Gabriele Corso

BoltzMol-1: Towards Reliable Virtual Screening for Fast and Cost-Effective Hit Discovery

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

BoltzProt-1: Towards Efficient De Novo Binder Design with Good Developability

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

BoltzGen: Toward Universal Binder Design

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: Towards Accurate and Efficient Binding Affinity Prediction

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.

Boltz-1: Democratizing Biomolecular Interaction Modeling

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.

PLINDER: The protein-ligand interactions dataset and evaluation resource

PLINDER provides a large, annotated PLI dataset with diverse annotations and improved evaluation splits to better assess computational methods for drug design.

PINDER: The protein interaction dataset and evaluation resource

PINDER provides a benchmark dataset for evaluating protein-protein interaction docking models, revealing overestimation of performance on leaky test sets and showing improvements with interface-clustered training.