# AI as Biology's Digital Microscope > Source: > Published: 2026-05-30 06:00:59+00:00 *This article is written as part of an ongoing research initiative by the AMIR Lab at Georgia Tech, exploring scientific discovery and mechanistic interpretability for biological AI models. Main results and discussion points raised are adapted from the ProtoMech framework, which was accepted into ICML 2026.*[[1]](https://www.lesswrong.com/feed.xml#fn1dpt90aqljy) AI models have revolutionized biology by enabling us to simulate, predict, and engineer biomolecules in silico. We have the unique opportunity to repurpose these AI models from opaque black boxes to digital microscopes that can help us learn more about the biological world around us. By introducing the ProtoMech framework, we demonstrate how tracing internal computational circuits can unmask hidden functional hotspots, structural motifs, and the mechanistic impacts of protein mutations. We envision a world where we can leverage these digital microscopes for scientific discovery. History's greatest biologists have always used cutting-edge tools to explore the microscopic world. In 1674, Antonie van Leeuwenhoek built pioneering single-lens microscopes to observe the first living cells. Nearly three centuries later, in 1953, Rosalind Franklin’s X-ray diffraction data allowed James Watson and Francis Crick to uncover the double-helix structure of DNA. Shortly after, in 1958, John Kendrew utilized X-ray crystallography to solve the very first atomic-resolution protein structure. Every major leap in our biological understanding has been propelled by the lenses we use to look at nature (Fig. 1). Today, we are entering a new frontier. We are no longer just observing the microscopic world through physical hardware. Now, we're building digital AI models capable of simulating it. Today, we have access to AI models that can predict biological structures and engineer novel proteins without a single wet-lab experiment. Unlike the tools of old though, this progress represents a fundamental challenge: for the first time, we don't understand how our own tools work. Even with our most complicated physical lenses, we fundamentally knew the biophysics principles that enabled them to work. But with AI models, we have no clue. Our core question is simple: rather than operate these AI models as black boxes, **can we operate these AI models as digital microscopes to learn more about the microscopic world?** To build this digital lens, our team developed ProtoMech, a framework for tracing out the internal computational pathways, or circuits, inside large protein language models, such as ESM2. ProtoMech utilizes cross-layer transcoders, which learn sparse latent representations jointly across layers to capture the model’s full computational circuitry. When we pointed this digital microscope at ESM2, we discovered that the model, without any understanding of the real world, had independently learned complex biochemistry:[[3]](https://www.lesswrong.com/feed.xml#fnxb6prvysbfk) By adjusting the lens we view biology with, we now have the capability of turning AI models from opaque black boxes to digital microscopes. Right now, we are only scratching the surface of what these digital microscopes can view. As our capacity to interpret features is bounded by our current biological knowledge, it is possible that there exist circuits governing mechanisms that are not yet well-characterized. Work toward automating the interpretation of features is most certainly necessary to expand our knowledge. However, the true paradigm shift lies in moving from *interpretation* to *scientific discovery*. Historically, biological research has been bottlenecked by the speed of wet-lab trials. By using frameworks like ProtoMech, this gives us the opportunity to translate AI models into digital labs, potentially enabling us to conduct initial biological exploration through our digital microscopes. These circuits are publicly available at [https://protmech.github.io/](https://protmech.github.io/).