Nowadays, the physiopathological and molecular mechanisms of multiple diseases have been identified, thus helping scientists to provide a clear answer, especially to those ambiguities related to chronic illnesses. This has been accomplished in part through the contribution of a key discipline known as bioinformatics. In this study, the bioinformatics approach was applied on four compounds identified in Centaurea tougourensis, using two axes of research: an in silico study to predict the molecular characteristics, medicinal chemistry attributes as well as the possible cardiotoxicity and adverse liability profile of these compounds. In this context, four compounds were selected and named, respectively, 2,5-monoformal-l-rhamnitol (compound 1), cholest-7-en-3.beta.,5.alpha.-diol-6.alpha.-benzoate (compound 2), 7,8-epoxylanostan-11-ol, 3-acetoxy- (compound 3), and 1H-pyrrole-2,5-dione, 3-ethyl-4-methyl- (compound 4). The second part looked into molecular docking, which objective was to evaluate the possible binding affinity between these compounds and the serotonin 5-hydroxytryptamine 2A (5-HT2A) receptor. Results indicated that compounds 1 and 4 were respecting Pfizer and giant Glaxo-SmithKline rules, while compounds 2 and 3 exhibited an optimal medicinal chemistry evolution 18 score. The structural and molecular features of almost all tested compounds could be considered optimal, indicating that these phyto-compounds may possess drug-likeness capacity. However, only compounds 1 and 4 could be considered non-cardiotoxic, but with a level of confidence more pronounced for compound 1 (80%). In addition, these four biocompounds could preferentially interact with G protein-coupled receptor, ion channel, transporters, and nuclear receptors. However, the heat map was less pronounced for compound 2. Data also indicated that these four compounds could possibly interact with serotonin 5-HT2A receptor, but in an antagonistic way. This research proved once again that plants could be crucial precursors of pharmaceutical substances, which could be helpful to enrich the international pharmacopoeia.

Linear non-coding RNA (lncRNA) has recently attracted a variety of research, critical attention to some intriguing new knowledge about its essential functions. Thousands of lncRNAs were discovered in absurdly short durations because of tools like RNA-Seq. Only a handful of them should be intrinsically depicted, although, due to lesser citation incidence. Wet lab tests to elucidate the roles of lncRNA are complex, laborious, and sometimes ridiculously expensive. This research explores the critical topic of generating molecular docking toward determining the essential functions of lncRNAs. The model provided here uses a measure based on a systematic review known as AvgSim on a heterogeneous network infrastructure to predict various lncRNA (HIN) RNA activities. The same framework is designed with proposed statistics on the lncRNA enzyme or activity relationships and information on the lncRNA coexpression and linkage proteins. The proposed methodology forecasts probable capabilities for 2,695 lncRNAs with a reliability of 73.68% out of 2,758 lncRNAs evaluated for investigation and was performed remarkably better than other methodologies like a random forest for an impartial training dataset. These same linked roles of two well-known lncRNAs are investigated inside as an example. The findings were confirmed by research observations identified in this review.