Quantitative structure–activity relationship

Global Quantitative Structure-Activity Relationship Market Report 2021-2027 - Growing Adoption of QSAR and Artificial Intelligence in Drug Discovery - ResearchAndMarkets.com

Retrieved on: 
Friday, April 30, 2021
Software, Technology, Pharmaceutical, Health, Medicinal chemistry, Natural sciences, Physical sciences, Chemistry, Drug discovery, Computational chemistry, Cheminformatics, Quantitative structure–activity relationship, Structure–activity relationship, Partition coefficient, Drug design, Pharmaceutical industry in China

However, low adoption rate of the technique in emerging countries is hindering the quantitative structure-activity relationship market growth.\nBased on application, the quantitative structure-activity relationship market is segmented into drug discovery, molecular modeling, chemical screening, regulatory and decision-making, and other applications.

Key Points: 
  • However, low adoption rate of the technique in emerging countries is hindering the quantitative structure-activity relationship market growth.\nBased on application, the quantitative structure-activity relationship market is segmented into drug discovery, molecular modeling, chemical screening, regulatory and decision-making, and other applications.
  • According to Lipinski\'s Rule of Five, the prediction of partition coefficient log P is an important measure used in identifying "drug likeness."
  • .\nQSARs have a substantial role in toxicity prediction, drug design, and environmental fate modeling of food & beverages, chemicals, and pharmaceuticals.
  • However, the high demand for promising tools for rapid and accurate drugs and vaccine development has boosted the demand for QSAR, thereby fueling the market growth.\n'

Drug Discovery Informatics Market Size Worth $5.63 Billion By 2028: Grand View Research, Inc.

Retrieved on: 
Monday, February 22, 2021
Health sciences, Drug discovery, Pharmaceutical sciences, Natural sciences, Cheminformatics, Computational chemistry, Medicinal chemistry, Quantitative structure–activity relationship

Companies operating in the market are receiving funding for expanding their drug discovery platforms, further supplementing the market growth.

Key Points: 
  • Companies operating in the market are receiving funding for expanding their drug discovery platforms, further supplementing the market growth.
  • In addition, the QSAR models helped in the data mining of molecules for rapid Covid-19 drug discovery.
  • Hence, the need to facilitate drug development for Covid-19 is expected to propel the industry expansion over the coming years.
  • Grand View Research has segmented the global drug discovery informatics market on the basis of workflow, mode, services, and region:
    Drug Discovery Informatics Workflow Outlook (Revenue, USD Million, 2017 - 2028)

Drug Discovery Informatics Market Size Worth $5.63 Billion By 2028: Grand View Research, Inc.

Retrieved on: 
Monday, February 22, 2021
Health sciences, Drug discovery, Pharmaceutical sciences, Natural sciences, Cheminformatics, Computational chemistry, Medicinal chemistry, Quantitative structure–activity relationship

Companies operating in the market are receiving funding for expanding their drug discovery platforms, further supplementing the market growth.

Key Points: 
  • Companies operating in the market are receiving funding for expanding their drug discovery platforms, further supplementing the market growth.
  • In addition, the QSAR models helped in the data mining of molecules for rapid Covid-19 drug discovery.
  • Hence, the need to facilitate drug development for Covid-19 is expected to propel the industry expansion over the coming years.
  • Grand View Research has segmented the global drug discovery informatics market on the basis of workflow, mode, services, and region:
    Drug Discovery Informatics Workflow Outlook (Revenue, USD Million, 2017 - 2028)

New AI-Enabled Virtual Screening Tool Identifies Most Promising Approved Drugs for Success in Treating COVID-19

Retrieved on: 
Thursday, February 18, 2021
Natural sciences, Health sciences, Drug discovery, Physical sciences, Medicinal chemistry, Medical tests, Computational chemistry, Quantitative structure–activity relationship, Virtual screening, Screening, Structure–activity relationship, Virus

The goal of this project was to create a virtual screening tool model that can be replicated by outside researchers and biopharmaceutical companies to quickly identify compounds to treat COVID-19 and other diseases.

Key Points: 
  • The goal of this project was to create a virtual screening tool model that can be replicated by outside researchers and biopharmaceutical companies to quickly identify compounds to treat COVID-19 and other diseases.
  • Using a well-studied medicinal chemistry tool known as quantitative structure activity relationships, or QSAR, the team evolved the model from human-led input.
  • "This new AI-enabled virtual screening tool is a quicker, more assured path to helping medical researchers identify cures across disease states, including other emerging viruses.
  • With more than 110 years' experience, no one knows more about scientific information than CAS.For more information, visit www.cas.org .

Detailed Insights into In Silico/Computer-Aided Drug Discovery Services Market to 2030 - Elaborate Profiles of Key Industry Players - ResearchAndMarkets.com

Retrieved on: 
Thursday, March 26, 2020
Pharmaceutical, Science, Health, Other Science, Health sciences, Natural sciences, Pharmaceutical sciences, Medicinal chemistry, Computational chemistry, Alternatives to animal testing, In silico, Drug design, Quantitative structure–activity relationship, Drug discovery, Pharmaceutical bioinformatics, Cheminformatics

The "In Silico / Computer-Aided Drug Discovery Services Market: Focus on Large Molecules" report has been added to ResearchAndMarkets.com's offering.

Key Points: 
  • The "In Silico / Computer-Aided Drug Discovery Services Market: Focus on Large Molecules" report has been added to ResearchAndMarkets.com's offering.
  • Over time, the complexities of drug discovery, have increased; this is especially true for large molecules, which are inherently more complex than conventional small molecule drugs.
  • Currently, there are several in silico approaches available for the drug discovery process alone, such as structure based drug design, fragment based drug discovery and ligand based drug discovery.
  • As a result, companies offering in silico drug discovery services, such as computer-aided drug design (CADD), molecular modeling and quantitative structure-activity relationship (QSAR), have now become an important part of the pharmaceutical industry.

PsychoGenics Announces Milestones Associated with Sunovion’s Advancing Pipeline

Retrieved on: 
Wednesday, October 30, 2019
General Health, Health, Clinical trials, Health sciences, Medicinal chemistry, Pharmaceutical sciences, Natural sciences, Cheminformatics, Drug discovery, Computational chemistry, Pharmacognosy, Sunovion, Structure–activity relationship, Quantitative structure–activity relationship, Chemist, SmartCube®–driven Drug Discovery, PsychoGenics Inc. and PGI Drug Discovery LLC

PsychoGenics and Sunovion initiated a collaboration in August 2007, with PsychoGenics evaluating a library of Sunovion compounds.

Key Points: 
  • PsychoGenics and Sunovion initiated a collaboration in August 2007, with PsychoGenics evaluating a library of Sunovion compounds.
  • SEP-378614 and SEP-380135 were optimized for in vivo activity by Sunovion medicinal chemists based on quantitative structure-activity relationship analysis, in collaboration with PsychoGenics.
  • PsychoGenics is entitled to receive payments upon Sunovions achievement of certain development milestones for SEP-363856, SEP-378608, SEP-378614 and SEP-380135, as well as, royalty payments for any sales of these compounds.
  • PsychoGenics Inc. and its discovery arm PGI Drug Discovery LLC (collectively known as PsychoGenics) have pioneered the translation of rodent behavioral and physiological responses into robust, high-throughput and high-content phenotyping.