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

<p>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 dataset of 498 diverse drug-like 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 datasets suggest a non-linear 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.</p>

Name thesis:degree_name

Master of Science

Discipline thesis:degree_discipline

Pharmaceutical Science

Year dc:date.available

2010

Author dc:creator

• Albaugh, Daniel Ray

Contributors dc:contributor

• Jose Manautou;Dennis Hill;Michael Fisher
• David F. Grant

• HPLC Retention Index
• Artificial Neural Networks
• E-State Indices

Repository record dc:identifier

https://digitalcommons.lib.uconn.edu/gs_theses/34

OAI identifier oai:identifier

oai:digitalcommons.lib.uconn.edu:gs_theses-1030