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Pharma Tech Outlook | Monday, November 30, 2020
The first vital strand involves mining current data for insights on how to treat the disease. One drug discovery firm has its AI system to search through a massive quantity of medical datasets and classify already approved drugs that can be employed for the current pandemic.
FREMONT, CA: The emergence of the new disease remains a vital parameter in human health and society. Advances in Artificial Intelligence (AI) empower rapid processing and analysis of massive and complex data. The growing power of AI can offer a solution in discovering biological insights into a new viral strain and handling new outbreaks.
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The recent applications across disease prediction and drug development concerning the COVID-19 pandemic are given below.
So, how can AI be applied to the present pandemic of COVID-19 and in the future to prepare for the next pandemic, one might ask? The first vital strand involves mining current data for insights on how to treat the disease. One drug discovery firm has its AI system to search through a massive quantity of medical datasets and classify already approved drugs that can be employed for the current pandemic.
Another feature has been in accelerating the understanding of viral structures. An algorithm, a new method for protein structure prediction, recently released structures of proteins associated with COVID-19. Understanding the structure can accelerate the process of drug development. Another Hong Kong-based startup used Machine Learning (ML) to recognize a 3C-like protease crucial for the reproduction of COVID-19. In over four days, they came up with new compounds that could block the target’s role by mimicking its’ structure.
The last facet of application concerns viral mutation prediction and pre-emption of the next generation of viral disease. Researchers sequenced the genome of some members of the same family infected with COVID-19 and discovered viral mutation during person-to-person transmission. Mutations can denote a rise in virulence, evasion of the host immune system, and progression of resistance to antiviral treatments. Human coronavirus was first recognized in the 1960s and has undergone numerous mutations. Using advanced viral sequences from the past decades and recently sequenced COVID-19 samples with freshly emerging mutations, researchers can use prediction systems to develop probable sequences in the next generation. Given the sequence, algorithmic systems can offer data about abnormally folded proteins resulting from viral mutation. By training an ML-based system with known viral mutations in specific sequence regions, and their impacts on viral behavior, it can predict a pathogenic aspect of the expected mutation as well.
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