Workflows

From structure prediction and docking to activity prediction and compound generation, pick the workflow that fits your research and run it.

  • AI-Cofolding

    Predict a complex's 3D structure and binding affinity with Boltz-2

    Give protein sequences and, optionally, a ligand, DNA or RNA, and Boltz-2 predicts how they fold and fit together.

    Input
    Sequences, structures, SMILES
    Output
    3D complex, confidence, affinity
    Engine
    Boltz-2
    Cost
    From 100 cr, quoted before submit
  • Virtual Screening

    Dock a compound library to find hit candidates

    Give a protein structure and a list of compounds, and each compound is docked into the pocket and scored.

    Input
    Structure + compounds
    Output
    Poses, scores, clusters
    Engine
    GNINA
    Cost
    1 cr per compound
  • TSAP (Boltz-2)

    Target-specific activity prediction with a fine-tuned Boltz-2

    Predict how active your compounds are against one target.

    Input
    Target + activity data, or a pre-trained model
    Output
    Predicted activity, model quality
    Engine
    Boltz-2
    Cost
    From 100 cr, quoted before submit
  • TSAP (AI-QSAR)

    Docking-based activity prediction you can read feature by feature

    Predict how active your compounds are against one target with a QSAR model built on docking.

    Input
    Activity data, or a pre-trained model
    Output
    Predicted activity, feature effects
    Engine
    GNINA, PLIP, scikit-learn
    Cost
    From 100 cr, quoted before submit
  • Compound Generation

    Keep the core that works and regrow the rest

    Pick the part of a compound you want to keep, and a generative model proposes a new fragment for every position you cut away.

    Input
    One compound, and the core to keep
    Output
    New compounds with properties
    Engine
    REINVENT4 LibInvent
    Cost
    10 cr per job, any size