Similar to how an LLM uses a 'chain of thought' to reason, Genesis's model 'thinks' by iteratively refining an in-memory representation of a crystal structure. This process is guided by physics-based principles, significantly improving the final prediction's accuracy.

The core philosophy behind ESMFold is that massive datasets and large transformer models can learn fundamental biological principles without needing built-in domain knowledge, applying Rich Sutton's "The Bitter Lesson" directly to bioinformatics.

AI is moving beyond simply identifying patterns in existing research papers. It is now able to extrapolate fundamental biological principles, enabling it to understand complex systems from the ground up, like the relationship between atoms, molecules, and proteins.

Biohub's goal was to create a general world model that "understands proteins." An emergent property of this generalist model was state-of-the-art performance in the highly specialized task of designing single-chain antibodies, a critical function for therapeutics. This demonstrates the power of general models to solve niche problems without explicit training.

Bolts Gen's protein design model simplifies its task by predicting only the final 3D atomic structure. Because different amino acids have unique atomic compositions, the model's placement of atoms implicitly determines the protein's sequence, elegantly merging two traditionally separate prediction tasks.

The success of protein language models can be explained by Zellig Harris's 1954 linguistic theory. Just as a word's meaning is defined by its contexts, an amino acid's biological role is determined by the sequences it can appear in. The model learns this deep statistical structure, effectively learning biology.

Contrary to trends in other AI fields, structural biology problems are not yet dominated by simple, scaled-up transformers. Specialized architectures that bake in physical priors, like equivariance, still yield vastly superior performance, as the domain's complexity requires strong inductive biases.

Trained only on sequence prediction, ESM-C independently developed a hierarchical feature space mirroring decades of human scientific discovery. Its learned representations range from basic biochemical properties to complex, abstract functional concepts, all without prior biological knowledge.

Generate Biomedicines' AI learns the fundamental rules of protein structure and function, much like a language's grammar. This allows it to design entirely new proteins by generating novel "sentences" (sequences) that are biologically coherent and functional, rather than just mimicking existing ones found in nature.