Engineering researchers at the University of Waterloo have developed a new system that could significantly speed up the discovery of new drugs and reduce the need for costly and time-consuming laboratory tests.
The new technology, called Pattern to Knowledge (P2K), can predict the binding of biosequences in seconds and potentially reduce bottlenecks in drug research.
Andrew Wong (second from left) poses with research associates (left to right) Antonio Sze-To, Jieming Li and Pei-Yuan Zhou.
P2K uses artificial intelligence (AI) to leverage deep knowledge from data instead of relying solely on classical machine learning.
“P2K is a game changer given its ability to reveal subtle protein associations entangled in complex physiochemical environments and powerfully predict interactions based only on sequence data,” said Andrew Wong, a systems design engineering professor and founding director of the Centre for Pattern Analysis and Machine Intelligence (CPAMI) at Waterloo.
“The ability to access this deep knowledge from proven scientific results will shift biological research going forward. P2K has the power to transform how data could be used in the future.”
Although a large amount of biological sequence data has been collected, extracting meaningful and useful knowledge hasn’t been easy. P2K algorithms tackle this challenge by disentangling multiple associations to identify and predict amino acid bindings that govern protein interactions.
Since P2K is much faster than existing biosequence analysis software with almost 30 per cent better prediction accuracy, it could significantly speed up the discovery of new drugs. By drawing information from databases in the Cloud, P2K could predict how tumour proteins and potential cancer treatments would interact.[Read more]