DE Shaw Research (DESRES) invested heavily in custom silicon for molecular dynamics (MD) to solve protein folding. In contrast, DeepMind's AlphaFold, using ML on experimental data, solved it on commodity hardware. This demonstrates data-driven approaches can be vastly more effective than brute-force simulation for complex scientific problems.

Modern protein models use a generative approach (diffusion) instead of regression. Instead of predicting one "correct" structure, they model a distribution of possibilities. This better handles molecular dynamism and avoids averaging between multiple valid states, which is a flaw of regression models.

Models like AlphaFold don't solve protein folding from physics alone. They heavily rely on co-evolutionary data, where correlated mutations across species provide strong hints about which amino acids are physically close. This dramatically constrains the search space for the final structure.

The low-hanging fruit of applying AI to existing datasets is being picked. The next major leap forward will come not from slightly better models, but from creative strategies to generate entirely new datasets for unsolved problems like protein stability or in vivo effects.

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.

Current AI for protein engineering relies on small public datasets like the PDB (~10,000 structures), causing models to "hallucinate" or default to known examples. This data bottleneck, orders of magnitude smaller than data used for LLMs, hinders the development of novel therapeutics.

AlphaFold 2 was a breakthrough for predicting single protein structures. However, this success highlighted the much larger, unsolved challenges of modeling protein interactions, their dynamic movements, and the actual folding process, which are critical for understanding disease and drug discovery.

The pipeline's high success rate stems from its final filter, which uses an AlphaFold model trained only on single proteins (monomers) to predict a protein complex. The rationale is that if a model naive to complexes can still predict the interaction, the interface must be exceptionally strong and well-defined.

Standard co-folding models struggle with induced proximity because public databases like the PDB lack sufficient training data for multi-protein complexes. Because dynamic multi-protein complexes take on endless conformations, standard AI models become overconfident and inaccurate. To solve this, Protai generates proprietary structural proteomics data that maps physical distances and conformations, enabling accurate modeling of complexes that can be targeted by molecular glues and PROTACs.