importance of the feature. Finally, 253 features were selected for model training and testing. Model Training The complete dataset was split into 70% training set and 30% test set using the function (R package) (Kuhn, 2008). Next, 5-fold internal createDataPartition caret cross-validation was performed on the training dataset and predictions were made on the test set. Two machine learning methods, Random Forest (RF) and Decision Trees (DT) (Quinlan, 1986; Breiman, 2001) were employed for model training and bioactivity prediction. Random forest was tuned using values of 1 to 8 (R mtry caret package), in addition to decision trees (R package) using the complexity rpart parameter (cp) set equal to zero. The top 253 selected features RF were used to via train a predictive model on the external test set with DT, and visualized using the R package (Torsten Hothorn, Kurt Hornik, 2006; Strobl 2007, 2008; party et al., Garge 2013). et al., Homology Modelling The first structure of the GABAA receptor subtype was modelled in 2012 and was α1 used to hypothesize the common binding modes of traditional benzodiazepines (Richter 2012). This structure was used as a template in the current study for et al., modeling the GABAA subtype. Sequence alignment of the and receptor α2 α1 α2 subtype, performed by ClustalW (Larkin 2007), gave about 80% pairwise et al., sequence similarity, and hence this modelled structure of the subtype of the GABA α1 receptor was employed as a template for model prediction using Modeller 9V3 α2 (Eswar 2007; Webb and Sali, 2014). The structures of both subtypes were et al., structurally aligned by performing structure superimposition using Chimera 1.6
importance of the feature. Finally, 253 features were selected for model training and testing. Model Training The complete dataset was split into 70% training set and 30% test set using the createDataPartition function (R caret package) (Kuhn, 2008). Next, 5-fold internal cross-validation was performed on the training dataset and predictions were made on the test set. Two machine learning methods, Random Forest (RF) and Decision Trees (DT) (Quinlan, 1986; Breiman, 2001) were employed for model training and bioactivity prediction. Random forest was tuned using mtry values of 1 to 8 (R caret package), in addition to decision trees (R rpart package) using the complexity parameter (cp) set equal to zero. The top 253 selected features via RF were used to train a predictive model on the external test set with DT, and visualized using the party R package (Torsten Hothorn, Kurt Hornik, 2006; Strobl et al., 2007, 2008; Garge et al., 2013). Homology Modelling The first structure of the GABAA receptor α1 subtype was modelled in 2012 and was used to hypothesize the common binding modes of traditional benzodiazepines (Richter et al., 2012). This structure was used as a template in the current study for modeling the GABAA receptor α2 subtype. Sequence alignment of the α1 and α2 subtype, performed by ClustalW (Larkin et al., 2007), gave about 80% pairwise sequence similarity, and hence this modelled structure of the α1 subtype of the GABA receptor was employed as a template for α2 model prediction using Modeller 9V3 (Eswar et al., 2007; Webb and Sali, 2014). The structures of both subtypes were structurally aligned by performing structure superimposition using Chimera 1.6
GABA_final.docx