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Implementing AI Against COVID-19

In the last few years, technological advancements have given birth to AI, machine learning and a whole new range of capabilities in the tech world. The implementation of AI and machine learning has met with both criticism and appreciation, sometimes the former more than the latter. A string of experts fear the misuse of the capabilities of AI to breakdown global solidarity and collaboration.

However, AI has proven to be an irreplaceable asset in almost every industry it has been implemented in. From aviation to software development to assisting medical personnel in operations, AI has had a positive impact on each of its endeavors. Now, with the global pandemic that COVID-19 has caused, AI is being put to use again to find a solution against the virus. But how exactly is artificial intelligence being implemented to fight the virus? Let’s have a look:

Google’s Deepmind project introduced Alphafold, a research targeted towards the prediction of protein structures in 3-D form. This system was used to identify and assess proteins associated with SARS-CoV-2, the virus responsible for COVID-19. Alphafold hopes that with its studies on the virus, it will be able to better understand its nature and come up with a vaccine against it.

What Artificial Intelligence essentially does is that it studies large datasets and tries to find out patterns and make predictions based on them. If AI is able to crack the structure of the virus and able to understand its mutation as it evolves in the coming months, a preventive vaccine can be created and tested on prospective human candidates to effectively eradicate the virus. This process now entirely depends on AI and its capability to accurately perform predictive analysis on the virus as it mutates. However, a potential coronavirus medicine will still take at least a year, or possibly longer, to develop.

Another area where AI is implemented is in the acceleration of drug development. With the use of machine learning, the process of generating 3–D models of the virus in order to understand its genetic makeup and find patterns of its pathological behavior is accelerated to boost the research. Drug development typically takes at least a decade to move from an idea to the market with a failure rate of at least 90% and high costs, but with AI, this process can be sped up and costs can be decreased substantially.

We hope that these implementations of AI in the field of biological research against COVID-19 will offer a permanent solution within a short span of time.