AI Used to Predict Drug-Target Interactions

Researchers in Florida have developed a drug discovery screening method based on artificial intelligence (AI) that could accelerate identification of promising therapeutic candidates—a process that can otherwise take billions of dollars and decades to see to completion.  By helping identify critical protein binding sites along with their functional properties, the University of Central Florida (UCF) team was able to achieve up to 97% accuracy in identifying promising drug candidates.  

The technique uses AI to model drug and target protein interactions for each protein binding site using natural language processing techniques. “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.”

Known as AttentionSiteDTI, the model is the first to be interpretable using the language of protein binding sites. The researchers made AttentionSiteDTI 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. This self-attention ability works by selectively focusing on the most relevant parts of the protein.

The researchers validated AttentionSiteDTI 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.

Mehdi Yazdani-Jahromi, a doctoral student in UCF’s College of Engineering and Computer Science and the study’s lead author, says the work offers a new direction in drug pre-screening. “This enables researchers to use AI to identify drugs more accurately to respond quickly to new diseases,” Yazdani-Jahromi says. “This method also allows the researchers to identify the best binding site of a virus’s protein to focus on in drug design. The next step of our research is going to be designing novel drugs using the power of AI. This naturally can be the next step to be prepared for a pandemic.”

The findings  were published recently in Briefings in Bioinformatics.