The Use of Qsar and Computational Methods in Drug Design

Bajot, Fania

doi:10.1007/978-1-4020-9783-6_9

Cited by 11 publications

(8 citation statements)

References 86 publications

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“…Such an approach is notably used in drug design to optimize the molecular structure of a compound that has been identified in the virtual screening of databases of existing chemical products. In such cases, analog structures are generated by applying structural modifications on the molecular scaffold of the selected structure …”

Section: Proposed Uses Of Qspr Models In the Context Of Green Chemistrymentioning

confidence: 99%

“…In such approaches, QSPR models can not only give predictions but, much more, they can also give structural trends toward (better) target properties and lower hazards. For such an inverse exercise, a current strategy (originated also from drug design) 16,58 is to generate a virtual combinatorial library of structures in a defi ned chemical space and to screen it using computational methods to predict the target specifi cations. Th en, the most relevant compounds are further investigated for the fi nal application, as shown in Fig.…”

Section: Qspr Models As Screening Toolsmentioning

confidence: 99%

“…In such cases, analog structures are generated by applying structural modifi cations on the molecular scaff old of the selected structure. 16 As detailed in the previous section, the target specifications can be defi ned not only in terms of functional properties but also in terms of hazards. In the case of substitution studies, the properties of the compound to be substituted give in general the main expectations both for the required properties and the undesired hazards that represent the motivation for searching for an alternative substance.…”

Section: Qspr Models As Screening Toolsmentioning

confidence: 99%

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How to use QSPR‐type approaches to predict properties in the context of Green Chemistry

Fayet

Rotureau

2016

Biofuels Bioprod Bioref

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Green Chemistry is an active field of chemical research in which the ultimate targets are to promote high‐value, safe, clean products from renewable resources using inherently safer and clean processes. Until recently, the main efforts focused on the production of chemicals based on renewable resources by cleaner processes, notably by eliminating or substituting solvents. Quantitative Structure‐Property Relationships (QSPR) offer the opportunity to also take into account safety issues (in particular fire and explosion risks) in the early steps of development of chemicals and processes in the context of Green Chemistry. Based on robust methods for their development and validation, these predictive approaches allow access to the properties of substances based only on the knowledge of their molecular structures, even before their synthesis. QSPR models have been developed for various kinds of properties, including physico‐chemical hazards, for diverse families of compounds. They can be used as virtual screening tools to identify the best candidate among a series of possible compounds (within databases of products) for a target application and even in computer‐aided molecular design to propose alternative molecular structures, for instance for substitution purposes. So they represent very relevant tools to take into account the physico‐chemical hazards of substances together with targeted functional properties from the early stages of R&D projects toward safe‐by‐design “green” products and processes. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd

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Section: Proposed Uses Of Qspr Models In the Context Of Green Chemistrymentioning

confidence: 99%

Section: Qspr Models As Screening Toolsmentioning

confidence: 99%

Section: Qspr Models As Screening Toolsmentioning

confidence: 99%

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How to use QSPR‐type approaches to predict properties in the context of Green Chemistry

Fayet

Rotureau

2016

Biofuels Bioprod Bioref

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“…The programmatic access to PubChem database can be accomplished through web API packages such as PUB-REST and PyPubChem by which data mining for a particular task can be automated by programmatic access to the database using python commands [2,3]. Statistically predicted drug leads can be generated from a QSAR model for a particular pharmacological activity [4][5][6][7]. However the data sets used in QSAR modelling has to be curated each time by researchers' before carrying out the study.…”

Section: Introductionmentioning

confidence: 99%

A program to automate the discovery of drugs for West Nile and Dengue virus – programmatic screening of over a billion compounds on PubChem, generation of drug leads and automatedIn Silicomodelling

S¹,

Sanker

Madaj³

et al. 2020

Preprint

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Our work is composed of a python program for programmatic data mining of PubChem to collect data to implement a machine learning based AutoQSAR algorithm to generate drug leads for the flaviviruses -Dengue and West Nile. The drug leads generated by the program are feed as programmatic inputs to AutoDock Vina package for automated In Silico modelling of interaction between the compounds generated as drug leads by the program and the chosen Dengue and West Nile drug target methyltransferase, whose inhibition leads to the control of viral replication. The machine learning based AutoQSAR algorithm involves feature selection, QSAR modelling, validation and prediction. The drug leads generated each time the program is run is reflective of the constantly growing PubChem database is an important dynamic feature of the program which facilitates fast and dynamic drug lead generation against the West Nile and Dengue virus in way which is reflective of the constantly growing PubChem database. The program prints out the top drug leads after screening PubChem library which is over a billion compounds. The leads generated by the program are fed as programmatic inputs to an In Silico modelling package. The interaction of top drug lead compounds generated by the program and drug targets of West Nile and Dengue virus, was modelled in an automated way through programmatic commands. Thus our program ushers in a new age of automatic ease in the virtual drug screening and drug identification through programmatic data mining of chemical data libraries and drug lead generation through machine learning based AutoQSAR algorithm and an automated In Silico

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“…PubChem is a data repository of chemical compounds, their properties and biological activities [1] which can be programmatically accessed through web API packages such as PUB-REST and python web scrapping techniques [2,3]. Quantitative Structure-Activity Relationship(QSAR) studies are statistical based studies through which drug leads are generated which provide cost cutting advantages in testing and drug discovery for the pharmaceutical industry [4][5][6][7]. However the data set required to perform a QSAR study is curated by researchers before performing the statistically study.…”

Section: Introductionmentioning

confidence: 99%

A programmatic tool for automatic ease in coronavirus drug discovery through programmatically automated data mining, QSAR and In Silico modelling

Madaj²,

Sanker³

et al. 2020

Preprint

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<div><p>The work is composed of python based programmatic tool that automates the workflow of drug discovery for coronavirus. Firstly, the python program is written to automate the process of data mining PubChem database to collect data required to perform a machine learning based AutoQSAR algorithm through which drug leads for coronavirus are generated. The data acquisition from PubChem was carried out through python web scrapping techniques. The workflow of the machine learning based AutoQSAR involves feature learning and descriptor selection, QSAR modelling, validation and prediction. The drug leads generated by the program are required to satisfy the Lipinski’s drug likeness criteria as compounds that satisfy Lipinski’s criteria are likely to be an orally active drug in humans. Drug leads generated by the program are fed as programmatic inputs to an In Silico modelling package to computer model the interaction of the compounds generated as drug leads and two coronavirus drug targets identified with their PDB ID : 6W9C and 1P9U. The results are stored in the working folder of the user. The program also generates protein-ligand interaction profiling and stores the visualized images in the working folder of the user. Thus our programmatic tool ushers in the new age automatic ease in drug identification for coronavirus through a fully automated QSAR and an automated In Silico modelling of the drug leads generated by the autoQSAR algorithm.<br><br></p><p>The program is hosted, maintained and supported at the GitHub repository link given below</p><p><a href="https://github.com/bengeof/Programmatic-tool-to-automate-the-drug-discovery-workflow-for-coronavirus">https://github.com/bengeof/Programmatic-tool-to-automate-the-drug-discovery-workflow-for-coronavirus</a></p></div>

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The Use of Qsar and Computational Methods in Drug Design

Cited by 11 publications

References 86 publications

How to use QSPR‐type approaches to predict properties in the context of Green Chemistry

How to use QSPR‐type approaches to predict properties in the context of Green Chemistry

A program to automate the discovery of drugs for West Nile and Dengue virus – programmatic screening of over a billion compounds on PubChem, generation of drug leads and automatedIn Silicomodelling

A programmatic tool for automatic ease in coronavirus drug discovery through programmatically automated data mining, QSAR and In Silico modelling

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