Top Ways to Trade the Drug Development AI Boom

Jul 12, 2023
Bioinformatics and Biostatistics Concept - New Software Tools and Systems for Understanding Complex Biological Datasets - Computer Science and Engineering Applied to Biology and Medicine - Conceptual Illustration

The AI boom is still alive and well.

Now, Recursion Pharmaceuticals is joining in thanks to a $50 million investment from Nvidia.
At the moment, the drug maker is using machine learning to discover new medicines.

This cements Recursion’s leading position as the preeminent AI-driven biotech firm, according to Gil Blum an analyst with Needham. “The collaboration will provide Recursion access to the most powerful AI computing company on earth,” he said, as quoted by Bloomberg.

We could see even more M&A just like this with AI changing how drugs are developed.

Consider this. According to Science Direct, “Drug discovery and development is a long, costly, and high-risk process that takes over 10–15 years with an average cost of over $1–2 billion for each new drug to be approved for clinical use.”

And, according to Morgan Stanley: “For biotech firms, it can take a blockbuster drug discovery just to break even. The median investment required to bring a new drug to market is estimated to be nearly $1 billion, while the true cost of research and development may be as high as $2.5 billion per marketed therapy, when factoring in abandoned trials and clinical failures.”

However, with AI/ML, companies like Pfizer say AI could assist pharma companies in getting medicines to market faster. AI today not only does flashy gene-sequencing work, it’s being trained to predict drug efficacy and side effects, and to manage the vast amounts of documents and data that support any pharmaceutical product.

Aside from Recursion Pharmaceuticals, investors may want to keep an eye on stocks, like Schrodinger (SDGR), AbCellera (ABCL), BioXcel Therapeutics (BTAI), and Moderna (MRNA) to name a few off the top of our head.

Look at Schrodinger, for example.

Schrodinger’s AI-powered software technology uses physics-based modeling and machine learning (ML) algorithms to help companies identify which molecules can help treat specific ailments. Better, its programs can help predict behavior of molecules and possible outcomes. And, it even allows for faster and cheaper discoveries of novel molecules, with a higher rate of success than traditional research methods.

It’s so fast the company’s platform can evaluate molecules in just hours, rather than weeks in a lab. Plus, the software can look at billions of molecules in a single day. Meanwhile, a typical lab would be lucky to look at a thousand inside of a year.

According to a published report in the Journal of Chemical Information and Modeling, “Schrödinger’s drug discovery group used these tools to design inhibitors of cyclin-dependent kinase 2 (CDK2) as a proof of concept. The team was able to explore more than 300,000 ideas and identify more than 100 ligands — among them, four unique cores — that are predicted to meet key parameters (including an IC50 <100 nM). A process that would have otherwise taken years was completed in under a week using Schrödinger’s drug discovery platform.”

In short, we could be looking at a big, big opportunity.