Marrero-Ponce, Yovani published the artcileLigand-Based Virtual Screening and in Silico Design of New Antimalarial Compounds Using Nonstochastic and Stochastic Total and Atom-Type Quadratic Maps, Quality Control of 2508-72-7, the publication is Journal of Chemical Information and Modeling (2005), 45(4), 1082-1100, database is CAplus and MEDLINE.
Malaria has been one of the most significant public health problems for centuries. It affects many tropical and subtropical regions of the world. The increasing resistance of Plasmodium spp. to existing therapies has heightened alarms about malaria in the international health community. Nowadays, there is a pressing need for identifying and developing new drug-based antimalarial therapies. In an effort to overcome this problem, the main purpose of this study is to develop simple linear discriminant-based quant. structure-activity relation (QSAR) models for the classification and prediction of antimalarial activity using some of the TOMOCOMD-CARDD (TOpol. Mol. COMputer Design-Computer Aided “Rational” Drug Design) fingerprints, to enable computational screening from virtual combinatorial datasets. In this sense, a database of 1562 organic chems. having great structural variability, 597 of them antimalarial agents and 965 compounds having other clin. uses, was analyzed and presented as a helpful tool, not only for theor. chemists but also for other researchers in this area. This series of compounds was processed by a k-means cluster anal. to design training and predicting sets. Afterward, two linear classification functions were derived to discriminate between antimalarial and nonantimalarial compounds The models (including nonstochastic and stochastic indexes) correctly classify more than 93% of the compound set, in both training and external prediction datasets. They showed high Matthews’ correlation coefficients, 0.889 and 0.866 for the training set and 0.855 and 0.857 for the test one. The models’ predictivity was also assessed and validated by the random removal of 10% of the compounds to form a new test set, for which predictions were made using the models. The overall means of the correct classification for this process (leave group 10% full-out cross validation) using the equations with nonstochastic and stochastic atom-based quadratic fingerprints were 93.93% and 92.77%, resp. The quadratic maps-based TOMOCOMD-CARDD approach implemented in this work was successfully compared with four of the most useful models for antimalarials selection reported to date. The developed models were then used in a simulation of a virtual search for Ras FTase (FTase = farnesyltransferase) inhibitors with antimalarial activity; 70% and 100% of the 10 inhibitors used in this virtual search were correctly classified, showing the ability of the models to identify new lead antimalarials. Finally, these two QSAR models were used in the identification of previously unknown antimalarials. In this sense, three synthetic intermediaries of quinolinic compounds were evaluated as active/inactive ones using the developed models. The synthesis and biol. evaluation of these chems. against two malaria strains, using chloroquine as a reference, was performed. An accuracy of 100% with the theor. predictions was observed Compound 3 showed antimalarial activity, being the first report of an arylaminomethylenemalonate having such behavior. This result opens a door to a virtual study considering a higher variability of the structural core already evaluated, as well as of other chems. not included in this study. We conclude that the approach described here seems to be a promising QSAR tool for the mol. discovery of novel classes of antimalarial drugs, which may meet the dual challenges posed by drug-resistant parasites and the rapid progression of malaria illnesses.
Journal of Chemical Information and Modeling published new progress about 2508-72-7. 2508-72-7 belongs to imidazoles-derivatives, auxiliary class Inhibitor,Immunology/Inflammation,Histamine Receptor, name is N-Benzyl-N-((4,5-dihydro-1H-imidazol-2-yl)methyl)aniline hydrochloride, and the molecular formula is C17H20ClN3, Quality Control of 2508-72-7.
Referemce:
https://en.wikipedia.org/wiki/Imidazole,
Imidazole | C3H4N2 – PubChem