Prediction of HPLC Retention Index Using Artificial Neural Networks and IGroup E-State Indices

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• 作者：Daniel R. Albaugh ; L. Mark Hall ; Dennis W. Hill ; Tzipporah M. Kertesz ; Marc Parham ; Lowell H. Hall ; David F. Grant
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2009
• 出版时间：April 27, 2009
• 年：2009
• 卷：49
• 期：4
• 页码：788-799
• 全文大小：300K
• 年卷期：v.49,no.4(April 27, 2009)
• ISSN：1549-960X

文摘

A back-propagation artificial neural network (ANN) was used to create a 10-fold leave-10%-out cross-validated ensemble model of high performance liquid chromatography retention index (HPLC-RI) for a data set of 498 diverse druglike compounds. A 10-fold multiple linear regression (MLR) ensemble model of the same data was developed for comparison. Molecular structure was described using IGroup E-state indices, a novel set of structure-information representation (SIR) descriptors, along with molecular connectivity chi and kappa indices and other SIR descriptors previously reported. The same input descriptors were used to develop models by both learning algorithms. The MLR model yielded marginally acceptable statistics with training correlation r² = 0.65, mean absolute error (MAE) = 83 RI units. External validation of 104 compounds not used for model development yielded validation v² = 0.49 and MAE = 73 RI units. The distribution of residuals for the fit and validate data sets suggest a nonlinear relationship between retention index and molecular structure as described by the SIR indices. Not surprisingly, the ANN model was significantly more accurate for both training and validation with training set r² = 0.93, MAE = 30 RI units and validation v² = 0.84, MAE = 41 RI units. For the ANN model, a total of 91% of validation predictions were within 100 RI units of the experimental value.