Chapter 8. Predicting Cures
It’s hardly news that drug development is a horrendously expensive undertaking, one requiring painstaking physical synthesis and testing. That’s because the process demands a massive commitment of equipment, time, and human labor; there’s no other way, it seems, to assure newly identified molecules will properly address specific disorders without causing unacceptable side effects. Though it exists on the nano scale, the biochemical realm is vast in its granularity, and exploring and confirming its configurations is a Herculean task.
Correct that: there was no other way. In recent years, dramatic advancements in AI and machine learning have pointed to an alternative path, one that leads out of the wet lab to a virtual arena where the potential efficacy of candidate drugs can be predicted with a high degree of accuracy, greatly reducing the time necessary for the development, testing, and dissemination of new medicines.
“On average, it takes 14 years and about $2 billion to discover and refine a new drug, take it through clinical trials, and get it into pharmacies,” says Abraham Heifets, the CEO of San Francisco–based Atomwise. “And for every hundred projects that are initiated, only a few ultimately are approved, but the costs must be covered for every one of the failures. We have to do better. We have to reduce the number of flameouts.”
Atomwise uses proprietary deep-learning ...
Get BioCoder #11 now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
