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Predictive modeling

Pull measured activities for your targets, curate them into a clean dataset, and train a model that predicts activity for your own compounds.

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[Predictive modeling]Fig. 04
Workflows
1
Stages
3
Tools
3
Each line is one workflow. Lines share a station where they run the same step; select a workflow below the map to jump to its details.
[Predictive modeling · what goes in, what comes out]

  1. Your target

  2. Its measured inhibitors

    Imatinib
    Nilotinib
    Dasatinib
Target
ABL1 kinase domain
Measured
3 ABL inhibitors
Model
MPNN
Shown: the ABL1 kinase domain from its crystal structure and imatinib, nilotinib, and dasatinib, inhibitors whose measured potencies are in ChEMBL. The workflow pulls measurements like these for your targets, curates them into a dataset, and trains a model that predicts activity for your own compounds. Structure: Cowan-Jacob et al. (2007), Acta Crystallogr D Biol Crystallogr.

Workflows

Pick the one that fits.

Open any of them in the builder: signed out you get a read-only preview, signed in you can add your inputs, see the estimate for your plan, and run. The cost cap is the default; you can change it at launch, and a run pauses instead of spending past it.

Related tools

Run any step on its own.

The tools behind these workflows also run individually.

  • ChEMBL Target Activities

    Pull measured IC50, Ki, Kd and EC50 values for your targets from ChEMBL into one table, ready to curate and train on

  • Bioactivity Dataset Curator & Splitter

    Normalize assay units, censoring, and replicates into SAR/QSAR datasets with scaffold, random, or time splits

  • MPNN Models

    Fast structure-property inference that highlights key molecule substructures