Artificial Intelligence or AI is fastly becoming an intrinsic and seemingly crucial part of the lifesciences arena.
Today’s biotech industry no longer employs old school techniques and ways, and has progressed beyond recognition when compared to what it was decades earlier. Now, if you are a new tech startup, AI capabilities represent your minimum ticket to enter the industry; and over the past few years, AI has stepped into the realm of biotech, due to an analogous transformation of biotech data.
Mark Andreessen once famously said “Software is eating the world”. Software and hi-tech industry in general has had significant impact on biotech industry as much as on other industries such as automotive, manufacturing among others.
But it is more than just the software- it is a combination of software and hardware that work in tandem to make the exascale computing possible.
Recently in a MIT Technology Review, NVidia CEO Jensen Huang said that “Software is eating the world, but AI is going to eat software”. Technology companies and many Silicon Valley investors are now pouring money into artificial intelligence.
With the renowned IBM’s AI platform- Watson, extending an arm to the biotech start-up Viome in order to make leapfrog
medical breakthroughs in microbiome science, to U.K.-based Excientia’s AI-based platform designing new molecules and helping confirm their potency, selectivity and ability to bind to specific targets, AI and biotech have forged a noteworthy alliance.Diagnostic assays are extremely important in the field of healthcare, and are updated only in case of a significant paradigm shift due to which, there are at times, missed opportunities to improve the assay when the true results of previous diagnoses become known.
But with the advent of AI in the industry, we can immediately use the true result to improve the diagnostic test which means that the more diagnostic tests that are run, the more accurate the test can become.
For instance, Sophia Genetics, startup founded in 2011 uses powerful analytical AI algorithms to intake a biopsy or blood sample from the patient, process the sample, and also analyze it. While speed is a clear benefit, the long-term advantage is that the machine learning algorithm that’s behind the AI analysis enables the diagnostic process to become smarter with each iteration.
The work of Lab assistants is extremely tedious and not to mention really unsatisfactory. This work is also, now being slowly handed over to AI programs as well.
A biotech startup, Desktop Genetics has created a novel platform to design gene editing constructs using CRISPR that works through AI which follows the entire gene editing process, from selecting proper sgRNA molecules to analyzing the data of the experiment.
Also, scientists who want quicker and/or easier data analysis can now rely on startups focused on using AI to look at many types of data. An example being the startup- H2O.ai which is an open-source platform on which people can analyze data using thousands of different statistical analysis models.
There are also a dozen other out there focused specifically on healthcare and biotech data, alleviating the burden of data analysis from healthcare providers.
Without a doubt, Drug discovery is one of the most exciting advances in biotech using AI.
A blockbuster drug can cure a critical disease for hundreds of thousands of patients across the globe and can earn its pharma company billions of dollars in revenue. That is why, to bring just one of blockbuster drug to market, companies spend hundreds of millions of dollars, in hopes of big cash risking everything not even being certain of the research’s fate.
But Ai holds promises to make drug discovery cheaper and quicker, effectively making the time needed for lead discovery a small fraction of what it is today. And with each passing day we are witnessing partnerships forming between young startups and pharma giants.
While some startups are focused on leveraging the increasing amount of genetic data and cheap sequencing to approach drug discovery from a genetics standpoint, the others are employing computer vision to analyze images of cells that have been treated with drug compounds, which eliminates the need for scores of PhDs to painstakingly peer into a microscope and screen for compounds of interest.
Despite mounting concerns around privacy, data protection etc, it certainly looks like AI is here for a long haul.
