The Robots are Coming: Is AI the Future of Biotech?
AI, or artificial intelligence, has taken root in biotech. In this article, we explore its newfound niches in the industry.
Artificial intelligence (AI) and machine learning (ML) have become ubiquitous in tech startups, fueled largely by the increasing availability and amount of data and cheaper, more powerful computers. Now, if you are a new tech startup, ML or AI capabilities represent your minimum ticket to enter the industry. Over the past few years, AI and ML have started to peek their heads into the realm of biotech, due to an analogous transformation of biotech data.
We are beginning to see partnerships form between Big Pharma and biotech startups that employ AI and ML for drug discovery and other purposes. Positive results have already come out of joint projects, notably the delay in the onset of motor neuron disease in an efficacy study conducted by SITraN on a drug candidate proposed by BenevolentBIO.
With these results in mind, we must ask ourselves the question, what is the role of AI and ML now and also in the future of biotech?
Diagnostic assays today are usually developed once and only updated when there is a significant paradigm shift. Because of this, there are missed opportunities to improve the assay when the true results of previous diagnoses become known. However, ML techniques can immediately use the true result to improve the diagnostic test. This means that the more diagnostic tests that are run, the more accurate the test can become.
Currently, the most obvious implementation of ML techniques for diagnostics lies in genetic analysis. Sophia Genetics, the Swiss startup founded in 2011, exemplifies the state of the art. They intake a biopsy or blood sample from the patient, process the sample, and then analyze the data with their powerful analytical AI algorithms.
In Sophia Genetics’ case, the data analysis takes a few days with its platform, rather than several months like the current standard. While speed is clearly a 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.
Besides genetic analysis, ML techniques can be used in any diagnostic that can be digitized, allowing the algorithm to determine the correct “features” to embed into its final decision-making process. DNAlytics demonstrates another use of ML in diagnostics, using the advanced computations to help diagnose rheumatoid arthritis.
Tedious tasks done in the lab such as designing constructs for gene editing or data analysis are slowly being handed over to AI programs as well, as a sort of secretarial work. Desktop Genetics has created a novel platform to design gene editing constructs using CRISPR that works through AI. Their gene editing platform follows the entire process, from selecting proper sgRNA molecules to analyzing the data of the experiment.
The power of AI allows them to more quickly and effectively construct CRISPR libraries that may be needed for a single experiment or an entire lab. Especially for people who do not have much experience working with CRISPR-Cas9, this platform is valuable to not only expedite the process from designing to conducting an experiment but also to ensure that the guides are as effective as they can be, improving the efficacy of gene editing.
For scientists who want quicker and/or easier data analysis, there are startups focused on using AI to look at many types of data. H2O.ai is an open-source platform on which people can analyze data using thousands of different statistical analysis models. While H2O.ai is industry-agnostic, there are a few startups focused specifically on healthcare and biotech data, alleviating the burden of data analysis from healthcare providers.
Increasingly more data is being generated, but not all of this data can be used, much less appropriately, at the moment.
