Using a method that models drug and target protein interactions using natural language processing techniques, the researchers achieved up to 97% accuracy in identifying promising drug candidates. The results were published recently in the journal Briefings in Bioinformatics. The technique represents drug-protein interactions through words for each protein binding site and uses deep learning to extract the features that govern the complex interactions between the two. "With AI becoming more available, this has become something that AI can tackle," says study co-author Ozlem Garibay, an assistant professor in UCF's Department of Industrial Engineering and Management Systems. "You can try out so many variations of proteins and drug interactions and find out which are more likely to bind or not."

Algorithms modeled loosely on the brain have helped artificial intelligence take a giant leap forward in recent years. Those algorithms, in turn, have advanced our understanding of human intelligence while fueling discoveries in a range of other fields. MIT founded the Quest for Intelligence to apply new breakthroughs in human intelligence to AI, and use advances in AI to push human intelligence research even further. This fall, nearly 50 undergraduates joined MIT's human-machine intelligence quest under the Undergraduate Research Opportunities Program (UROP). Students worked on a mix of projects focused on the brain, computing, and connecting computing to disciplines across MIT.