Descriptors and their selection methods in QSAR analysis: paradigm for drug design

Danishuddin,; Khan, Asad U.

doi:10.1016/j.drudis.2016.06.013

Cited by 241 publications

(126 citation statements)

References 73 publications

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“…It shows that the distribution, bioaccumulation, and biomagnification of these pollutants is conditioned by their physicochemical properties. Using these parameters, an estimate of the toxicity generated by a variable source can be found, that is, a weighting of how toxic an emission or discharge is [22].…”

Section: Ecotoxicology Of Emerging Contaminantsmentioning

confidence: 99%

Ecotoxicological Analysis of Emerging Contaminants from Wastewater Discharges in The Coastal Zone of Cihuatlán (Jalisco, Mexico)

Arguello-Pérez

Mendoza-Pérez

Tintos-Gómez

et al. 2019

Water

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This research provides a baseline tool to detect, predict and scientifically evaluate the toxic environmental impact generated by chemical substances that are categorized as emerging contaminants (ECs) with endocrine disruptive action. The present study was carried out in five effluents of wastewater produced by urban and rural settlements of the coastal zone of Cihuatlan, Jalisco. Five compounds, considered ECs and that act as endocrine disruptors, were analyzed: Diclofenac, Ibuprofen, Ketorolac, Pentachlorophenol (PCP), and Estradiol. The toxicity level (TEQ) of the ECs is estimated by a Quantitative Structure-Activity Relationship (QSAR) analysis, evaluating their concentration and assessing the risk involved in the incorporation of each one into the environment. The presence of the ECs was confirmed in all the studied sites. It was attested that the concentrations of pollutants Diclofenac, Ibuprofen, Ketorolac, and Pentachlorophenol were within the toxic range, whereas the compound Estradiol was found in concentrations that represent a high toxicity in the same effluents. This research recognizes that the analysis of the physicochemical properties of substances allows for predicting whether a contaminant is likely to act and persist in the environment and, in turn, bioaccumulate in organisms.

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Section: Ecotoxicology Of Emerging Contaminantsmentioning

confidence: 99%

Ecotoxicological Analysis of Emerging Contaminants from Wastewater Discharges in The Coastal Zone of Cihuatlán (Jalisco, Mexico)

Arguello-Pérez

Mendoza-Pérez

Tintos-Gómez

et al. 2019

Water

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“…These molecular descriptors would rather be calculated than experimentally determined; this leap in the descriptor calculation and a decrease in the quality of QSAR models are not accidental. More than ever we have new high-quality predictions of physicochemical properties, which has enabled the development of complex QSAR models with a larger number of correlated variables [12]. Still, the association between occasional correlations together with the misuse of the number of parameters has led to a misunderstanding of QSAR as a scientific technique.…”

Section: Trends In Scientific Computation and Big Data In The Qsar Fieldmentioning

confidence: 99%

Current Trends in Quantitative Structure–Activity Relationship Validation and Applications On Drug Discovery

2017

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Quantitative structure-activity relationship history, current status & the importance of validationMethods to correlate biological activity and chemical structure of compounds have been employed since 1868 [1]. Early on, quantitative structure-activity relationship (QSAR) analyses were performed using experimentally determined physicochemical properties, such as logarithm of water/n-octanol partition coefficient (log P), hydrophobic constant (π) and Hammet electronic constant (σ), which were then correlated with the biological activity of the tested compounds [2].Nowadays, there are several prerequisites to construct and apply QSAR models. From the applicability point of view, QSAR requires a compound set that has been tested against an identified molecular target, cell, tissue, or even microorganism, under the same experimental conditions, and possesses the minimum variance in the observed responses [3]. Once an appropriate dataset has been selected, the main steps of a QSAR modeling require molecular/physicochemical properties, followed by variable selection, model generation from different algorithms and, most importantly, a validation process using internal and external datasets.After the generation and validation of a QSAR model, the model can be employed in predictions of biological activity of new samples and a physicochemical interpretation of the observed phenomena could be also conducted, providing insights for the design of new bioactive chemicals and/or their molecular mechanism of action. Subsequently, QSAR models have been widely employed in several steps of the drug design process, for instance, in order to understand and predict the compound binding affinity for a specific molecular target. QSAR modeling is also applied to better comprehend general phenomena such as pharmacokinetics and toxicity-related end points, which generally are standard measurements and for which there are available large datasets [4].Complementarily, although animal testing is still considered crucial to the evaluation of chemical safety, toxicity testing is moving toward a greater understanding of the disease at multiple biological levels, so as to develop alternative methods [5,6]. From the biological point of view, various models representing several layers of human physiology and metabolism, both healthy and disease based, have been developed. In order to address general problems, such as skin sensitization, hepatotoxicity and DNA harming agents, some research groups have built local QSAR models using mechanistic information [6,7]. However, in the future, we can easily expect that improvements in the validation techniques and data quality will follow the growth in the data generation, leading to broader QSAR models.There are several relevant articles in the QSAR field describing methods and validation techniques [8], besides extensive literature on troubleshooting, which makes this field still highly attractive for research and

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“…The two-dimensional quantitative structure-activity relationship (2D-QSAR) method has been applied to build prediction models of toxicity by determining the physical and chemical properties of chemical compounds from their chemical structures [29][30][31][32][33]. However, in conventional QSAR analysis, there are some problems concerning limited prediction performance [34][35][36][37]. Recently, QSAR analysis using the deep neural network (DNN) has shown superior prediction performance compared with other conventional machine learning (ML) methods [38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap–Deep Learning

et al. 2020

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The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that senses environmental exogenous and endogenous ligands or xenobiotic chemicals. In particular, exposure of the liver to environmental metabolism-disrupting chemicals contributes to the development and propagation of steatosis and hepatotoxicity. However, the mechanisms for AhR-induced hepatotoxicity and tumor propagation in the liver remain to be revealed, due to the wide variety of AhR ligands. Recently, quantitative structure–activity relationship (QSAR) analysis using deep neural network (DNN) has shown superior performance for the prediction of chemical compounds. Therefore, this study proposes a novel QSAR analysis using deep learning (DL), called the DeepSnap–DL method, to construct prediction models of chemical activation of AhR. Compared with conventional machine learning (ML) techniques, such as the random forest, XGBoost, LightGBM, and CatBoost, the proposed method achieves high-performance prediction of AhR activation. Thus, the DeepSnap–DL method may be considered a useful tool for achieving high-throughput in silico evaluation of AhR-induced hepatotoxicity.

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Descriptors and their selection methods in QSAR analysis: paradigm for drug design

Cited by 241 publications

References 73 publications

Ecotoxicological Analysis of Emerging Contaminants from Wastewater Discharges in The Coastal Zone of Cihuatlán (Jalisco, Mexico)

Ecotoxicological Analysis of Emerging Contaminants from Wastewater Discharges in The Coastal Zone of Cihuatlán (Jalisco, Mexico)

Current Trends in Quantitative Structure–Activity Relationship Validation and Applications On Drug Discovery

Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap–Deep Learning

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