Using technology that models drug-target-protein interactions using natural language, researchers achieve 97% accuracy in identifying promising candidate drugs – ScienceDaily

Developing life-saving drugs could take billions of dollars and decades, but researchers at the University of Central Florida aim to speed that process up with a new AI-based drug screening process they’ve developed.

Using a method that models drug-target-protein interactions using natural language processing techniques, the researchers achieved an accuracy of 97% in identifying promising candidate drugs. The results were recently published in the journal Briefings in Bioinformatics.

This technology represents drug-protein interactions through words for each protein binding site and uses deep learning to extract features that govern the complex interactions between the two.

“With AI becoming more available, this is becoming something that AI can tackle,” says study co-author Ozlem Garibai, an assistant professor in the Department of Industrial Engineering and Management Systems at UCF. “You can try many different forms of proteins and drug interactions and see which one is most likely to bind or not.”

The model they developed, known as AttentionSiteDTI, is the first to be interpreted using the language of protein binding sites.

This work is important because it will help drug designers identify important protein binding sites along with their functional properties, which is key to determining whether a drug will be effective.

The researchers achieved the breakthrough by devising a self-attention mechanism that makes the model recognize which parts of a protein interact with drug compounds, while achieving advanced prediction performance.

The mechanism’s self-attention ability works by selectively focusing on the most relevant parts of the protein.

The researchers validated their model using laboratory experiments that measure binding interactions between compounds and proteins and then compared the results with those that their model predicted computationally. As drugs used to treat COVID remain of interest, trials have also included testing and validating drug compounds that may bind to the spike protein of SARS-CoV2.

The high concordance between laboratory results and computational predictions demonstrates the potential of AttentionSiteDTI to pre-screen potentially effective drug compounds and accelerate the exploration of new drugs and the reuse of existing drugs, Garibai says.

“This high-impact research was only possible because of the interdisciplinary collaboration between materials engineering, AI/computer learning scientists and computer scientists to address COVID-related discoveries,” says Sudipta Sell, co-author of the study and chair of the Department of Materials Science and Engineering at UCLA.

Mehdi Yazdani Jahromi, a doctoral student in the College of Engineering and Computer Science at the University of California, and lead author of the study, says the work offers a new direction in drug prescreening.

“This allows researchers to use AI to more accurately identify drugs to respond quickly to new diseases,” says Yazdani Jahromi. “This method also allows researchers to determine the best binding site for the virus protein to focus on in drug design.”

“The next step in our research will be to design new drugs using the power of artificial intelligence,” he says. “This could naturally be the next step in preparing for a pandemic.”

The research was funded by the Initial Funding for Big Data and Internal Artificial Intelligence Program at UCF.

Study co-authors also included Nilufer Yousfi, a postdoctoral researcher in the UCF’s Complex Adaptive Systems Laboratory in the UCSD School of Engineering and Computer Science. Aida El-Teibi, a doctoral student in the Department of Industrial Engineering and Management Systems at the University of California. Elayaraja Kolanthai, is a postdoctoral research associate in the University of California’s Department of Materials Science and Engineering. and Craig Neal, a postdoctoral researcher in the University of California’s Department of Materials Science and Engineering.

Garibai received her Ph.D. in Computer Science from the University of California, and joined the UCSD Department of Industrial Engineering and Management Systems, part of the College of Engineering and Computer Science, in 2020. She previously worked for 16 years in the IT field at the UCLA Research Office.

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Materials Introduction of University of Central Florida. Original by Robert Wells. Note: Content can be modified according to style and length.