Hard Problems, AlphaFold, and a Minor in Biology

The Protein Folding Problem Was Unsolvable — Until It Wasn't

Hard problems are an opportunity. That's where progress happens. Where humanity comes together to find creative, novel solutions to things that seem out of reach. When we need to be creative, that's when we are the most creative. When we need to be courageous, that's when we are the most courageous.

Yet our time is limited. It's a scarce resource, and how we use it directly affects the range of things we can accomplish in that blink of an eye we call a lifetime. But what if we weren't alone in this problem-solving endeavor? What if we had help? I believe AI will help us solve some of the hardest problems we can imagine — not by replacing human curiosity, but by extending how far that curiosity can reach.

The Problem That Stumped Science for 50 Years

Artificial Intelligence is a magnificently fast-evolving technology. It has enormous potential, with applications that can extensively improve our quality of life. But among all of them, one stood out to me so profoundly that it changed the course of my academic life. Protein folding is the reason I decided to pursue a minor in biology. It is the clearest example I know of AI doing something that genuinely matters.

Protein folding was a problem scientists wrestled with for over 50 years. Given a sequence of amino acids, it was extremely difficult to accurately and efficiently predict the three-dimensional structure that protein would take once folded. The process was both time-consuming and resource-intensive. Yet there had to be a more elegant solution somewhere.

Two scientists made foundational contributions that pointed toward it. Christian Anfinsen's research proved that the three-dimensional structure of a protein is determined entirely by its amino acid sequence — meaning that, in theory, if you had the sequence, you should be able to find the structure. Cyrus Levinthal's famous paradox added another dimension: the folding process, whatever it was, could not be random. If it were, it would take longer than the age of the universe to complete. So there was a way to compute it. It just wasn't visible to us yet.

Then AlphaFold Changed Everything

The team at DeepMind, after groundbreaking work on AlphaGo and AlphaZero, decided to take on something even greater. Protein folding. And through that effort, they gave birth to AlphaFold — a powerful AI model capable of predicting, with remarkable accuracy, the three-dimensional structure of any amino acid chain. It was revolutionary. It wasn't easy. But it was possible. And they made it.

That moment — when I first understood what AlphaFold had actually done — is what made me want to study biology. Not as a detour from AI, but as a deeper commitment to it. Because the most powerful thing about this story isn't the model. It's what the model unlocks: faster drug discovery, a clearer understanding of disease, new possibilities in medicine that were simply out of reach before. AI didn't just solve a scientific puzzle. It opened a door that had been closed for half a century.

A Proof of Concept for What's Possible

This is what I mean when I say AI has the potential to solve even the hardest problems — including those once believed to be so difficult they crossed into impossible. The evidence is already here. AlphaFold is not a promise or a projection. It is a proof of concept for what becomes possible when human knowledge and artificial intelligence work together toward something that matters.

Problems will always exist. But so will the people willing to face them — and now, for the first time in history, those people don't have to face them alone.