Background of QSAR and Historical Developments

Roy, Kunal; Kar, Supratik; Das, Rudra Narayan

doi:10.1016/b978-0-12-801505-6.00001-6

Cited by 34 publications

(26 citation statements)

References 37 publications

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“…In this work we apply instance-based and feature-based MTL for the problem of predicting quantitative structure activity relationship (QSAR). The goal of QSAR learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the compound against an assay (a test that predicts the potential of the compound being a drug) [8].…”

Section: Introduction and Problem Specificationmentioning

confidence: 99%

Multi-task learning with a natural metric for quantitative structure activity relationship learning

et al. 2019

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The goal of quantitative structure activity relationship (QSAR) learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the compound. We employed multi-task learning (MTL) to exploit commonalities in drug targets and assays. We used datasets containing curated records about the activity of specific compounds on drug targets provided by ChEMBL. Totally, 1091 assays have been analysed. As a baseline, a single task learning approach that trains random forest to predict drug activity for each drug target individually was considered. We then carried out feature-based and instance-based MTL to predict drug activities. We introduced a natural metric of evolutionary distance between drug targets as a measure of tasks relatedness. Instance-based MTL significantly outperformed both, feature-based MTL and the base learner, on 741 drug targets out of 1091. Feature-based MTL won on 179 occasions and the base learner performed best on 171 drug targets. We conclude that MTL QSAR is improved by incorporating the evolutionary distance between targets. These results indicate that QSAR learning can be performed effectively, even if little data is available for specific drug targets, by leveraging what is known about similar drug targets.

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Section: Introduction and Problem Specificationmentioning

confidence: 99%

Multi-task learning with a natural metric for quantitative structure activity relationship learning

et al. 2019

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“…In this mechanism, the electronegative atoms (e.g. nitrogen, oxygen, and chlorine) found in the neutral molecules may attract the electron cloud via a covalent bond with a less electronegative neighbouring atom, thereby enhancing the molecule's dipolar nature (Roy et al 2015 ). When the positive end of one molecule is aligned with the negative end of another, the partial positive (δ+) and negative (δ−) charges within the molecule are stimulated.…”

Section: Environmental Remediationmentioning

confidence: 99%

“…When nonpolar species enter the system, they impose a constraint on the water molecules, limiting their freedom of orientation. Therefore, the undesirable interaction is avoided by limiting the interaction of water molecules with the nonpolar surface, which results in the aggregation of nonpolar pollutants and hydrophobic biochar in an aqueous medium (Roy et al 2015 ). According to Zhou et al, the removal of ciprofloxacin and sparfloxacin using iron(II, III) oxide/graphene-modified biochar was significantly attributed to hydrophobic interaction due to the increased hydrophobicity, which resulted in a strong interaction between the adsorbate and adsorbent surface (Zhou et al 2019a ).…”

Section: Environmental Remediationmentioning

confidence: 99%

“…One of two organic molecules is aromatic, which means it contains π-electrons and forms a complex with the other molecule via non-covalent bonding forces. Other non-covalent bonding forces that compromise the π system include the n–π interaction, in which ions act as electron donors and the aromatic ring on the biochar surface acts as an electron acceptor (Roy et al 2015 ). According to Dong et al ( 2018 ), the aromatic sheets found in magnetic biochar nanoparticles facilitated the removal of 17β-estradiol via π–π interactions.…”

Section: Environmental Remediationmentioning

confidence: 99%

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Biochar for agronomy, animal farming, anaerobic digestion, composting, water treatment, soil remediation, construction, energy storage, and carbon sequestration: a review

et al. 2022

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In the context of climate change and the circular economy, biochar has recently found many applications in various sectors as a versatile and recycled material. Here, we review application of biochar-based for carbon sink, covering agronomy, animal farming, anaerobic digestion, composting, environmental remediation, construction, and energy storage. The ultimate storage reservoirs for biochar are soils, civil infrastructure, and landfills. Biochar-based fertilisers, which combine traditional fertilisers with biochar as a nutrient carrier, are promising in agronomy. The use of biochar as a feed additive for animals shows benefits in terms of animal growth, gut microbiota, reduced enteric methane production, egg yield, and endo-toxicant mitigation. Biochar enhances anaerobic digestion operations, primarily for biogas generation and upgrading, performance and sustainability, and the mitigation of inhibitory impurities. In composts, biochar controls the release of greenhouse gases and enhances microbial activity. Co-composted biochar improves soil properties and enhances crop productivity. Pristine and engineered biochar can also be employed for water and soil remediation to remove pollutants. In construction, biochar can be added to cement or asphalt, thus conferring structural and functional advantages. Incorporating biochar in biocomposites improves insulation, electromagnetic radiation protection and moisture control. Finally, synthesising biochar-based materials for energy storage applications requires additional functionalisation.

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“…Bioinformatics applications in drug design uses the computer-aided drug design (CADD) method that integrates the methods of lead compound Quantitative Structure-Activity Relationship (QSAR) optimization, sequence, structural homology, stereo-chemical validation, molecular docking, and 2-dimensonal (2D) molecular interaction examination. 57 - 59 …”

Section: The Role Of Bioinformatics In Drug Design For Covid-19 Treatmentioning

confidence: 99%

The Relevance of Bioinformatics Applications in the Discovery of Vaccine Candidates and Potential Drugs for COVID-19 Treatment

Chukwudozie

Duru

Ndiribe

et al. 2021

Bioinform Biol Insights

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The application of bioinformatics to vaccine research and drug discovery has never been so essential in the fight against infectious diseases. The greatest combat of the 21st century against a debilitating disease agent SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus discovered in Wuhan, China, December 2019, has piqued an unprecedented usage of bioinformatics tools in deciphering the molecular characterizations of infectious pathogens. With the viral genome data of SARS-COV-2 been made available barely weeks after the reported outbreak, bioinformatics platforms have become an all-time critical tool to gain time in the fight against the disease pandemic. Before the outbreak, different platforms have been developed to explore antigenic epitopes, predict peptide-protein docking and antibody structures, and simulate antigen-antibody reactions and lots more. However, the advent of the pandemic witnessed an upsurge in the application of these pipelines with the development of newer ones such as the Coronavirus Explorer in the development of efficacious vaccines, drug repurposing, and/or discovery. In this review, we have explored the various pipelines available for use, their relevance, and limitations in the timely development of useful therapeutic candidates from genomic data knowledge to clinical therapy.

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Background of QSAR and Historical Developments

Cited by 34 publications

References 37 publications

Multi-task learning with a natural metric for quantitative structure activity relationship learning

Multi-task learning with a natural metric for quantitative structure activity relationship learning

Biochar for agronomy, animal farming, anaerobic digestion, composting, water treatment, soil remediation, construction, energy storage, and carbon sequestration: a review

The Relevance of Bioinformatics Applications in the Discovery of Vaccine Candidates and Potential Drugs for COVID-19 Treatment

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