This week’s AI ∩ Bio: Reading the Revolution series covers Boltz-2, a new structural biology foundation model that exhibits strong performance for both structure and affinity prediction.

To put this work in context, let’s start with the classic protein modeling pipeline logic:
🧬 Sequence → 🧱 Structure → 🎯 Function

AlphaFold revolutionized the first step, grounded in the premise that function follows from structure. Boltz-2 puts that premise to the test. It starts at the middle of the pipeline — with the 3D structure of a protein–ligand complex — and asks: Can we predict binding affinity using only geometry?

Structure is signal

Boltz-2 is a deep learning model that predicts binding affinity directly from 3D geometry — no sequence, no docking scores, no molecular dynamics.
It learns by:

  • Using real 3D snapshots of protein–ligand complexes from experiments (via the PDBBind database) as “correct” examples
  • Comparing them to incorrect or nonbinding versions (decoys)
  • Teaching itself to distinguish between the two by assigning higher scores to the true binders — a method called contrastive learning
  • Viewing each complex from multiple angles and modeling how atoms interact using cross-attention between the ligand and protein

The result? Accuracy approaching Free Energy Perturbation (FEP) — a gold-standard physics-based method — at a fraction of the computational cost. So if you have the correct structure, you can get binding affinity. But that’s the tradeoff.

AI ∩ Bio

Illustration of scientific paper with the title "AI ∩ Bio: Reading the Revolution"

Boltz-2 doesn’t predict binding sites. It doesn’t model flexible loops or conformational dynamics. It assumes the structure is already known — and that it’s accurate. But we know that:

  • Crystallography can trap proteins in inactive states
  • Ligand poses may not reflect behavior in solution
  • Flexibility is collapsed into a single static frame

Still, Boltz-2 shows how much signal is embedded in structure — when that structure is right.

 

Reflection for Early-Career Scientists

What happens when you flip the framing? Instead of building up from sequence to structure to function, Boltz-2 works from the middle, assuming structure is known, and asking how far that alone can take you. As a result, Boltz-2 sharpens the boundary of what structure can predict — and what it can’t. In other words, Boltz-2 is a boundary marker: a way to measure what’s possible if geometry is complete and correct.

Graph of Pearson Correlations over time, showing Boltz-2 with a strong accuracy / speed trade-off for affinity prediction

Boltz-2 presents a strong accuracy / speed trade-off for affinity prediction.