Drug Discovery Partnerships

Run molecular dynamics simulations, conduct virtual screening campaigns or train machine learning models. We do it for you.

We are the right partner:

  • Private, proprietary cluster of GPUs and CPUs. Your data will never leave our facilities.
  • Unparalleled experience in structural biology and computational chemistry.
  • Strong publication record in top-tier journals.
  • Proven collaborations with pharmaceutical companies.
  • Pioneers in Molecular Dynamics and Machine learning applied to drug discovery.
  • 9.4/10 rating in Science Exchange.

Fragment virtual screening with mixed-solvent molecular dynamics

Screen hundreds of fragments against your target in weeks.

You will obtain:

  • A short list with the most promising compounds
  • Binding modes
  • Pharmacophores
  • Pocket ensembles

Further details on this protocol.

In silico binding assay for binding mode prediction

With our software, we can:


Further details on this protocol.

Exploring the conformational landscape of your target

In collaboration with UCB, we transitioned a GPCR from inactive to active state using Molecular Dynamics.

Kinases, GPCRs and other targets undergo dramatic changes to reach their active states. We have experience exploring these structural changes with unbiased simulations, and we can do it for you.

Further details on this protocol.

Docking and Virtual Screening with AceDock

Our docking protocol, where we used KDEEP to re-score docked poses, won 2 blind sub-challenges of the D3R Grand Challenge 4. With our docking software, we can cover several relevant scenarios:

  • Free docking: No restrictions or prior knowledge is imposed in the docking exercise. Poses are re-scored with KDEEP.
  • Scaffold-hopping: Identify compounds in your library with great pharmacophoric overlap against a known binder.
  • Ensemble docking: We build an ensemble of conformations for your pocket to model protein flexibility.
  • Template docking: Use a known binder to guide the docking of a similar compound or series.
  • Constrained docking: Impose restrictions so certain moieties in the ligand (e.g. aromatic rings) are placed in the right spot.


Further details on this protocol.

Binding affinity prediction with machine learning

Predict protein-ligand binding affinity with proven accuracy.

Our propietary predictors (KDEEP, DeltaDelta, BindScope) have been published in peer-reviewed journals and validated in data from top pharmaceutical companies. Furthermore, KDEEP won two blind subchallenges of the D3R Grand Challenge 4 and it is now used daily in a large pharma company to perform their predictions.

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