Simulating lipophilicity of organic molecules with a back-propagation neural network.
From a training set of 7200 chemicals, a back-propagation neural network (BNN) model was developed for calculating the 1-octanol/water partition coefficient (log P) of molecules containing nitrogen, oxygen, halogen, phosphorus, and/or sulfur atoms. Chemicals were described by means of autocorrelation vectors encoding hydrophobicity, molar refractivity, H-bonding acceptor ability, and H-bonding donor ability. A 35/32/1 composite network composed of four configurations was selected as the final model (root-mean-square error (RMS) = 0.37, r = 0.97) because it provided the best simulation results (RMS = 0.39, r = 0.98) on an external testing set of 519 molecules. This final model compared favorably with a recently published BNN model using variables (atoms and bonds) derived from connection matrices.
Devillers
J
J
CTIS, 3 Chemin de la Gravière, 69140 Rillieux La Pape, France, and EU/Joint Research Center, Ispra Center, I-21020 Ispra, Varese, Italy. jde-ctis@imaginet.fr
Domine
D
D
Guillon
C
C
Karcher
W
W
D003198:Computer Simulation; D008055:Lipids / Q000737:chemistry*; D008956:Models, Chemical; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D012995:Solubility
D008055:Lipids; D004364:Pharmaceutical Preparations