Neural networks convergence using physicochemical data.
An investigation of the neural network convergence and prediction based on three optimization algorithms, namely, Levenberg-Marquardt, conjugate gradient, and delta rule, is described. Several simulated neural networks built using the above three algorithms indicated that the Levenberg-Marquardt optimizer implemented as a back-propagation neural network converged faster than the other two algorithms and provides in most of the cases better prediction. These conclusions are based on eight physicochemical data sets, each with a significant number of compounds comparable to that usually used in the QSAR/QSPR modeling. The superiority of the Levenberg-Marquardt algorithm is revealed in terms of functional dependence of the change of the neural network weights with respect to the gradient of the error propagation as well as distribution of the weight values. The prediction of the models is assessed by the error of the validation sets not used in the training process.
Karelson
Mati
M
Department of Chemistry, University of Tartu, 2 Jakobi Street, Tartu 51014, Estonia. mati.karelson@ttu.ee
Dobchev
Dimitar A
DA
Kulshyn
Oleksandr V
OV
Katritzky
Alan R
AR
D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1 / Q000037:antagonists & inhibitors; D000465:Algorithms; D002273:Carcinogens / Q000737:chemistry / Q000494:pharmacology; D005419:Flavonoids / Q000494:pharmacology; D016571:Neural Networks, Computer; D009930:Organic Chemicals / Q000737:chemistry; D010126:Ozone / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship; D012869:Skin Absorption
D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1; D002273:Carcinogens; D005419:Flavonoids; D009930:Organic Chemicals; D010126:Ozone