Many medical professionals have applauded artificial intelligence and machine learning in the drug development process for a long time, as they can increase the speed while decreasing the cost. However, there are a few barriers in the way before AI can reach its full potential.

What Obstacles Does AI Face?

While AI and machine learning have been shown to improve the drug development process, there are a number of obstacles that stand in the way of it becoming widely used, especially in big pharma. However, over 200 startups have begun to utilize AI to identify and develop various treatments.

One of the major obstacles is inertia. Large pharmaceutical companies already make large sums of money in the drug development process, so there is little reason for them to change their ways. While they have integrated this technology into other facets of their work, they have yet to bring it into development.

AI in Drug Development

While big pharma has reason to avoid AI, many startups have embraced it fully. These companies do not have access to the same resources as the pharmaceutical giants, so they cannot afford to spend millions on the same preclinical work.

One of these startups is Lantern Pharma. They use machine learning and AI to indicate failed drugs for new indications. As of now, they have identified drug candidates for lung and prostate cancers.

Another startup, Recursion Pharmaceuticals, also utilizes AI, specifically to create treatments for rare, genetic diseases. In sub-teams, data scientists, biologists, and engineers all work together to tackle challenges that cannot be solved by just technology or people alone.

Looking Forward

The call for AI in the drug development process is so strong because it could help to lower drug prices. While personalized therapies are very effective for patients, they are very expensive to create. Because machine learning and AI can help to lower the cost of the development process, it can lead to less expensive treatment as well.

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