QSAR modeling of anti-invasive activity of organic compounds using structural descriptors.
The anti-invasive activity of 139 compounds was correlated by an artificial neural network approach with descriptors calculated solely from the molecular structures using CODESSA Pro. The best multilinear regression method implemented in CODESSA Pro was used for a pre-selection of descriptors. The resulting nonlinear (artificial neural network) QSAR model predicted the exact class for 66 (71%) of the training set of 93 compounds and 32 (70%) of validation set of 46 compounds. The standard deviation ratios for the both training and validation sets are less than unity, indicating a satisfactory predictive capability for classification of the nature of the anti-invasive activity data. The proposed model can be used for the prediction of the anti-invasive activity of novel classes of compounds enabling a virtual screening of large databases of anticancer drugs.
Katritzky
Alan R
AR
Center for Heterocyclic Compounds, Department of Chemistry, University of Florida, Gainesville, FL 32611, USA. katritzky@chem.ufl.edu
Kuanar
Minati
M
Dobchev
Dimitar A
DA
Vanhoecke
Barbara W A
BW
Karelson
Mati
M
Parmar
Virinder S
VS
Stevens
Christian V
CV
Bracke
Marc E
ME
D000465:Algorithms; D000970:Antineoplastic Agents / Q000737:chemistry*; D001185:Artificial Intelligence; D015195:Drug Design; D016014:Linear Models; D009361:Neoplasm Invasiveness; D009369:Neoplasms / Q000188:drug therapy*; D016571:Neural Networks, Computer; D009930:Organic Chemicals / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship
D000970:Antineoplastic Agents; D009930:Organic Chemicals