CoMFA, LeapFrog and blind docking studies on sulfonanilide derivatives acting as selective aromatase expression regulators

<i>In silico</i> design and evaluation of carboxylesterase inhibitors

“…Combining QSAR and de novo design has been used by several investigators 49–53) . For example, Gueto and coworkers 49) developed a CoMFA (Comparative Molecular Fields Analysis) model for aromatase inhibitors with 45 training set compounds and used a test set of 10 sulfonanilide compounds. This CoMFA model was used to generate new inhibitors in silico with the NEW module of LeapFrog, followed by calculation of their predicted activity.…”

Towards understanding aromatase inhibitory activity via QSAR modeling

“…4)) with the publication of ten articles in the time period (Bak and Polanski, 2007[8]; Nagar et al, 2008[55]; Castellano et al, 2008[16]; Mittal et al, 2009[53]; Gueto et al, 2009[36]; Nagar and Saha, 2010[57][56]; Roy and Roy, 2010[77][78]; Dai et al, 2010[24]). Most of the QSAR models in this time frame were built utilizing physicochemical descriptors as compared to other techniques in the previous years.…”

“…The method enabled the authors to identify molecular motifs contributing to the aromatase enzyme binding activity. Gueto et al (2009[36]) employed structure-based drug design approach using receptor-independent CoMFA maps that were generated from LeapFrog calculations. …”

“…As summarized in Table 4(Tab. 4) (References in Table 4: Nagy et al, 1994[58]; Recanatini, 1996[75]; Oprea and García, 1996[67]; Sulea et al, 1997[92]; Recanatini and Cavalli, 1998[76]; Cavalli et al, 2000[18]; Beger et al, 2001[10]; Gironés and Carbó-Dorca, 2002[34]; Beger and Wilkes, 2002[12]; Polanski and Gieleciak, 2003[71]; Leonetti et al, 2004[50]; Beger et al, 2004[11]; Cavalli et al, 2005[17]; Bak and Polanski, 2007[8]; Castellano et al, 2008[16]; Nagar et al, 2008[55]; Mittal et al, 2009[53]; Gueto et al, 2009[36]; Dai et al, 2010[24]; Roy and Roy, 2010[78]; Roy and Roy, 2010[77]; Nagar and Saha, 2010[57]; Nagar and Saha, 2010[56]; Narayana et al, 2012[65]; Nantasenamat et al, 2013[64]; Nantasenamat et al, 2013[61]; Kishore et al, 2013[44]; Worachartcheewan et al, 2014[103]; Worachartcheewan et al, 2014[101]; Nantasenamat et al, 2014[63]; Dai et al, 2014[25]; Awasthi et al, 2015[7]; Xie et al, 2015[105]; Shoombuatong et al, 2015[85]; Xie et al, 2014[102]; Kumar et al, 2016[48]; Ghodsi and Hemmateenejad, 2016[32]; Song et al, 2016[91]; Prachayasittikul et al, 2017[72]; Adhikari et al, 2017[1]; Lee and Barron, 2018[49]; Pingaew et al, 2018[70]; Barigye et al, 2018[9]), it can be observed that prior to 2010, MLR and PLS models, also known as white-box approaches, were the most popular and yet simple learning algorithms used for QSAR modeling of AIs. …”

Molecular modelling studies on d-annulated benzazepinones as VEGF-R2 kinase inhibitors using docking and 3D-QSAR

“…These molecules were aligned to Compound No. Basic structure ), based on the molecules of test set, was defined as follows: r 2 pred = (SD-PRESS)/SD SD is the sum of the squared deviations between the inhibitory activities of the test set and mean activities of the training molecules; PRESS is the sum of squared deviations between predicted and actual activity values for each molecule in the test set 17,[21][22][23] .…”