Researchers develop artificial intelligence screening techniques

Developing life-saving drugs can cost billions of dollars and take years, but researchers have found a new artificial intelligence-based drug screening technique, and by using that they assume to shorten the process.

Using a process that models drug and target protein interactions using natural language processing techniques, the researchers gained up to 97% accuracy in identifying promising drug candidates. The results were published currently in the journal Briefings in Bioinformatics. The researchers accomplish the achievement by devising a self-attention mechanism that makes the model understand which parts of the protein interact with the drug compounds while achieving state-of-the-art prediction performance. 

Artificial Intelligence screening technique- Significance

1. The technique reinforce drug-protein interactions through words for each protein binding site.
2. It uses deep learning to extract the features that govern the complicated interactions between the two. 
3. With AI becoming more accessible, this has become something that AI can face. People can try out so many variations of proteins and drug interactions and discover which are more likely to bind or not. 
4. The work is essential because it will assist drug designers to identify critical protein binding sites along with their functional properties, which is the key to identifying if a drug will be efficient.

How did the artificial intelligence screening techniques develop?

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

Background

The one scientists developed, known as AttentionSiteDTI, is the first to be interpretable using the language of protein binding sites. The mechanism’s self-attention capability works by selectively focusing on the most relevant parts of the protein.

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