Structure-based classification of antibacterial activity.
The aim of this study was to develop a simple quantitative structure-activity relationship (QSAR) for the classification and prediction of antibacterial activity, so as to enable in silico screening. To this end a database of 661 compounds, classified according to whether they had antibacterial activity, and for which a total of 167 physicochemical and structural descriptors were calculated, was analyzed. To identify descriptors that allowed separation of the two classes (i.e. those compounds with and without antibacterial activity), analysis of variance was utilized and models were developed using linear discriminant and binary logistic regression analyses. Model predictivity was assessed and validated by the random removal of 30% of the compounds to form a test set, for which predictions were made from the model. The results of the analyses indicated that six descriptors, accounting for hydrophobicity and inter- and intramolecular hydrogen bonding, provided excellent separation of the data. Logistic regression analysis was shown to model the data slightly more accurately than discriminant analysis.
School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England. m.t.cronin@livjm.ac.uk
Mark T D
MT
Cronin
Aynur O
AO
Aptula
John C
JC
Dearden
Judith C
JC
Duffy
Tatiana I
TI
Netzeva
Hiren
H
Patel
Philip H
PH
Rowe
T Wayne
TW
Schultz
Andrew P
AP
Worth
Konstantinos
K
Voutzoulidis
Gerrit
G
Schüürmann
D000900:Anti-Bacterial Agents / Q000737:chemistry* / Q000145:classification* / Q000494:pharmacology; D001419:Bacteria / Q000187:drug effects; D020650:Combinatorial Chemistry Techniques; D003198:Computer Simulation; D006860:Hydrogen Bonding; D057927:Hydrophobic and Hydrophilic Interactions; D016014:Linear Models; D016015:Logistic Models; D021281:Quantitative Structure-Activity Relationship
D000900:Anti-Bacterial Agents