title: Modeling structure-activity relationships of prodiginines with antimalarial activity using GA/MLR and OPS/PLS.
abstract: In the present study, we performed a multivariate quantitative structure-activity relationship (QSAR) analysis of 52 prodiginines with antimalarial activity. Variable selection was based on the genetic algorithm (GA) and ordered predictor selection (OPS) approaches, and the models were built using the multiple linear regression (MLR) and partial least squares (PLS) regression methods. The leave-N-out crossvalidation and y-randomization tests showed that the models were robust and free from chance correlation. The mechanistic interpretation of the results was supported by earlier findings. In addition, the comparison of our models with those previously described indicated that the OPS/PLS-based model had a higher quality of external prediction. Thus, this study provides a comprehensive approach to the evaluation of the antimalarial activity of prodiginines, which may be used as a support tool in designing new therapeutic agents for malaria.
authors.affiliation: Theoretical Medicinal and Environmental Chemistry Laboratory (LQMAT), Department of Pharmacy, Western Paraná State University (UNIOESTE), Cascavel, PR, Brazil.
authors.forename: Luana Janaína
authors.initials: LJ
authors.lastname: de Campos
authors.affiliation: Theoretical Medicinal and Environmental Chemistry Laboratory (LQMAT), Department of Pharmacy, Western Paraná State University (UNIOESTE), Cascavel, PR, Brazil. Electronic address: eduardo.b.de.melo@gmail.com.
authors.forename: Eduardo Borges
authors.initials: EB
authors.lastname: de Melo
mesh_terms: D000465:Algorithms; D000962:Antimalarials / Q000737:chemistry*; D011353:Prodigiosin / Q000031:analogs & derivatives* / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship; D013329:Structure-Activity Relationship
chemical_list: D000962:Antimalarials; C010307:prodiginine; D011353:Prodigiosin
keywords: Genetic algorithm; Malaria; OPS; PLS; Prodiginines; QSAR