Thomas Chittenden, chief data science officer at Genuity Science, says what's keeping the genomics revolution from turning into an equivalent revolution in drug discovery is that most of our domain knowledge about the molecular biology of disease has come from a hunt-and-peck approach, focused on one gene at a time. Find some gene relevant to a disease, knock it out, and you see what happens. Such experiments are always revealing, but the reality is that human biology is the product of the interactions of huge networks of thousands of genes—which means most diseases are the product of dysregulation across these networks. Which means, in turn, that to figure out where to intervene with a drug, you really need to identify the patterns that cascade through the whole network. 

That’s where AI and machine learning come in, and that’s why Genuity has tasked Chittenden to lead R&D at its Advanced Artificial Intelligence Research Laboratory. Chittenden's team is pioneering new applications of old ideas from the world of probability and statistics, including some that go all the way back to the work of the English statistician Thomas Bayes in the eighteenth century, to look at gene expression data from individual cells and predict which genes are at the beginning of the cascade and are the causal drivers of diseases like atherosclerosis or high blood pressure. The hope is that Genuity can help its clients in the drug discovery business make smarter bets about which drug candidates will be most effective. And that could help shave years of development and billions of dollars in costs off the drug development process.

Chittenden is one of those rare professionals who has more degrees than you can shake a stick at—he has a PhD in Molecular Cell Biology and Biotechnology from Virginia Tech and a DPhil in Computational Statistics from the University of Oxford, and completed postdoctoral training at Dartmouth Medical School, the Dana-Farber Cancer Institute, and the Harvard School of Public Health—but can also explain the actual science in a way that makes sense for a non-expert. On top of that he’s been thinking hard about how to rein in some of the hype around the power of AI and machine learning in drug development and how to set expectations about what computing can and can’t do for the industry.

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