Boltz-2 is introduced as an open model for both biomolecular structure and binding-affinity prediction. The article frames affinity estimation as a trade-off: free-energy perturbation can be precise but computationally expensive, while docking is faster and noisier.
The authors report performance on several evaluations, including an FEP benchmark, the CASP16 affinity challenge, and virtual screening. They describe affinity predictions running roughly three orders of magnitude faster than FEP while reaching comparable benchmark accuracy; these are reported study results, not a guarantee for every target.
For structure prediction, the post highlights gains over Boltz-1 on DNA-protein, RNA, and antibody-antigen cases, alongside controls for contacts and templates. Boltz-2 adds an affinity head, finer input controls, GPU optimizations, and additional synthetic and molecular-dynamics training data.
The article presents the model as a way to connect structure prediction and compound screening. It also notes that affinity performance depends on the quality of the predicted binding pose, so computational rankings remain part of a broader validation workflow.