Will deep learning discover new medicines? Startups — and big-pharma partners — are betting on it.
The problem: In theory, there’s a pharmacological cure for just about any ailment. In practice, discovering those therapies takes years and billions of dollars.
The solution: Deep learning, with its ability to discern patterns amid noise, could speed up drug discovery considerably. In a dramatic test, Insilico used an algorithm to sift through petabytes of biochemical data to find potential drugs in 21 days.
How it works: Based in Rockville, Maryland, Insilico used its Generative Tensorial Reinforcement Learning, or GENTRL, to create digital representations of molecules with properties that inhibit an enzyme linked to several types of cancer, atherosclerosis, and fibrosis.
- To make sure the model steered clear of established intellectual property, the researchers fed it a database of 17,000 patented compounds.
- The model produced 30,000 candidates, which the researchers whittled down to 848 using a mix of computational and AI methods.
- They selected 40 at random to examine more closely. They sent six of the most promising to WuXi AppTec, a pharmaceutical contract manufacturer in Shanghai, to synthesize. One of the molecules did indeed inhibit the enzyme in mice.
Status: Insilico’s enzyme inhibitor was only a proof of concept. However, it attracted partnerships with GlaxoSmithKline, Jiangsu Chia Tai Fenghai Pharmaceutical, and Pfizer.
Behind the news: Drug discovery is an attractive target for AI startups, given the abundance of biochemical data and desperation of pharmaceutical giants to cut costs. But success still seems hit-or-miss. Only one AI-designed drug — made by Exscientia — has progressed to human trials. Verseon has been working on the problem for nearly two decades without creating a marketable product. And, crucially, no one has found a reliable way to accelerate clinical trials, the most expensive and time-consuming part of drug development.
Why it matters: The average successful drug costs $2.5 billion dollars to bring to market, according to a 2016 study. Cutting even a fraction of that cost could allow companies to channel resources towards more and different drugs, potentially providing the public with more cures in less time.
We’re thinking: Finding a molecule that becomes a viable drug is like hunting for a single, specific plankton in the Pacific Ocean. Good thing machine learning engineers relish searching for tiny patterns in massive pools of data.
Use deep learning to estimate treatment effects for individual patients in Course 3 of our AI for Medicine Specialization.