Predicting acetyl cholinesterase enzyme inhibition potential of ionic liquids using machine learning approaches: An aid to green chemicals designing

Environmental Impact of Ionic Liquids: Recent Advances in (Eco)toxicology and (Bio)degradability

“…Toxicological assays initially mainly focused on the aquatic impact of ILs; however, over time, data on the phytotoxicity, cytotoxicity, genotoxicity, antimicrobial activity, and enzymatic inhibition have also been reported. Moreover, a series of in silico models have been developed to predict the toxicity of ILs to different living organisms, such as Daphnia magna , Vibrio fischeri , acetylcholinesterase enzyme, and cell lines . Recent data demonstrate that not only internal factors, such as the chemical structure of the ILs, but also external factors can influence the toxicity of ILs, including the selected biological system and the experimental conditions.…”

“…Investigation of the contributions of a wide variety of ILs and chemical structures towards the acetylcholinesterase enzyme (AChE) through predictive qualitative and quantitative structure–activity relationship (SAR) models has been conducted by Basant et al . and Das and Roy .…”

Interpretation of toxicological activity of ionic liquids to acetylcholinesterase inhibition via in silico modelling

“…Therefore, a theoretical approach is advisable. Indeed, theoretical approaches have been be successfully applied to prediction of IL toxicity towards water flea Couling et al, 2006;Hossain et al, 2011;Roy and Das, 2013, 2015bSosnowska et al, 2014;Wang et al, 2015), bacteria (Cho et al, 2013Luis et al, 2010;Bruzzone et al, 2011;Sosnowska et al, 2014), animal cells (Cho et al, 2013;Sosnowska et al, 2014;Torrecilla et al, 2009;Yan et al, 2012a;Fatemi and Izadiyan, 2011;GarciaLorenzo et al, 2008), enzymes (Sosnowska et al, 2014;Torrecilla et al, 2009;Yan et al, 2012b;Basant et al, 2015;Das and Roy, 2014a) and algae (Cho et al, 2013;Das and Roy, 2014b).…”

“…Peric et al (2015) developed a model for aliphatic protic ILs using the group contribution method. Basant et al (2015) suggested nonlinear models based on machine-learning approaches; i.e., cascade correlation network (CCN) and support vector machines (SVMs). However, some former models have some shortcomings and must be updated when a new class of IL is introduced.…”

Comprehensive approach for predicting toxicological effects of ionic liquids on several biological systems using unified descriptors

“…Indeed, OECD 4 and REACH 5 recommend the approach like quantitative structure activity relationship (QSAR) for risk management. With the motivation, several researchers have developed the theoretical models for the toxicological effects of ILs on various toxicity testing systems including water fleas 6 7 8 9 10 11 12 13 14 .algae 12 15 16 , animal cell 12 15 17 18 19 , bacteria 12 15 20 21 22 23 24 25 26 27 , and enzyme activity 12 17 28 29 30 31 32 . However, since the previous models have physico-chemically ambiguous parameters and each study employs different parameters according to the toxicity testing systems, it is hard to comprehensively understand the structural effects of ILs on their toxicological activities to various environmental organisms.…”