QSAR Approaches and Ecotoxicological Risk Assessment

Hamadache, Moussa; Benkortbi, Othmane; Amrane, Abdeltif; Hanini, Salah

doi:10.1007/978-1-0716-0150-1_25

Cited by 5 publications

(7 citation statements)

References 158 publications

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“…Propagation of output signals from hidden layers and output prediction based on the recognized pattern in the output layer are determined by activation functions used in these layers. 25 In QSAR, MLPs were used in the prediction of toxicity, 26 bioactivity for antibreast cancer drug development, 27 blood− brain barrier permeability for discovery of CNS (central nervous system) therapeutic drugs, 28 and so forth. Rings, cyclic structures, and other molecular patterns are encompassed in the SMILES representation.…”

Section: Discussionmentioning

confidence: 99%

Identifying High-Quality Leads among Screened Anticancerous Compounds Using SMILES Representations

Peter,

Kalakoti,

Sundar

2024

ACS Omega

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Cancer is a lethal disease that affects numerous people worldwide. Chemotherapy stands as one of the most effective treatment regimens to combat cancer. Nevertheless, anticancer drugs face a high failure rate due to safety and efficacy issues. Drug failure could be subdued by instigating drug leads with reduced toxicity and enhanced efficacy. Computer-aided drug discovery endorses drug leads in manoeuvring protein and ligand structures or representations. Simplified molecular input line entry system (SMILES) is a linear notation representing the threedimensional structure of a molecule using symbols and alphanumeric characters. SMILES representation hoards rings and scaffold structures in its depiction. Mining ring and scaffold patterns from molecular SMILES would assist in ascertaining biological properties based on molecular patterns. Moreover, the emergence of artificial intelligence (AI) technologies would accelerate identification of efficient anticancer drug leads. AI algorithms proclaimed for their pattern recognition ability could be employed for identifying molecular patterns from SMILES representation, thereby enabling property prediction. Consequently, we developed a multilayer perceptron (MLP) model for the prediction of anticancer activity using SMILES of NCI-60 cancer growth inhibition data. Furthermore, the top 8 frequent scaffolds were identified on preliminary analysis of cancer growth inhibition data and ChEMBL drugs. The developed MLP model classified anticancer and nonanticancer compounds with a classification accuracy of 0.92. Also, benchmarking of the developed model with machine learning algorithms exhibited better performance of the MLP model.

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Section: Discussionmentioning

confidence: 99%

Identifying High-Quality Leads among Screened Anticancerous Compounds Using SMILES Representations

Peter,

Kalakoti,

Sundar

2024

ACS Omega

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“…[34] Literature survey reveals that various studies have been devoted to the development of a single task QSAR/QSTR models [35] for environmental fate or toxicity for a particular class of insecticides or a large diverse datasets. [36][37][38] In the assessment of environmental fate and toxicological properties of insecticides, almost all prior studies have two remarkable disadvantages. First, the in silico methodologies employed for the prediction of the target property, generally use small databases of analogous compounds.…”

Section: Introductionmentioning

confidence: 99%

“…Using the aforementioned techniques, the development of QSAR models for the prediction of environmental fate and ecotoxicological parameters of chemicals has generated a lot of attention [34] . Literature survey reveals that various studies have been devoted to the development of a single task QSAR/QSTR models [35] for environmental fate or toxicity for a particular class of insecticides or a large diverse datasets [36–38] . In the assessment of environmental fate and toxicological properties of insecticides, almost all prior studies have two remarkable disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Predictionof Environmental FateandToxicityofInsecticidesUsing Multi‐Target QSAR Approach

Pandey

2023

Chemistry & Biodiversity

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Ecotoxicological risk assessments form the foundation of regulatory decisions for industrial chemicals used in various sectors. In this study, a multi‐target‐QSAR model established by a backpropagation neural network trained with the Levenberg‐Marquardt (LM) algorithm was used to construct a statistically robust and easily interpretable Mt‐QSAR model with high external predictability for the simultaneous prediction of the environmental fate in form of octanol‐water partition coefficient (LogP), (BCF) and acute oral toxicity in mammals and birds (LD50rat) and (LD50bird) for a wide range of chemical structural classes of insecticides. Principal component analysis was performed on descriptors selected by the SW‐MLR method, and the selected PCs were used for constructing the SW‐MLR‐PCA‐ANN model. The developed well‐trained model (RMSE=0.83, MPE=0.004, CCC=0.82, IIC=0.78, R2=0.69) was statistically robust as indicated by the external validation parameters (RMSE=0.93, MPE=0.008, CCC=0.77, IIC=0.68, R2=0.61). The AD of the developed Mt‐QSAR model was also defined to identify the most reliable predictions. Finally, the missing values in the dataset for the aforementioned targets were predicted using the constructed Mt‐QSAR model. The proposed approach can be used for simultaneous prediction of the environmental fate of new insecticides, especially ones that haven′t been tested yet.

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“…[38][39][40] Moreover, the practical applications of a combined use of QSAR analysis, principal component analysis (PCA), regression analysis, and computational modelling in rational drug design has been extensively demonstrated. [41][42][43][44][45][46][47][48] We, therefore explored the possible combined use of QSAR, PCA, regression analysis and computational modelling for rational design of ATX inhibitors with potential LPAR1 inhibitory activity in this study.…”

Section: Introductionmentioning

confidence: 99%

Molecular modelling guided design, synthesis and QSAR analysis of new small molecule non-lipid autotaxin inhibitors

Banerjee

Norman

Deng

et al. 2020

Bioorganic Chemistry

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The lysophospholipase D autotaxin (ATX) generates lysophosphatidic acid (LPA) that activates six cognate G-protein coupled receptors (GPCR) in cancerous cells, promoting their motility and invasion. Four novel compounds were generated aided by molecular docking guided design and synthesis techniques to obtain new dual inhibitors of ATX and the lysophosphatidic acid receptor subtype 1 (LPAR1). Biological evaluation of these compounds revealed two compounds, 10 and 11, as new ATX enzyme inhibitors with potencies in the range of 218-220 nM and water solubility (> 100 μg/mL), but with no LPAR1 inhibitory activity. A QSAR model was generated that included four newly designed compounds and twenty-one additional compounds that we have reported previously. The QSAR model provided excellent predictability of the pharmacological activity and potency among structurally related drug candidates. This model will be highly useful in guiding the synthesis of new ATX inhibitors in the future.

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QSAR Approaches and Ecotoxicological Risk Assessment

Cited by 5 publications

References 158 publications

Identifying High-Quality Leads among Screened Anticancerous Compounds Using SMILES Representations

Identifying High-Quality Leads among Screened Anticancerous Compounds Using SMILES Representations

Predictionof Environmental FateandToxicityofInsecticidesUsing Multi‐Target QSAR Approach

Molecular modelling guided design, synthesis and QSAR analysis of new small molecule non-lipid autotaxin inhibitors

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