How AI is being used to develop anti-aging drugs

With the use and development of artificial intelligence (AI), the future of anti-aging is in a new era. Scientists are turning to machines that cannot experience age themselves.

How AI is changing the way we develop drugs Speed, accuracy, and precision are crucial in the field of drug development?

It takes a long time to develop a new drug and get it on the market. This is especially true for anti-aging treatments, which take a long time to show any results. The problem is that when a drug patent starts, it starts from the beginning of the pipeline, not when the drug is released to the public. If it takes more than 20 years to test a drug and see if it works, then we can’t make any money off of it.

Many companies are using AI to identify promising compounds for drugs. This can be done in several different ways.

  • Researchers at Atomwise wanted to know if any existing, FDA-approved medications could be useful for fighting Ebola. They found a likely target for intervention- a particular point on a viral coat protein that needs another protein to operate. They hoped that by blocking the receptor they could prevent the virus from entering cells. After training their model by asking how well a set of compounds fit the target receptor, they fed the algorithm new compounds and asked how well these fit. Out of 7000 compounds, they identified 17 promising compounds.
  • Numerate wanted to use a molecule that would fit the same receptor as ApoE4 to target Alzheimer’s disease. They used a process called scaffold hopping to generate molecular formulas for compounds that would fit the same receptor. This process took about 9 months and cost $1,000,000. Out of 10 million compounds, they found 10 that were patentable. 4 of those went on to pass in vivo studies.
  • Some companies use computers to learn how poisonous a drug might be. The computer looks at how poisonous different drugs are and what they are made of. Then it guesses how poisonous a new drug might be. This helps the company save money because they don’t have to test every new drug to see if it is poisonous.
  • BioAge Labs took a different approach to finding new drugs. They looked for biomarkers instead of using deep learning. Biomarkers are measurements that are highly predictive of aging outcomes. This allows you to evaluate the use of your intervention without waiting for the event you are trying to cause or avoid. For example, looking for treatments that reduce blood pressure is easier than looking for treatments that reduce heart attacks, because you’re able to get statistically significant results from a smaller sample size over a shorter period of time. This means that useless compounds are discarded sooner, and promising compounds are brought to the public faster.

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