Quantitative structure/property relationship analysis on aqueous solubility using genetic algorithm-combined partial least squares method.
The present study was initiated to generate a model of predicting aqueous solubility of substances from their molecular structure. For 211 drugs or drug-like compounds, their topological indices were calculated by Molconn-Z software. The optimal subset of the descriptors for the prediction of aqueous solubility was determined by genetic algorithm in combination with partial least squares (PLS) method. Thirty-four descriptors were selected by this method. Using 29 of the descriptors selected, of which the scaled PLS coefficient was significant, the cross-validated predictive q2 was 0.785 with 19 principal components that was the optimal and the standard error of prediction was 0.676. Thus, it is suggested that the model obtained would exhibit a good performance in predicting the aqueous solubility of compounds.
Wanchana
S
S
Department of Drug Delivery Research, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan.
Yamashita
F
F
Hashida
M
M
D000465:Algorithms; D003627:Data Interpretation, Statistical; D005823:Genetics / Q000706:statistics & numerical data*; D016018:Least-Squares Analysis; D008956:Models, Chemical; D008968:Molecular Conformation; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D011237:Predictive Value of Tests; D021281:Quantitative Structure-Activity Relationship; D012995:Solubility; D014867:Water
D004364:Pharmaceutical Preparations; D014867:Water