Why AI must be involved in developing a vaccine
Only this can assure speedy mining of data in an error- freemanner with deeper insights and come up with potential solutions
Drug discovery has traditionally been a slow process, but AI ( Artificial Intelligence) is shortening the time that such life- saving pharmacological research used to take — from years to months in the case of a coronavirus vaccine.
There is a ton of mind- numbing, timeconsuming, repetitive and tedious administrative work that goes into finding an effective vaccine. Which is a large part of why successful vaccines take years to get to clinical trials… and even longer to get approved.
Today, we are enabling pharmacological researchers to fast- track what used to take years. AI algorithms are being used to crunch through millions of pharmaceutical compounds, simulate and predict what might work best against the coronavirus, and doing that far faster than a human researcher ever could.
These algorithms work together with people, who feed in the high- quality data sets, so that predictions can be made with data from a foundation of sound science. This is critical because if trustworthy, reliable, standardised, and certified data is not provided, the algorithms will simply magnify the impact of poor quality, or poorly understood data exponentially.
Making data trustworthy and fit for purpose is tedious. Much of the data that is required to accelerate coronavirus research is tied up in disparate databases within individual big pharmaceutical companies, or buried deep inside computer files at laboratories, universities and disjointed healthcare organisations.
Every time a pharmacological researcher receives a newer set of data from these sources, they must spend valuable time making it usable before it can be used by AI algorithms crunching through it all and leading to a vaccine breakthrough.
If we work in thisway, the ability tomake bold steps will be significantly impeded.
Hand over the task
AI- driven data management is the best way to govern these volumes of structured and unstructured data at the heart of vaccine research, to provide AI algorithms with clean, high quality, and trustworthy data to deliver results that support a fasttrack to clinical trials.
Not only is AI more efficient, scalable, and cost- effective, AI also makes fewer errors, and can complete more extensive cross- checking against more data than a human being ever could.
Pharmacological researchers and clinicians are working smarter, faster and more efficiently by using AI to also automate these time consuming, but critical, data management tasks in much the same way they are using AI to automate the predictive analytics to search for clinical solutions. This combination is the key to our achieving a coronavirus vaccine in months, rather than years.
By integrating the human talents and AI- driven functions to boost productivity, the time it takes to find a vaccine has been greatly reduced. And when researchers get to clinical trials for possible vaccines, they are also using AI- driven data management to integrate and catalogue new data — data like clinical trial locations, names, patient background, providers and products — faster. The AI algorithms managing all of this also provide capabilities that humans cannot, like discovering hidden relationships in data and quickly highlighting powerful insights.
The good news is that the incorporation of AI into data management tools and applications is maturing rapidly, and due to the global pandemic, these tools are being seamlessly integrated within the pharmaceutical, healthcare, university and government organisations.
After an effective, safe and easily produced vaccine for Covid- 19 is found, researchers will begin looking for a cure. It is most likely that a combination of drugs will eventually be used to defeat coronavirus, requiring analysis of not only millions of possible drug pairs, but also billions of triple- drug combinations stemming fromover 4,000 approved drugs on themarket today.
People working together with AI- driven analytics and data management will be the key to surmounting this challenge as well, and in eradicating other deadly diseases like cancer.
The AI algorithmsmanaging all of this also provide capabilities that humans cannot, like discovering hidden relationships in data and quickly highlighting powerful insights.