- Mechanism of action (MoA) prediction assigns likely MoA labels to a compound from its biological activity, gene expression and cell viability profiles.
- H2O Driverless AI can build and explain MoA models on your own assay data, with the evidence for each prediction.
- Predictions rank hypotheses for your scientists. They do not replace follow-on assays, toxicology review or any regulatory study.
- H2O makes no claim of GxP, 21 CFR Part 11 or FDA validation. The platform supports validated workflows; the validation is yours.
Learn the mechanism behind a screening hit, not just the activity
A phenotypic screen tells you a compound does something to cells. It does not tell you how, and knowing the mechanism matters for explaining the finding and for anticipating side effects.
Working out the mechanism of action (MoA) by hand takes follow-on assays, literature review and expert time for every promising hit, so with hundreds of hits per screen, most wait in a queue.
How mechanism of action prediction works
H2O can be used to learn the link between a compound's screening profile and known mechanisms, then score new compounds. Your scientists decide which predictions are worth testing.
Step 1: assemble labeled screening data
Start with compounds whose mechanisms are already annotated, together with their gene expression, cell viability and other assay readouts. Your own screens and permitted reference sets both work as training data.
Step 2: learn the patterns behind each mechanism
H2O Driverless AI runs supervised machine learning experiments that test many model types, including neural networks, and keeps what scores best on held-out compounds. Automatic feature engineering finds combinations of readouts that separate one mechanism from another.
Step 3: score new compounds
Each new compound receives a prediction for each candidate mechanism, as a probability, so a ranked list of likely targets and pathways goes to your scientists as a starting point for hit prioritization.
Step 4: explain and document
Reason codes show which readouts drove each prediction, and model documentation is written while the model trains.
Where predictions help, and where people decide
The same models can suggest new mechanisms for existing compounds, giving your team repurposing hypotheses to test. Any use for toxicity or safety questions stays decision support that your toxicologists review, never a substitute for a required study. H2O makes no claim of GxP, 21 CFR Part 11 or FDA validation; the platform supports validated workflows, but the validation is yours.
Where the modeling proof comes from
H2O has no named life sciences customer today. Its proof in this vertical is public and checkable: 1st place in the Kaggle diabetic retinopathy detection competition, 1st of 784 in a Kaggle pulmonary embolism computer-vision challenge, and top 3 in the HEROHE histopathology challenge. These are competition results, not customer outcomes.