According to techspot, themedia, medicine is perhaps the most common in all areas where machine learning is expected to be revolutionary. In an important new milestone, drugs developed using machine learning are about to enter human trials. Three to five years of work are usually required to study the causes of diseases and compounds that may help treat diseases before new drugs enter human trials. But in partnership with Exscientia, a Japanese agency that develops AI development, will begin a Phase 1 clinical trial in just 12 months, in partnership with Exscientia, a Japanese start-up.
The drug is called DSP-1181, a forward-looking treatment for obsessive-compulsive disorder. Obsessive-compulsive disorder affects millions of people around the world to varying degrees and may weaken its psychological impact. Oxford-based Exscientia runs a machine learning platform called Centaur Chemist. The platform is said to save time on research into new compounds by combining AI technology with existing knowledge of how drugs interact with the human body.
The benefit of machine learning is that it can happen virtually and much faster than scientists working in the real world. The platform can analyze millions of molecular combinations and try to determine which is the safest and most effective way to treat a particular disease. Even more important is the potential savings associated with using machine learning to develop new drugs. Typically, it costs more than $1 billion to bring new drugs to market from concepts, many of which are proven at the research stage. But years of hard research will save time and money, speed development and free up the resources to develop more drugs.
The Exscientia and Sumitomo Dainippon trials have many aspects. The first stage is to examine how the drug affects the body and how the body metabolizes the drug. Therefore, this does not prove the efficacy of the drug. However, if DSP-1181 is proven to be safe, you can proceed to stages 2 and 3 to see if the drug can help patients with compulsive disorder in the real world.