“…They also utilized a quantitative structure activity relationship (QSAR) model to improve the correlation to 0.88, combining affinities from MMPBSA, QM/MMGBSA, and Glide scoring (Slynko et al, 2014 ). There were other attractive targets including indoleamine 2,3-dioxygenase 1 (Zou et al, 2017 ), translationally controlled tumor protein (Kumar R. et al, 2017 ), estrogen receptor (Anbarasu and Jayanthi, 2017 ), MutT homolog 1 (Zhou et al, 2016 ), survivin (Sarvagalla et al, 2016 ), CD44 (Nguyen et al, 2016 ), calmodulin (Gonzalez-Andrade et al, 2016 ), androgen receptor (Liu H. L. et al, 2016 ), human topoisomerase I (Guruge et al, 2016 ), Mcl-1 (Zhao et al, 2015 ), vascular endothelial growth factor receptor-2 (Wu et al, 2015 ), tubulin (Liao et al, 2014a , b ; Santoshi and Naik, 2014 ; Suri and Naik, 2015 ; Suri et al, 2015 ), the Hsp70 protein family (Bhattacharjee et al, 2015 ; Schneider et al, 2016 ), the Hsp90 protein family (Arba et al, 2015 ), glucose 6-phosphate dehydrogenase (Obiol-Pardo et al, 2014 ; Zhao et al, 2014 ), lysozyme (Zhan et al, 2015 ), p53 (Verma S. et al, 2016 ), wheat germ agglutinin (Parasuraman et al, 2014 ), bromodomains (Muvva et al, 2014 ), matrix metalloproteinases (Zhou et al, 2014 ), protein arginine methyltransferases (Hong et al, 2014 ; Yan et al, 2014 ), human arsenic methyltransferase (Abro and Azam, 2016 ), Atox1 proteins (Wang X. L. et al, 2014 ), tyrosyl-DNA phosphodiesterase 2 (Kossmann et al, 2016 ), and urokinase-type plasminogen activator (Sa et al, 2014 ).…”