Rianna Herzlinger
The third iteration of Alpha Fold, first created in 2018, Alpha Fold 3 can now predict not only single-chain proteins but also the structure of protein complexes within DNA, RNA and more. Co-developed by Google DeepMind, it leverages the most recent ML architecture: transformers. The model begins with clouds of atoms and refines their positions to generate 3D representations of molecular structures. Before this technology, predicting the folding of a protein from its amino acid sequence was possible but a deeply time-consuming and painstaking process, taking an entire PhD to produce just one. Now, Alpha Fold 3 can do this instantly and with immense accuracy.
Alpha Fold is causing a tearing in scientific discipline, as the historical value of confirmation/validation is being subordinated to blind belief in Alpha Fold’s predictions. For example, seasoned academics report PhD students give presentations without even denoting that the structure they’re referencing is a prediction. Sometimes, Alpha Fold can make predictions that don’t match up to real activity changes between proteins. So what? Weren’t the original theoretical models for these proteins also inaccurate? Yes, but scientists understood why these models were true or untrue. With Alpha Fold, we cannot say why any protein is or isn’t predicted to fold a certain way. Thus, the validation process itself isn’t helped— scientists still need to do the painstaking work of proving the prediction. Worse, as a new generation of scientists work and contribute to their discipline post Alpha Fold, they may lose appreciation for validation entirely.
Returning to my thematic questions, Alpha Fold demonstrates why theoretical knowledge is important. First, because it can help with the generation of further knowledge. Understanding how one protein folds creates a knowledge base with which to understand another protein. Second, because it contributes to genuine understanding of the knowledge we have. Scientists don’t just know how that protein folds as it does, they can explain why it does and under what conditions it would react otherwise. Alpha Fold does not contribute to this theoretical knowledge. The validation process of these structures still requires the same process as it did pre-ML. Currently, seasoned scientists treat these predictions as preliminary, unproven and suspect. Papers make statements like “a prediction can still provide a useful starting hypothesis, but it is even more important to seek independent experimental data to validate conclusions,” and that Alpha Fold is an “action plan for future development,” (Kovalevskiy et al. 2024). This recognizes the contributions ML models can make to science but caution they are not the ends of scientific knowledge, merely the means. But, in an end of theory world that Anderson advocates for, scientists would no longer see a reason to validate Alpha Fold’s results; it would become gospel. What would that mean for science? It leaves us with mistakes we have no understanding of how to fix. It leaves us with drugs that could have serious consequences that we cannot augment because we don’t know where the prediction went wrong. And worse, it leaves us with a fragile and fleeting understanding of the proteins themselves.
This Case Study is part of a larger essay, Science, Data, and Knowledge: End of Theory and Understanding the World.
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“Alphafold.” Wikipedia, Wikimedia Foundation, 19 Feb. 2025, en.wikipedia.org/wiki/AlphaFold#:~:text=AlphaFold%201%20(2018)%20was%20built,appeared%20to%20be%20correlated%2C%20even.
Jumper, John, et al. “Highly Accurate Protein Structure Prediction with Alphafold.” Nature News, Nature Publishing Group, 15 July 2021, www.nature.com/articles/s41586-021-03819-2.
Kovalevskiy, Oleg, et al. “AlphaFold Two Years on: Validation and Impact.” PNAS, 12 Aug. 2024, www.pnas.org/doi/10.1073/pnas.2304819120.
R/Labrats on Reddit: People Are Overestimating Alphafold and It’s a Problem, www.reddit.com/r/labrats/comments/1b1l68p/people_are_overestimating_alphafold_and_its_a/. Accessed 21 Feb. 2025.
