MolVerity AI evaluates more than predictive accuracy. Predictive discrimination alone is not sufficient for scientific decision support; calibration, uncertainty, chemical-domain coverage and robustness under distribution shift also matter.
Each toxicity endpoint is modeled to reflect its biological endpoint and dataset.
Evaluation accounts for structural scaffolds to reduce overly optimistic estimates.
Models are assessed across repeated runs rather than relying on one split.
Raw model outputs are calibrated to improve probabilistic interpretability.
Ensemble disagreement is quantified alongside predictions.
Each prediction is evaluated against represented chemistry.
Molecules outside the model-development domain are flagged.
The platform can decline a forced prediction when support is insufficient.
Model behavior is assessed on held-out external data where available.
Performance estimates include uncertainty ranges.
Known endpoint and model limitations are documented.
Model releases and predictions are designed for traceability.
Formal documentation will be published as it becomes available. This page does not present fabricated performance claims.
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Reach out for methodology questions, academic collaboration or available documentation.