0000000001080868

AUTHOR

Rosa Acevedo-barrios

showing 3 related works from this author

In silicoAntibacterial Activity Modeling Based on the TOMOCOMD-CARDD Approach

2015

In the recent times, the race to cope with the increasing multidrug resistance of pathogenic bacteria has lost much of its momentum and health professionals are grasping for solutions to deal with the unprecedented resistance levels. As a result, there is an urgent need for a concerted effort towards the development of new antimicrobial drugs to stay ahead in the fight against the ever adapting bacteria. In the present report, antibacterial classification functions (models) based on the topological molecular computational design-computer aided >rational> drug design (TOMOCOMD-CARDD) atom-based non-stochastic and stochastic bilinear indices are presented. These models were built using the li…

Virtual screeningQuantitative structure–activity relationshipVirtual screeninglinear discriminant analysisLinear discriminant analysisQSARTOMOCOMD-CARDD softwareIn silicoDegrees of freedom (statistics)Bilinear interpolationNanotechnologyGeneral Chemistryatom-based bilinear indexvirtual screeningLinear discriminant analysisRange (mathematics)antibacterial activityAtom-based bilinear indexAntibacterial activityBiological systemAntibacterial activityMathematicsJournal of the Brazilian Chemical Society
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QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents

2015

The QuBiLs-MAS approach is used for the in silico modelling of the antifungal activity of organic molecules. To this effect, non-stochastic (NS) and simple-stochastic (SS) atom-based quadratic indices are used to codify chemical information for a comprehensive dataset of 2478 compounds having a great structural variability, with 1087 of them being antifungal agents, covering the broadest antifungal mechanisms of action known so far. The NS and SS index-based antifungal activity classification models obtained using linear discriminant analysis (LDA) yield correct classification percentages of 90.73% and 92.47%, respectively, for the training set. Additionally, these models are able to correc…

AntifungalQuantitative structure–activity relationshipAntifungal AgentsLinear discriminant analysismedicine.drug_classIn silicoAtom-based quadratic indicesQSAR modelQuantitative Structure-Activity RelationshipBioengineeringDrug developmentComputational biologyQuantitative structure activity relationVrtual screening antifungal agentDrug DiscoverymedicineComputer SimulationDrug identificationChemistryDrug discoveryLinear modelDiscriminant AnalysisGeneral MedicineLinear discriminant analysisCombinatorial chemistryChemistryTest setLinear ModelsMolecular MedicineQuBiLs-MAS softwareStatistical modelAntifungal agent
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In silico Antibacterial Activity Modeling Based on the TOMOCOMD-CARDD Approach

2021

In the recent times, the race to cope with the increasing multidrug resistance of pathogenic bacteria has lost much of its momentum and health professionals are grasping for solutions to deal with the unprecedented resistance levels. As a result, there is an urgent need for a concerted effort towards the development of new antimicrobial drugs to stay ahead in the fight against the ever adapting bacteria. In the present report, antibacterial classification functions (models) based on the topological molecular computational design-computer aided ‘‘rational’’ drug design (TOMOCOMD-CARDD) atom-based non-stochastic and stochastic bilinear indices are presented. These models were built using the …

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