A drug target is a molecule in the body, usually a protein that is intricately associated with a particular disease. The target is treated with medicine to produce an effective therapeutic effect. Finding new drug targets for specific diseases is the first step of developing novel drugs. Previously, researchers had to compare the effect of medicine to each target through manual work.
SK Telecom (SKT) said in a statement on May 6 that the company forged a cooperation agreement with Geninus, a gene tech company, to utilize Meta Learner, SKT's automated AI platform, and analyze clinical genome big data to create an algorithm that can selectively sort novel drug targets.
SKT regards the algorithm as vital for the development of medicine for diseases such as cardiac infarction and Alzheimer's disease. "The research on the human genome has accelerated since the human genome project was completed in 2003. However, the research field is one of natural science's conundrums because there are individual differences in the interaction between genomes, metabolites, and drugs," said SKT's chief technology officer Kim Yoon.
The Meta Learner system was designed to minimize the involvement of human specialists. It is able to autonomously process huge data and come up with AI solutions for reducing work processes and time. SKT partnered with the Catholic Medical Center in late April to create an AI-based vision solution capable of diagnosing diseases using clinical image data.
Genome analysis has become an important process of personalized treatment of chronic illnesses. Doctors can predict the risk of a disease by studying the so-called map of humans. Because genome data is as large as up to 100 gigabytes, it requires an automated platform for analysis and deciphering.
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