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
The authors like to thank Lewis Mervin (Centre for Molecular Informatics, Department of Chemistry, University of Cambridge) for his insightful comments and for reviewing the manuscript. Authorship Contribution Participated in research design: Qurrat Ain, Andreas Bender, David Pryde, Robert Owen, Kiyoyuki Omoto and Krishna C Bulusu Conducted experiments: Qurrat Ain and Julian Fuchs Performed Analysis: Qurrat Ain, Julian Fuchs, Rubben Torella Wrote and contributed to manuscript: Qurrat Ain, Andreas Bender, David Pryde, Robert Owen and Kiyoyuki Omoto, Julian Fuchs, Rubben Torella and Robert Glen. Abdi H, and Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2:433–459. Atack JR (2010) Development of Subtype-Selective GABAA Receptor Compounds for the Treatment of Anxiety, Sleep Disorders and Epilepsy (Monti JM ed), Springer. Atack JR (2008) GABAA Receptor Subtype-Selective Efficacy: TPA023, an α2/α3 Selective Non-sedating Anxiolytic and α5IA, an α5 Selective Cognition Enhancer. CNS Drug Rev 14:25–35. Atack JR (2011a) GABAA receptor subtype-selective modulators. I. α2/α3-selective agonists as non-sedating anxiolytics. Curr Top Med Chem 11:1176–202. Atack JR (2011b) GABAA receptor subtype-selective modulators. II. α5-selective inverse agonists for cognition enhancement. Curr Top Med Chem 11:1203–1214. Atack JR (2009) Subtype-selective GABA(A) receptor modulation yields a novel pharmacological profile: the design and development of TPA023., Edition, Elsevier. Atack JR (2005) The benzodiazepine binding site of GABA(A) receptors as a target