Reliability-centered AI for early small-molecule drug discovery. MolVerity AI helps drug-discovery teams identify potential toxicity liabilities earlier while evaluating uncertainty, chemical-domain support, and prediction reliability.
Every prediction is paired with an assessment of whether it should be trusted — applicability domain, ensemble uncertainty, scaffold novelty, and out-of-distribution status, evaluated alongside the toxicity call itself.
Drug discovery remains expensive and high risk. Experimental toxicology remains essential — but screening every candidate experimentally at the earliest discovery stage is costly and time-consuming.
Liabilities discovered late in a program are far more expensive to address than liabilities identified during early candidate selection.
Discovery teams cannot run full experimental toxicology on every candidate in a library — resources must be prioritized.
Computational screening can help teams decide which compounds should receive scarce experimental resources first.
AI-powered toxicity intelligence for early small-molecule screening, combining endpoint predictions with a structured reliability assessment.
Endpoint-specific toxicity classifications with calibrated probabilities.
Decision thresholds fixed prior to evaluation for defensible, reproducible calls.
Ensemble disagreement quantified alongside every prediction.
Assessment of whether a candidate is represented by training chemistry.
Nearest-training similarity, scaffold novelty, and out-of-distribution detection.
Structured recommendations for experimental follow-up, including abstention.
Bacterial mutagenicity-related activity for early genotoxicity screening.
Potential blockade of the cardiac hERG potassium channel.
Oxidative-stress response associated with antioxidant-response-element activation.
Cellular response associated with DNA damage and genotoxic stress.
Mitochondrial membrane-potential disruption.
p53-mediated cellular stress response.
“AI should know what it does not know.” Every ToxVerity output is accompanied by a structured assessment of how much that output should be trusted.
Probability after calibration to improve probabilistic interpretability.
Endpoint-specific locked threshold for supported binary decisions.
Ensemble disagreement indicating prediction stability.
Whether the compound is represented by model-development chemistry.
Chemical similarity to compounds represented during training.
Whether the structural scaffold is absent from training chemistry.
Flags molecules insufficiently represented by the development domain.
Declines a supported binary call when evidence is insufficient.
Enter a structure or molecular library.
Apply reproducible cheminformatics standardization.
Generate endpoint-specific toxicity predictions.
Evaluate calibrated probabilities against locked thresholds.
Evaluate uncertainty, similarity, domain, novelty and OOD.
Structure experimental follow-up, including abstention.
Representative screening output. Values shown are illustrative only and do not reflect validated model performance.
| Endpoint | Predicted Outcome | Cal. Probability | Threshold | Uncertainty | AD | Recommendation |
|---|---|---|---|---|---|---|
| hERG | Predicted Active | 0.71 | 0.55 | Low | Inside | Confirm in vitro |
| Ames | Predicted Inactive | 0.22 | 0.50 | Low | Inside | Standard priority |
| SR-p53 | Abstained | 0.51 | 0.50 | High | Boundary | Insufficient support |
Current capabilities and future planned development are clearly distinguished.
Toxicity Intelligence across six research endpoints, with reliability assessment.
Future development toward DILI/hepatotoxicity, CYP450 inhibition, metabolic stability, solubility, permeability, BBB penetration, protein binding, clearance and broader safety pharmacology.
Discuss a research pilot and evaluate how reliability-aware computational toxicity screening can support your discovery workflow.