Researchers have found a new drug detection technique based on artificial intelligence. Read below to learn the meaning and process of the experiment.
Researchers develop artificial intelligence detection techniques
Developing life-saving drugs can cost billions of dollars and take years, but researchers have found a new AI-based drug detection technique and, by using it, they hope to shorten the process.
Using a process that models interactions between drugs and target proteins using natural language processing techniques, the researchers achieved up to 97% accuracy in identifying promising drug candidates. The results are currently published in the journal Briefings in Bioinformatics. The researchers achieve the milestone by devising a self-attention mechanism that makes the model understand which parts of the protein interact with drug compounds while achieving state-of-the-art prediction performance.
Artificial intelligence detection technique: meaning
- The technique reinforces drug-protein interactions through words for each protein-binding site.
- It uses deep learning to extract the features that govern the complicated interactions between the two.
- With AI becoming more accessible, this has become something that AI can deal with. People can test so many protein variations and drug interactions and find out which ones are most likely to bind or not.
- The work is essential because it will help drug designers identify critical protein-binding sites along with their functional properties, which is the key to identifying whether a drug will be effective.
How were artificial intelligence detection techniques developed?
- The researchers verified their model through experiments in the laboratory that calculated the binding interactions between compounds and proteins.
- They then examined the results with which their model computationally predicted.
- As drugs to treat COVID remain important and of interest, experiments also included testing and validating drug compounds that would bind to a spike protein of the SARS-CoV2 virus.
The one the scientists developed, known as AttentionSiteDTI, is the first that can be interpreted using the language of protein-binding sites. The mechanism’s self-attention ability works by selectively focusing on the most relevant parts of the protein.
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