Bi- and multilinear PLS coupled to MIA-QSAR in the prediction of antifungal activities of some benzothiazole derivatives.
The activities of a series of benzothiazole derivatives, some Candida albicans N-myristoyltransferase (Nmt) inhibitors, were modeled through MIA-QSAR (multivariate image analysis applied to quantitative structure-activity relationship) by using two different regression methods: N-PLS, applied to the three-way array, and PLS, applied to the unfolded array. Both models demonstrated excellent predictive ability, with results comparable to those obtained through 3D approaches. In order to compare the results obtained through MIA descriptors with the predictions of a classical 2D QSAR, some representative physicochemical descriptors were calculated and regressed against the experimental pIC50 values through multiple linear regression, demonstrating that MIA-QSAR was superior for this series of compounds.
Bitencourt
Michelle
M
Departamento de Química, Universidade Federal de Lavras - UFLA, Caixa Postal 3037, 37200-000 Lavras, MG, Brazil.
Freitas
Matheus P
MP
D000217:Acyltransferases / Q000037:antagonists & inhibitors / Q000737:chemistry; D000935:Antifungal Agents / Q000737:chemistry* / Q000494:pharmacology*; D052160:Benzothiazoles / Q000737:chemistry* / Q000494:pharmacology*; D001665:Binding Sites / Q000187:drug effects; D002176:Candida albicans / Q000737:chemistry / Q000187:drug effects / Q000201:enzymology; D003198:Computer Simulation; D016014:Linear Models; D008956:Models, Chemical; D015394:Molecular Structure; D011237:Predictive Value of Tests; D021281:Quantitative Structure-Activity Relationship; D013237:Stereoisomerism
D000935:Antifungal Agents; D052160:Benzothiazoles; D000217:Acyltransferases; C050480:glycylpeptide N-tetradecanoyltransferase