AI-based screening method could boost speed of new drug discovery: Using a technique that models drug and target protein interactions using natural language, researchers achieved up to 97% accuracy in identifying promising drug candidates

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."