TORONTO, March 24, 2020 (Newswire.com) - Scientists working on COVID-19 treatments, vaccines, and basic research can now access critical data to help them select antibody, RNAi, and protein reagents for more successful experiments. Developed through a combination of AI, bioinformatics, and expert curation, the data is available at https://www.benchsci.com/covid-19-data.
“Studying SARS-CoV-2/COVID-19 without the proper reagents and experimental data is extremely challenging,” says Casandra Mangroo, VP of Science at BenchSci, whose PhD research focused on virology in the Department of Laboratory Medicine & Pathobiology at the University of Toronto. “Because it’s a novel virus, there are limited data and reagents available to help scientists plan and run successful experiments. Thankfully, by leveraging AI, we can extract hidden insights from available literature and predict new uses for existing reagents based on similarities to previously studied coronaviruses.”
As a global leader in AI-assisted experimental design, BenchSci is prioritizing initiatives to accelerate SARS-CoV-2/COVID-19 research. These include:
- Data and reagent curation—processing and documenting all new publication data and reagents specifically associated with SARS-CoV-2/COVID-19.
- Reagent repurposing—performing an in silico analysis of existing antibody, RNAi, and protein reagents to find those that can be reused for SARS-CoV-2/COVID-19 research.
- Experimental insights—using targeted data mining strategies to identify pertinent publications and extract relevant experimental data from them.
BenchSci will be adding the data to its AI-Assisted Antibody Selection and AI-Assisted Reagent Selection products in the coming weeks. To ensure the most scientists can access it immediately, the company has also created a public data-sharing version at https://www.benchsci.com/covid-19-data.
BenchSci exponentially increases the speed and quality of life-saving research by empowering scientists with the world’s most advanced biomedical artificial intelligence to run more successful experiments. Backed by Google’s AI fund, Gradient Ventures, BenchSci uses machine learning to diagnose pharmaceutical R&D health from hidden patterns in procurement data. Customers receive a report on failure rates, productivity, and redundancy by department, therapeutic area, geography, and cost center. They can then address inefficiencies by deploying BenchSci's AI-Assisted Reagent Selection, which empowers scientists to select the best reagents and design criteria for their experiments. Thereafter, post-deployment reports confirm impact and ROI. A turnkey application of AI with immediate, quantifiable impact, BenchSci now optimizes reagent procurement and experimental success in 15 of the top 20 pharmaceutical companies and over 3,600 leading academic centers globally. Learn more at https://www.benchsci.com.
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