Synthesis and aromatase inhibitory evaluation of 4-N-nitrophenyl substituted amino-4H-1,2,4-triazole derivatives

Towards an Understanding of the Mode of Action of Human Aromatase Activity for Azoles through Quantum Chemical Descriptors-Based Regression and Structure Activity Relationship Modeling Analysis

“…The positive contribution of the shape index (tertiary carbon atom) to the antagonist activity revealed that compact molecules, with less surface area such as the spherical shape of antagonists, increase pIC 50 value for enzyme CYP19A1 inhibition. These stereochemical aspects for triazoles are consistent with the QSAR study by Song et al, 2016 [17], which found that the descriptor "molecular volume" has a negative contribution to antagonist activity.…”

“…Two techniques are commonly used for the global application of the predictive modeling in toxicology, for example, employing either a large dataset using techniques such as machine learning methods or deriving mechanistically interpretable simple models [14,15]. Both techniques have been exploited to create predictive models for the antagonist activity of azoles with CYP19A1 [16][17][18][19][20]. Shoombuatong et al, 2018, reviewed this area and concluded that the modeling of nonsteroidal aromatase inhibition requires nitrogen-containing descriptors, polarizability, the energy of highest occupied molecular orbital (HOMO), the energy gap of highest occupied molecular orbital and lowest unoccupied molecular orbital (HOMO-LUMO gap), and descriptors for hydrogen bond acceptors [21].…”

Towards understanding aromatase inhibitory activity via QSAR modeling

“…4)) shows that eight articles (Song et al, 2016[91]; Ghodsi and Hemmateenejad, 2016[32]; Adhikari et al, 2017[1]; Prachayasittikul et al, 2017[72]; Pingaew et al, 2018[70]; Lee and Barron, 2018[49]; Barigye et al, 2018[9]) have already been published in comparison to a total of 13 publications for the previous 5 years. Thus, it is promising that the number of publication regarding AIs utilizing QSAR models for prediction will continue to grow.…”

“…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. …”

Synthesis and docking study of benzimidazole–triazolothiadiazine hybrids as aromatase inhibitors

“…[ 16–18 ] Some imidazole and triazole derivatives have previously been synthesized and evaluated as potential antiaromatase agents. [ 19–22 ] The structures of anastrozole, letrozole, and vorozole, which carry the triazole structure of AIs, are shown in Figure 1.…”