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- Scientists are experimenting with the use of AI in drug development and testing.
- Through the use of AI, scientists can design, synthesize, and validate a new drug in 46 days.
- The scientists hope this new method will speed up the process of drug development. Not only that, they aim to use it for work on aging research.
About the Research
Insilico Medicine and Biogerontology Research Foundation are spearheading this research about artificial intelligence. Because of their joint aim, this collaboration is possible. That is, both companies want to discover potential ways to slow down the aging process.
Their research is the last of many experiments that aim to discover how we can implement AI in drug development. Furthermore, this particular experiment is the first successful attempt in a long line of research on the capabilities of AI. The article describing this research is available through the journal Nature Biotechnology.[/vc_column_text][vc_column_text]
Artificial intelligence and Drug Development
The scientist working on this project advocate for the use of AI in drug development. They believe the new technology has the potential to transform the pharmaceutical industry. Especially when it comes to research on the aging process and how we can prolong it.
The research team uses a combination of Reinforcement Learning (RL) and Generative Adversarial Networks (GANs) to better drug development. They show that this process is more effective than the previous Hit to Lead (H2L) method. This fact is most obvious when we compare the time it takes the H2L and AI to develop new drugs. With the old method, it took scientists up to three years to develop a new drug. AI can do the entire process in 46 days. This time-frame can significantly alter drug development in the future and it may help millions of people whit untreatable conditions.[/vc_column_text][vc_column_text]
- Even though this breakthrough is a huge step for medicine we still need to develop it further. That means, we have to examine the results critically and create new experiments that can validate the existing findings.
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