acid (-7.4 kcal/mol), clemaphenol A (-7.3 kcal/mol), pectolinarigenin (-7.2 kcal/mol), leucocyanidin (-7.2 kcal/mol), and 28-acetyl botulin (-7.2 kcal/mol) from ayurvedic medicinal plants phytochemicals possess greater affinity than the reference standard Molnupiravir (-7.0 kcal/mol) against SARS-CoV-2-Mpro. CONCLUSION: Two molecules, namely sennoside-B, and isotrilobine with low binding energies, were predicted as most promising. Furthermore, we carried out molecular dynamics simulations for the sennoside-B protein complexes based on the docking score. ADMET properties prediction confirmed that the selected docked phytochemicals were optimal. These compounds can be investigated further and utilized as a parent core molecule to create novel lead molecules for preventing COVID-19.

Rule mining and classification in a situation assessment application: a belief-theoretic approach for handling data imperfections.

Management of data imprecision and uncertainty has become increasingly important, especially in situation awareness and assessment applications where reliability of the decision-making process is critical (e.g., in military battlefields). These applications require the following: 1) an effective methodology for modeling data imperfections and 2) procedures for enabling knowledge discovery and quantifying and propagating partial or incomplete knowledge throughout the decision-making process. In this paper, using a Dempster-Shafer belief-theoretic relational database (DS-DB) that can conveniently represent a wider class of data imperfections, an association rule mining (ARM)-based classification algorithm possessing the desirable functionality is proposed. For this purpose, various ARM-related notions are revisited so that they could be applied in the presence of data imperfections. A data structure called belief itemset tree is used to efficiently extract frequent itemsets and generate association rules from the proposed DS-DB. This set of rules is used as the basis on which an unknown data record, whose attributes are represented via belief functions, is classified. These algorithms are validated on a simplified situation assessment scenario where sensor observations may have caused data imperfections in both attribute values and class labels.