Affinity Fine-Tuning of Boltz-2: An Open Framework for Protein-Ligand Potency Prediction in Drug Discovery

Boltz-2 has enabled accurate binding affinity prediction by leveraging co-folded protein-ligand structures, but the absence of a public training recipe has limited its use in active drug discovery projects, where new experimental measurements and congeneric ligand series continually arrive during lead optimization. We present an open framework for affinity fine-tuning of Boltz-2, showing that adapting only the affinity prediction components with project-specific experimental data can make the model substantially more useful for lead optimization. We evaluate the approach in two internal studies: a multi-target retrospective benchmark against physics-based and machine learning baselines, and a large single-target study with up to 1,700 ligands. In both settings, fine-tuned Boltz-2 improves correlation over the off-the-shelf model, and in some cases reaches performance competitive with free energy perturbation (FEP) methods. By releasing this framework, we aim to enable the community to adapt Boltz-2 to the specific targets and data of their own drug discovery campaigns. The code is available at: https://github.com/molecularinformatics/Boltz2_affinity