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Developing life-saving medicines can take billions of dollars and decades of time, but University of Central Florida researchers are aiming to speed up this process with a new artificial intelligence-based drug screening process they 麻豆精品 S檝e developed.

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 麻豆精品 S損rotein interactions through words for each protein binding site and uses deep learning to extract the features that govern the complex interactions between the two.

麻豆精品 S淲ith AI becoming more available, this has become something that AI can tackle, 麻豆精品 S� says study co-author Ozlem Garibay, an assistant professor in UCF 麻豆精品 S檚 Department of Industrial Engineering and Management Systems. 麻豆精品 S淵ou can try out so many variations of proteins and drug interactions and find out which are more likely to bind or not. 麻豆精品 S�

The model they 麻豆精品 S檝e developed, known as AttentionSiteDTI, is the first to be interpretable using the language of protein binding sites.

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

The researchers made the achievement by devising a self-attention mechanism that makes the model learn which parts of the protein interact with the drug compounds, while achieving state-of-the-art prediction performance.

The mechanism 麻豆精品 S檚 self-attention ability works by selectively focusing on the most relevant parts of the protein.

The researchers validated their model using in-lab experiments that measured binding interactions between compounds and proteins and then compared the results with the ones their model computationally predicted. As drugs to treat COVID are still of interest, the experiments also included testing and validating drug compounds that would bind to a spike protein of the SARS-CoV2 virus.

Garibay says the high agreement between the lab results and the computational predictions illustrates the potential of AttentionSiteDTI to pre-screen potentially effective drug compounds and accelerate the exploration of new medicines and the repurposing of existing ones.

麻豆精品 S淭his high impact research was only possible due to interdisciplinary collaboration between materials engineering and AI/machine learning and computer scientists to address COVID related discovery 麻豆精品 S� says Sudipta Seal, study co-author and chair of UCF 麻豆精品 S檚 Department of Materials Science and Engineering.

Mehdi Yazdani-Jahromi, a doctoral student in UCF 麻豆精品 S檚 College of Engineering and Computer Science and the study 麻豆精品 S檚 lead author, says the work is introducing a new direction in drug pre-screening.

麻豆精品 S淭his enables researchers to use AI to identify drugs more accurately to respond quickly to new diseases, Yazdani-Jahromi says. 麻豆精品 S淭his method also allows the researchers to identify the best binding site of a virus 麻豆精品 S檚 protein to focus on in drug design. 麻豆精品 S�

麻豆精品 S淭he next step of our research is going to be designing novel drugs using the power of AI, 麻豆精品 S� he says. 麻豆精品 S淭his naturally can be the next step to be prepared for a pandemic. 麻豆精品 S�

The research was funded by UCF 麻豆精品 S檚 internal AI and big data seed funding program.

Co-authors of the study also included Niloofar Yousefi, a postdoctoral research associate in UCF 麻豆精品 S檚 Complex Adaptive Systems Laboratory in UCF 麻豆精品 S檚 College of Engineering and Computer Science; Aida Tayebi, a doctoral student in UCF 麻豆精品 S檚 Department of Industrial Engineering and Management Systems; Elayaraja Kolanthai, a postdoctoral research associate in UCF 麻豆精品 S檚 Department of Materials Science and Engineering; and Craig Neal, a postdoctoral research associate in UCF 麻豆精品 S檚 Department of Materials Science and Engineering.

Garibay received her doctorate in computer science from UCF and joined UCF 麻豆精品 S檚 Department of Industrial Engineering and Management Systems, part of the College of Engineering and Computer Science, in 2020. Previously, she worked for 16 years in information technology for UCF 麻豆精品 S檚 Office of Research.

Article title: AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification