Quantitative Structure-Activity Relationship (QSAR): Modeling Approaches to Biological Applications

Peter, Swathik Clarancia; Dhanjal, Jaspreet Kaur; Malik, Vidhi; Radhakrishnan, Navaneethan; Mannu, Jayakanthan; Sundar, Durai

doi:10.1016/b978-0-12-809633-8.20197-0

Cited by 61 publications

(40 citation statements)

References 105 publications

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“…A combination of theoretical calculations and statistical analysis are utilized based on the QSAR model to identify the relationship between molecular descriptors and biological activities or physicochemical features of compounds. OSAR method can be generally classified based on the dimensions of the molecular descriptors, and also, type of the predicted biological activity (Y. Liu et al, 2019; Peter et al, 2019; Roy et al, 2015).…”

Section: Computational Methods For Biosensor Designmentioning

confidence: 99%

Recent advances in computational methods for biosensor design

Khoshbin

Housaindokht

Izadyar

et al. 2020

Biotech & Bioengineering

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Biosensors are analytical tools with a great application in healthcare, food quality control, and environmental monitoring. They are of considerable interest to be designed by using cost‐effective and efficient approaches. Designing biosensors with improved functionality or application in new target detection has been converted to a fast‐growing field of biomedicine and biotechnology branches. Experimental efforts have led to valuable successes in the field of biosensor design; however, some deficiencies restrict their utilization for this purpose. Computational design of biosensors is introduced as a promising key to eliminate the gap. A set of reliable structure prediction of the biosensor segments, their stability, and accurate descriptors of molecular interactions are required to computationally design biosensors. In this review, we provide a comprehensive insight into the progress of computational methods to guide the design and development of biosensors, including molecular dynamics simulation, quantum mechanics calculations, molecular docking, virtual screening, and a combination of them as the hybrid methodologies. By relying on the recent advances in the computational methods, an opportunity emerged for them to be complementary or an alternative to the experimental methods in the field of biosensor design.

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Section: Computational Methods For Biosensor Designmentioning

confidence: 99%

Recent advances in computational methods for biosensor design

Khoshbin

Housaindokht

Izadyar

et al. 2020

Biotech & Bioengineering

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“…According to the dimensions of molecular descriptors used for model generation, QSAR methods can be classified into several classes of modeling, such as 1D, 2D, 3D, 4D, 5D, and so forth [ 85 ]. In most cases, 2D- and 3D-QSAR studies are commonly used to evaluate the series of chemical compounds [ 86 , 87 , 88 ].…”

Section: Category Of In Silico Modelsmentioning

confidence: 99%

Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing

Huang

Lee

Hsu

et al. 2021

IJMS

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Millions of experimental animals are widely used in the assessment of toxicological or biological effects of manufactured nanomaterials in medical technology. However, the animal consciousness has increased and become an issue for debate in recent years. Currently, the principle of the 3Rs (i.e., reduction, refinement, and replacement) is applied to ensure the more ethical application of humane animal research. In order to avoid unethical procedures, the strategy of alternatives to animal testing has been employed to overcome the drawbacks of animal experiments. This article provides current alternative strategies to replace or reduce the use of experimental animals in the assessment of nanotoxicity. The currently available alternative methods include in vitro and in silico approaches, which can be used as cost-effective approaches to meet the principle of the 3Rs. These methods are regarded as non-animal approaches and have been implemented in many countries for scientific purposes. The in vitro experiments related to nanotoxicity assays involve cell culture testing and tissue engineering, while the in silico methods refer to prediction using molecular docking, molecular dynamics simulations, and quantitative structure–activity relationship (QSAR) modeling. The commonly used novel cell-based methods and computational approaches have the potential to help minimize the use of experimental animals for nanomaterial toxicity assessments.

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“…In silico predictive modelling is a computational data-driven approach that can be applied for the prediction of adverse effects of MWCNTs, in the effort to reduce animal testing. This aligns with Annex XI 16 of Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation (European Parliament and Council 2006), which describes alternative (non-animal) approaches such as grouping and read-across, 17 Quantitative Structure-Activity Relationship (QSAR), 18 in vitro methods and weight of evidence, which can be used instead of in vivo tests to examine and evaluate the risks of exposure to chemicals.…”

Section: Introductionmentioning

confidence: 60%