Foundation models can develop unexpected biological reasoning abilities through scale alone, and these emergent capabilities don't follow smooth scaling laws—they appear suddenly at larger model sizes, suggesting qualitative jumps in what AI can do with molecular data.
Bonbon is a foundation model trained on 3.2 trillion tokens of protein-ligand sequences that unexpectedly develops three distinct capabilities for predicting molecular interactions—mechanism of action, binding site location, and bond type—without explicit training for these tasks.