Prediction of oral bioavailability by adaptive fuzzy partitioning.
An adaptive fuzzy partition (AFP) algorithm was applied on two bioavailability data sets subdivided into four ranges of activity. A large set of molecular descriptors was tested and the most relevant parameters were selected with help of a procedure based on genetic algorithm concepts and stepwise method. After building several AFP models on a training set, the best ones were able to predict correctly 75% of the validation set compounds. Furthermore, an improvement of about 15% in the validation results was got, on the same data set, as regard to other prediction methods. The importance to work with data sets including a large molecular diversity, and to use tools able to manage it, was also shown. The prediction power was increased up to 25% employing a data set with a better-optimised molecular diversity.
BioChemics Consulting, Innovation Center, 16, rue Leonard de Vinci, 45074 Orleans cedex 2, France. jacques.chretien@univ-orleans.fr
Marco
M
Pintore
Han
H
van de Waterbeemd
Nadège
N
Piclin
Jacques R
JR
Chrétien
D000284:Administration, Oral; D000465:Algorithms; D001682:Biological Availability; D017143:Fuzzy Logic; D006801:Humans; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000008:administration & dosage*; D010599:Pharmacokinetics
D004364:Pharmaceutical Preparations