How AI Accelerates Drug Development

Deep-learning is discovering brand new life-saving medications

Dear Readers,

I’ve written a lot about AI in this newsletter (see here, here, and here, for just a few). Much of my work has focused on how AI may disrupt education and how doctors interact with patients and medical systems. An area I have not focused on as much is the potential for AI to radically accelerate research and development and lead to new breakthrough treatments. This is something AI optimists have been most excited about and we’re finally starting to see this promise materialize. In this edition of All Science, the first portion of the article provides an overview of how AI impacts the drug-development process and is free for everyone, while the full article that goes into specific examples is for paid subscribers.

—Eric

Deep-learning in drug discovery

The pharmaceutical industry is a high risk business—almost 90% of new drug candidates ultimately fail. The process of discovery and clinical trials is arduous, inefficient, and filled with trial-and-error. Any methods to improve this pipeline could be a boon to innovation and reduce costs.

This is where AI comes in: Deep-learning algorithms, a subset of machine learning, are adept at handling large datasets and complex patterns. In drug discovery, AI can rapidly screen tens of thousands of compounds to find subtle clues that identify new drug candidates, a process that could take years or decades for humans to do manually. Many experts believe AI could significantly reduce the time taken to identify molecules with potential therapeutic effects, leading to life-saving medications faster than ever.

There are numerous ways AI integrates into the drug-development pipeline (source)

AI's ability to predict molecular behavior is revolutionizing drug design. For instance, Google’s DeepMind developed AlphaFold, an AI system that predicts protein folding structures, a key factor in understanding diseases and developing drugs. Accurate predictions of protein structures can drastically reduce the trial-and-error aspect of drug design. AlphaFold recently released their predicted 3-D structures for hundreds of thousands of proteins, including almost all of those in our bodies, solving a long-standing challenge in biochemistry and molecular biology.

3D shapes of proteins predicted by AlphaFold (source)

AI may also streamline clinical trials. By analyzing patient data, AI can identify suitable candidates for studies, ensuring faster and more efficient enrollment. Furthermore, AI can monitor trial progress in real-time, allowing for quicker adjustments and decision-making. This has the potential to cut both the time and cost of drug development in half. Similarly, deep-learning AI can also pave the way for personalized medicine: by analyzing genetic data, AI could tailor treatments for patients based on their specific genes that metabolize drugs, potentially increasing their efficacy (this new field is called pharmacogenomics).

Machine learning can accelerate clinical trials by selecting the populations most likely to benefit from treatment (source)

Given all of this potential, let’s look at some real world examples of these benefits in action ⤵️