Quick prediction of the retention of solutes in 13 thin layer chromatographic screening systems on silica gel by classification and regression trees.
The use of classification and regression trees (CART) was studied in a quantitative structure-retention relationship (QSRR) context to predict the retention in 13 thin layer chromatographic screening systems on a silica gel, where large datasets of interlaboratory determined retention are available. The response (dependent variable) was the rate mobility (RM) factor, while a set of atomic contributions and functional substituent counts was used as an explanatory dataset. The trees were investigated against optimal complexity (number of the leaves) by external validation and internal crossvalidation. Their predictive performance is slightly lower than full atomic contribution model, but the main advantage is the simplicity. The retention prediction with the proposed trees can be done without computer or even pocket calculator.
Komsta
Łukasz
Ł
Department of Medicinal Chemistry, Medical University of Lublin, Jaczewskiego 4, 20-090 Lublin, Poland. lukasz.komsta@am.lublin.pl
D002855:Chromatography, Thin Layer / Q000295:instrumentation / Q000379:methods; D002965:Classification; D003627:Data Interpretation, Statistical; D003663:Decision Trees; D005782:Gels / Q000737:chemistry; D008403:Mass Screening / Q000379:methods; D015394:Molecular Structure; D011237:Predictive Value of Tests; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D012822:Silicon Dioxide / Q000737:chemistry*; D013997:Time Factors
D005782:Gels; D012822:Silicon Dioxide