Jeremy Wohlwend

BoltzMol-1 identifies chemically tractable binders for diverse targets with limited experimental resources using model-driven ranking and physicochemical filtering.

BoltzProt-1 improves nanobody design for protein targets by increasing hit rates and developability compared to existing methods.

An all-atom diffusion model unifying biomolecular structure prediction and sequence-structure generation designs experimentally validated nanobodies, mini-proteins, and linear or cyclic peptides across 26 diverse targets.

Boltz-2 predicts protein–ligand affinity from its co-folded structures, nearing free-energy perturbation accuracy (average Pearson 0.66 on a 4-target FEP+ subset) at over 1000× lower compute.

Open-source Boltz-1 predicts biomolecular complex structures comparably to AlphaFold3 and Chai-1 on CASP15 and recent PDB targets; Boltz-steering lets 97% of test-set poses pass physical checks.

This paper introduces MIST, a deep learning framework for metabolite structure annotation from mass spectra, outperforming existing methods and identifying new metabolites in a disease cohort.