AI may soon save a ton of cute (and ugly) animals from drug testing


As cold-blooded and inhuman as it may sound, animal tests are an integral part of modern-day drug and chemical compounds development and approval procedures. And with good reasons. Scientists can’t still reliably predict the properties of new chemicals, let alone how these compounds might interact with living cells.

But a new paper published in the research journal Toxicological Sciences shows that it is possible to predict the attributes of new compounds using the data we already have about past tests and experiments. The artificially intelligent system was trained to predict the toxicity of tens of thousands of unknown chemicals, based on previous animal tests, and the results are, in some cases, more accurate and reliable than real animal tests.

Using AI in the drug development process is nothing new. In fact with 28 pharma companies and 93 startups already spending hundreds of millions to apply machine learning and other AI techniques to drug discovery, the costly and time consuming process of identifying and testing new drugs, it seems that the industry is ripe for an artificially intelligent disruption.

According to Andrew Hopkins, CEO of Exscientia, artificial intelligence makes “better designs and better decisions about what compounds to make and test”, ultimately leading to fewer experiments and “fewer experiments means you’re saving time and money.”

He continues that people think we can’t use AI in this field because biology is complex and messy, “but it’s precisely because of the complexity of the decision-making that we should use AI. For example, Bayesian approaches are particularly applicable to messy data, where you can embrace uncertainty in the data. AI doesn’t require perfect data for perfect predictions. It’s actually about how you use it in these imperfect, messy, complicated situations to find a signal amid all the noise.”