Using Machine Learning For Medical Solutions
Pharmaceutical companies spend a lot of time testing potential drugs, and they end up wasting much of that effort on candidates that don’t pan out. Kyle Swanson wants to change that.
A master’s student in computer science and engineering, Swanson is working on a project that involves feeding a computer information about chemical compounds that have or have not worked as drugs in the past. From this input, the machine “learns” to predict which kinds of new compounds have the most promise as drug candidates, potentially saving money and time otherwise spent on testing. Several prominent companies have already adopted the software as their new model.
“Our model is never going to be perfect … but the hope is that by doing this prediction phase first, the molecules that they actually test in the lab have a much higher chance of being viable drugs,” says Swanson, who graduated from MIT in 2018 with a BS in computer science and engineering, a BS in mathematics, and a minor in music. READ MORE ON: MIT NEWS