Dublin, May 05, 2021 (GLOBE NEWSWIRE) -- The "Quantitative Structure-Activity Relationship Market Forecast to 2027 - COVID-19 Impact and Global Analysis by Application; Industry, and Geography" report has been added to ResearchAndMarkets.com's offering.
Quantitative Structure-Activity Relationship Market for Drug Discovery Segment to Grow at Highest CAGR during 2020-2027
Quantitative Structure-Activity Relationship (QSAR) Market is expected to reach US$ 1,888.5 million in 2027 from US$ 1,388.1 million in 2019, it is estimated to grow at a CAGR of 4.0% from 2020 to 2027.
The market growth is mainly attributed to the increasing adoption rate of modeling tools in drug discovery and rising investments for drug discovery. However, low adoption rate of the technique in emerging countries is hindering the quantitative structure-activity relationship market growth.
Based on application, the quantitative structure-activity relationship market is segmented into drug discovery, molecular modeling, chemical screening, regulatory and decision-making, and other applications. In 2019, the drug discovery segment accounted for the largest share, and it is further expected to register the highest CAGR in the market during the forecast period.
The drug discovery process often involves the use of QSAR to identify chemical structures that could have good inhibitory effects on specific targets and have low toxicity (non-specific activity). According to Lipinski's Rule of Five, the prediction of partition coefficient log P is an important measure used in identifying "drug likeness." .
QSARs have a substantial role in toxicity prediction, drug design, and environmental fate modeling of food & beverages, chemicals, and pharmaceuticals. Moreover, predictive QSAR models are used by different regulatory agencies to do the estimation of chemical, physical, and biological parameters of chemicals with the helps of specific applications that precisely performs the tasks of decision-making contexts in chemical safety assessment.
Ineffective drug targets are the main reason leading to the failure of various late-stage clinical trials. With the introduction of artificial intelligence (AI) in healthcare, numerous pharmaceutical companies have made investments in partnership agreements with software-based companies to develop better healthcare tools and technologies for avoiding drug failures.
For instance, Pfizer is using IBM Watson, a machine learning system, to enhance its search for immuno-oncology drugs. Sanofi has collaborated with Exscientia's artificial-intelligence (AI) platform, a UK-based start-up, to discover therapies to cure metabolic diseases. Genentech is enhancing its search for cancer treatments by using an AI-based system offered by GNS Healthcare. Therefore, most of the companies engaged in drug discovery are using AI tools to screen and identify compounds, calculate their potential, and minimize drug interactions that may cause issues later.
With the introduction of AI in the pharmaceuticals and healthcare sectors, the companies in these sectors are investing in collaborations with AI players for the development of better and advanced healthcare tools, which, in turn facilitates better identification of drug targets and aids in designing new drug candidates.