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In December, The Conversation hosted a webinar on AI’s revolutionary role in drug discovery and development.

Science and technology editor interviewed , eminent scholar in computational systems biology at �鶹��, and , assistant professor of pharmacology at Vanderbilt University.

Skolnick has developed AI-based approaches to predict protein structure and function that may help with drug discovery and finding off-label uses of existing drugs. Brown’s lab works on creating new computer models that make drug discovery faster and more reliable. Below is a condensed and edited version of the interview.

Let’s start with the big picture. How is AI changing biomedical research and drug discovery, and what is the potential we are talking about?

Skolnick: The upside, potentially, is very large. One of the frustrating things about drug discovery is that, in spite of the fact that the people doing it are extraordinarily intelligent and have done an extraordinarily good job, . �鶹�� drugs will have negative health effects that outweigh its benefits. Of the ones that pass, .

In drug development, there are several key issues: Can you predict which target is driving a particular disease? Once this target is identified, how can you guarantee the drug is going to work and isn’t simultaneously going to kill you?

These are outstanding problems in drug discovery in which AI can play an important, though not 100% guaranteed, role. Unlike us, AI can look at basically . On a good day it makes strong and true connections called “,” and on a bad day it does what is called “” and sees things that are weak and probably false.

At the end of the day, many diseases do not have a cure. Most diseases are maintained, such as high cholesterol or autoimmune conditions. A treatment for cancer might buy you five years, and now you’re in Stage 4 and you’ve exhausted all the standard care drugs. to suggest alternatives where there are none.

Let’s give some basic definitions here. When we use the word drug, we’re talking about a wide range of therapies. Can you explain the range – we’ve got small molecule drugs, biologics, gene therapies, cell therapies.

Brown: We have fairly large molecules in our bodies called proteins. They are like machines that and interact with one another. Oftentimes, when we’re trying to treat disease, we’re trying to . Many drugs, like and , are small molecules that can fit into a protein and change its function. Fundamentally, drugs don’t have to just interact with proteins, but this is a major way in which our current repertoire of medications work.

There are also proteins that act like drugs, such as . When you receive a vaccine for a virus, your body is basically given . These antibodies will target some part of that virus. Your body is creating these big molecules, much bigger than aspirin, to go and interact with foreign proteins in a different way. is a larger step beyond that.

So these modalities – molecule, protein, antibody or gene – are very different types of molecules. They have different scales and rules, so the way you approach designing and discovering them various widely.

Can you briefly explain artificial neural networks, and what the “deep” in deep learning means?

Skolnick: AlphaFold, developed by DeepMind, involved understanding how neural networks worked. They built a network with a lot of , similar to how your brain actually works. These simple connections, or neurons, have .

They also created sophisticated neural networks, such as like a special-purpose tool that can learn, and they added a mechanism called “attention,” which . Super neural networks with transformers is what we call deep learning. These now have literally billions, if not trillions, of parameters.

Essentially, these machines , meaning the patterns of conditional interactions that depend on the properties of multiple things simultaneously. In these higher order correlations, AI has the potential to see previously unknown things that are embedded in petabytes (a unit of data equivalent to of biological data.

AlphaFold, which , has millions of sequences and a couple of hundred thousand structures. It can tell you, based on a particular pattern, what that sticks to a protein to induce some kind of structural shift.

How is this technology being used in biomedical research to understand molecular dynamics or, essentially, the biological processes involved in health and disease?

Brown: In 2013, there was a Nobel Prize for , computational tools that help you understand the motions of molecules as they move according to physics. There’s a huge body of scientific research built around those ideas.

AI and deep learning are large right now, but it’s worth mentioning that for the last decade and a half, people have been to help design drugs. A lot of the ideas, such as [using machine learning for virtual screening], are not new and have been in practice for a while.

With AlphaFold’s technologies to help people design proteins and predict their structure, we’ve changed how we think about a lot of these problems. We have this to build ideas around and to start thinking about drug discovery.

From 20 years ago to now, what has today’s AI technology done in terms of scale of change in this process?

Skolnick: A lot of diseases, like cancers, are . AI now allows us to start to think conceptually about how these diseases are organized and related to each other.

Diseases tend to co-occur. For example, if you have . Kind of weird, right? We can look at pieces, but AI can look at all the information, integrate the collective behavior and then identify common drivers. This allows you to construct disease interrelationships which offer the that rather than narrow-spectrum treatments.

Relatedly, AI also can help us . Diseases that tend to . You have disease 1, it gives you disease 2, then gives you disease 3. This suggests that if you go back to the root with disease 1, you may be able to stop a whole bunch of stuff. You can’t analyze millions of trajectories and millions of data without a tool, so you couldn’t do this before.

This holds a lot of promise, but one also must be careful not to overpromise. It will help, it will accelerate, but , real clinical validation and trials.

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