In Silico Quantitative Structure Toxicity Relationship of Chemical Compounds: Some Case Studies

Deeb, Omar; Goodarzi, Mohammad

doi:10.2174/157488612804096533

Cited by 41 publications

(19 citation statements)

References 35 publications

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“…There are various methods for generating models to predict toxicity endpoints, including structural alerts (SAs) and rule-based models; read-across (RA), dose–response (DR), and time–response (TR) models; pharmacokinetic (PK) and pharmacodynamic (PD) models; uncertainty factors (UFs) models; and the quantitative structure–activity relationship (QSAR) model. 93–95 …”

Section: Risk Assessment and Control Bandingmentioning

confidence: 99%

Nanoparticles in Daily Life: Applications, Toxicity and Regulations

Gupta

Xie

2018

J Environ Pathol Toxicol Oncol

416

188

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At nanoscale, man-made materials may show unique properties that differ from bulk and dissolved counterparts. The unique properties of engineered nanomaterials not only impart critical advantages but also confer toxicity because of their unwanted interactions with different biological compartments and cellular processes. In this review, we discuss various entry routes of nanomaterials in the human body, their applications in daily life, and the mechanisms underlying their toxicity. We further explore the passage of nanomaterials into air, water, and soil ecosystems, resulting in diverse environmental impacts. Briefly, we probe the available strategies for risk assessment and risk management to assist in reducing the occupational risks of potentially hazardous engineered nanomaterials including the control banding (CB) approach. Moreover, we substantiate the need for uniform guidelines for systematic analysis of nanomaterial toxicity, in silico toxicological investigations, and obligation to ensure the safe disposal of nanowaste to reduce or eliminate untoward environmental and health impacts. At the end, we scrutinize global regulatory trends, hurdles, and efforts to develop better regulatory sciences in the field of nanomedicines.

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Section: Risk Assessment and Control Bandingmentioning

confidence: 99%

Nanoparticles in Daily Life: Applications, Toxicity and Regulations

Gupta

Xie

2018

J Environ Pathol Toxicol Oncol

416

188

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“…Several types of molecular descriptors can be used to describe chemicals as summarized in Supplementary Table S3. Therefore, feature selection algorithms based on, for example, simulated annealing, genetic algorithm, or principal component analysis can be used . If there are a small number of descriptors, using two‐dimensional scatter plots of each descriptor versus the biological activity can help identify significant descriptors (Figure ).…”

Section: In Silico Modeling Methodsmentioning

confidence: 99%

“…There are several types of algorithms to generate QSAR models: linear models such as those based on linear regression analysis, multiple linear regression and partial least squares for continuous endpoints, and linear discriminant analysis for categorical endpoints; nonlinear models such as artificial neural networks or support vector machines; and data‐driven models such as those based on decision trees, clustering, Naïve Bayes, and K‐nearest neighbor …”

Section: In Silico Modeling Methodsmentioning

confidence: 99%

“…Examples of QSARs for predicting toxicity of aromatic nitro compounds, nitrobenzene compounds, cytotoxicity of TIBO derivatives, and carcinogenicity of sulfa drugs are explained in ref. . A discussion of the performance of QSAR models to predict carcinogenicity, mutagenicity, reproductive, and developmental toxicity endpoints are available in ref.…”

Section: In Silico Modeling Methodsmentioning

confidence: 99%

“…In silico toxicology (computational toxicology) is one type of toxicity assessment that uses computational resources (i.e., methods, algorithms, software, data, etc.) to organize, analyze, model, simulate, visualize, or predict toxicity of chemicals . It is intertwined with in silico pharmacology, which uses information from computational tools to analyze beneficial or adverse effects of drugs for therapeutic purposes …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

In silico toxicology: computational methods for the prediction of chemical toxicity

Raies

Bajic

2016

WIREs Comput Mol Sci

570

321

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Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late-stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models.

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DG‐GL: Differential geometry‐based geometric learning of molecular datasets

Nguyen

Wei

2019

Numer Methods Biomed Eng

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Motivation: Despite its great success in various physical modeling, differential geometry (DG) has rarely been devised as a versatile tool for analyzing large, diverse, and complex molecular and biomolecular datasets because of the limited understanding of its potential power in dimensionality reduction and its ability to encode essential chemical and biological information in differentiable manifolds. Results: We put forward a differential geometry-based geometric learning (DG-GL) hypothesis that the intrinsic physics of three-dimensional (3D) molecular structures lies on a family of low-dimensional manifolds embedded in a high-dimensional data space. We encode crucial chemical, physical, and biological information into 2D element interactive manifolds, extracted from a high-dimensional structural data space via a multiscale discrete-to-continuum mapping using differentiable density estimators. Differential geometry apparatuses are utilized to construct element interactive curvatures in analytical forms for certain analytically differentiable density estimators. These low-dimensional differential geometry representations are paired with a robust machine learning algorithm to showcase their descriptive and predictive powers for large, diverse, and complex molecular and biomolecular datasets. Extensive numerical experiments are carried out to demonstrate that the proposed DG-GL strategy outperforms other advanced methods in the predictions of drug discovery-related protein-ligand binding affinity, drug toxicity, and molecular solvation free energy.

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In Silico Quantitative Structure Toxicity Relationship of Chemical Compounds: Some Case Studies

Cited by 41 publications

References 35 publications

Nanoparticles in Daily Life: Applications, Toxicity and Regulations

Nanoparticles in Daily Life: Applications, Toxicity and Regulations

In silico toxicology: computational methods for the prediction of chemical toxicity

DG‐GL: Differential geometry‐based geometric learning of molecular datasets

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