LABORATORY INFORMATICS
Unlocking new treatments with AI
SCIENTISTS AND RESEARCHERS ARE USING AI TO HELP ACCELERATE THE DISCOVERY OF NEW DRUGS FOR A WIDE VARIETY OF DIFFERENT MEDICAL APPLICATIONS
Drug discovery is undergoing a radical evolution of its capabilities due to the growing use of
computational methods, including artificial intelligence (AI) and machine learning (ML) methods. These increasingly ubiquitous methods are driving companies to find new ways to develop candidate drug compounds and are opening the path toward more personalised medicine initiatives in the future.
16 Scientific Computing World Summer 2022
One method that has gained a lot of popularity is harnessing AI and ML to find potential drug molecules faster. In a paper that will be presented at the International Conference on Machine Learning (ICML), MIT researchers developed a geometric deep-learning model called EquiBind that is 1,200 times faster than one of the fastest existing computational molecular docking models, QuickVina2-W, in successfully binding drug-like molecules to proteins. The researchers found that a geometric
deep-learning model is faster and more accurate than state-of-the-art computational models, reducing the chances and costs of drug trial failures. EquiBind is based on its predecessor,
EquiDock, which specialises in binding two proteins using a technique developed by the late Octavian-Eugen Ganea, a recent MIT Computer Science
and Artificial Intelligence Laboratory researcher and Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic) postdoc, who also co-authored the EquiBind paper. Before drug development can even
take place, drug researchers must find promising drug-like molecules that can bind or ‘dock’ properly onto certain protein targets in a process known as drug discovery. After successfully docking to the protein, the binding drug, also known as the ligand, can stop a protein from functioning. If this happens to an essential protein of a bacterium, it can kill the bacterium, conferring protection to the human body. However, the process of drug discovery
can be costly both financially and computationally, with billions of dollars poured into the process and over a decade of development and testing
@scwmagazine |
www.scientific-computing.com
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