Construction of Quantitative Structure Activity Relationship (QSAR) Models to Predict Potency of Structurally Diversed Janus Kinase 2 Inhibitors
Thailand Center of Excellence for Life Sciences (Public Organization), Ministry of Science and Technology, Bangkok 10400, Thailand
Department of Social and Applied Science, College of Industrial Technology, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
Author to whom correspondence should be addressed.
Academic Editor: Alejandro Speck-Planche
Molecules 2019, 24(23), 4393; https://doi.org/10.3390/molecules24234393
Received: 5 November 2019 / Revised: 24 November 2019 / Accepted: 28 November 2019 / Published: 1 December 2019
(This article belongs to the Special Issue Recent Advances in Computational Drug Discovery: From In Silico Screening to Multiscale De Novo Drug Design)
Janus kinase 2 (JAK2) inhibitors represent a promising therapeutic class of anticancer agents against many myeloproliferative disorders. Bioactivity data on pIC
of 2229 JAK2 inhibitors were employed in the construction of quantitative structure-activity relationship (QSAR) models. The models were built from 100 data splits using decision tree (DT), support vector machine (SVM), deep neural network (DNN) and random forest (RF). The predictive power of RF models were assessed via 10-fold cross validation, which afforded excellent predictive performance with and RMSE of 0.74 ± 0.05 and 0.63 ± 0.05, respectively. Moreover, test set has excellent performance of (0.75 ± 0.03) and RMSE (0.62 ± 0.04). In addition, Y-scrambling was utilized to evaluate the possibility of chance correlation of the predictive model. A thorough analysis of the substructure fingerprint count was conducted to provide insights on the inhibitory properties of JAK2 inhibitors. Molecular cluster analysis revealed that pyrazine scaffolds have nanomolar potency against JAK2.