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MIT develops machine learning model to quicken release of COVID-19 vaccine

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Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a new combinatorial machine learning system that could both decrease research time needed for a COVID-19 vaccine and make it more effective, researchers said. The platform, called OptiVax, focuses on developing peptide vaccines, which are a different approach from common whole virus, DNA, and RNA vaccines currently among the more than 100 vaccines in development. Peptide vaccines are a relatively recent development in the vaccination game that are designed around one specific short amino acid string, called a peptide, that can be found in the target disease. Peptide vaccines use a synthetic version of the peptide that is created in a laboratory and not harvested from the disease itself. Traditional vaccines have a larger amount of genetic information in them that isn't useful in developing resistance and can lead to unwanted immune responses and dangerous reactions--it's these genetic elements that peptide vaccines are designed to eliminate, MIT said.


How can Artificial Intelligence Contribute to a Coronavirus Vaccine?

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Biomedical research of vaccines against COVID-19 was already being tested in humans in March. Three months after the initial outbreak was identified in China, many of those owed their rapid start to the power of Artificial intelligence (AI). The feat is a promising and remarkable step in more than 200 years of immunization history. The experience may revolutionize the way vaccines are developed, potentially saving countless lives in future epidemics. According to the World Health Organization (WHO), 34 vaccine candidates were being tested in humans as of early September.


COVID-19 Vaccine Development: Know How Machine Learning Can Assist

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Several deadly viral outbreaks have happened in every part of the world, making all the nations race for a vaccine development every time. Similarly, COVID-19 or the novel coronavirus demands numerous research teams to find a vaccine to fight against this lethal virus. Wonder how machine learning contributes? Advanced technology is the greatest strength that researchers have in this digital era as it gathers data from all resources and offers useful insights. It's not just the biological researchers who work for vaccine development.


From Vaccine to Drug Making, AstraZeneca is Relying on AI for Growth

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Ever since the outbreak of coronavirus, the news is filled with daily record-breaking cases, deaths, and fortunately, vaccine-related improvements. When Pfizer became the first company among the democratic countries to unravel a vaccine, the stock market prices skyrocketed. Soon, Moderna, AstraZeneca, and Johnson and Johnson followed suit. AstraZeneca, one of the biggest contributors of the Covid-19 vaccine is using artificial intelligence to not just power Covishield vaccine, but also in other drug discoveries. AstraZeneca is a global, science-led biopharmaceutical business. The company produces major innovative medicines that are being used by millions of patients worldwide.


MIT CSAIL employs machine learning to optimize vaccine designs

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A study coauthored by researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) describes an open source system that introduces methods for designing, evaluating, and augmenting both new and existing vaccine designs. The system -- OptiVax -- leverages machine learning to select short strings of amino acids called peptides that are predicted to provide high population coverage for a vaccine. Fewer than 12% of all drugs entering clinical trials end up in pharmacies, and it takes at least 10 years for medicines to complete the journey from discovery to the marketplace. Clinical trials alone take six to seven years, on average, putting the cost of R&D at roughly $2.6 billion, according to the Pharmaceutical Research and Manufacturers of America. OptiVax might hold a key to reducing costs and expediting drug discovery, courtesy of its use of multiple predictive models.