Between-series triage
Compare liability patterns, evidence coverage, support, and uncertainty across related chemistry.
Compound and chemical-series safety
MLTox Small Molecule Safety
Prioritize compounds and related chemical series while keeping the evidence behind every signal visible.
The decision
Safety evidence loses leverage when it arrives after the chemistry is fixed
Discovery teams often have more compounds than assay capacity and several tools that disagree. The useful question is which result should change synthesis, design, testing, or advancement, and why.
Prioritize compounds with the whole profile attached
MLTox keeps toxicity, potency, ADME/PK, physicochemistry, analogue evidence, and uncertainty separate so one attractive score cannot hide a weak model or poor drug profile.
- Choose which compounds to synthesize or advance.
- Identify structures to redesign or deprioritize.
- Focus an assay, analog review, or expert escalation.
- Keep unsupported predictions out of the decision.
Compare the pattern, then inspect the compounds
SeriesLens is the chemical-series workflow inside MLTox Small Molecule Safety. It compares user-defined series while preserving each compound's endpoint evidence.
Within-series prioritization
Inspect the compounds and local structural changes that drive the pattern before deciding what to make or test next.
- Current engine
- Batch analysis for up to 300 compounds, with one evidence record per input.
- SeriesLens workflow
- User-confirmed series comparison with an analyst-supported prioritization review.
- Validation path
- Retrospective blind evaluation followed by a prospective discovery cycle.
Endpoint coverage
Fifteen visible hazard outputs, reviewed one decision at a time
Coverage and performance vary by endpoint and model version. Fourteen standard outputs and one exploratory nephrotoxicity output retain model evidence, applicability, uncertainty, and intended-use limits.
Selected endpoint
Hepatotoxicity
Potential to cause drug-induced liver injury (DILI).
- Decision question
- Could a liver-injury signal change compound priority or trigger focused review?
- Evidence to inspect
- Calibrated hazard state, conformal set, applicability domain, structural drivers, and measured analogues.
Boundary: this is a screening classification for scientific prioritization. It does not replace the endpoint-specific experimental study.
Hazard × dose
Ask not only whether, but at roughly what dose
Three direct structure-based dose readouts, plus a tiered systemic POD and a derived IVIVE cardiac margin, add magnitude to the hazard profile. Precision follows the evidence supporting each model.
Magnitude map
Read direct estimates and derived context differently
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Acute oral LD50Direct estimate
Approximate rat oral mg/kg value, broad interval, and GHS category.
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hERG IC50Direct estimate
Channel-block concentration, blocker class, and uncertainty range.
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ER AC50Direct estimate
Estrogen-receptor activity concentration and potency tier.
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Systemic POD + cardiac IVIVETiered context
Coarse bands remain visibly separate from direct model readouts.
Applicability gate. The platform withholds estimates outside recognized chemistry instead of extending the scale beyond its evidence.
ADME/PK and druglikeness
Ask whether the compound can behave like a drug
Seven regressors and eight classifiers cover absorption, distribution, metabolism, and excretion. Computed physicochemical and medicinal-chemistry rules sit beside the predictions.
Selected disposition domain
Absorption
Can the compound cross key barriers and support oral uptake?
- Caco-2 permeability
- Human intestinal absorption
- PAMPA permeability
- Oral bioavailability and P-gp inhibition
Each result keeps its endpoint metric, uncertainty range, and applicability decision beside the value.
Illustrative report values
Endpoint evidence view
Absorption
| Endpoint | Result | Type | Validation or range |
|---|---|---|---|
| Caco-2 permeability | -4.189 | log(Papp cm/s) | R² .637 · 95% range -5.106 to -3.272 |
| Human intestinal absorption | 100% | Positive probability | AUC .961 |
| PAMPA permeability | 88% | Positive probability | AUC .746 |
| Oral bioavailability | 83% | Positive probability | AUC .672 |
| P-gp inhibition | 8% | Positive probability | AUC .930 |
Move from a score to inspectable evidence
The report gives a chemist, toxicologist, or reviewer several independent ways to challenge the prediction before acting.
Nearest-analogue read-across
Inspect similar training compounds, ECFP4 similarity, domain status, and measured outcomes rather than trusting a global score alone.
Approved-drug percentiles
Place the whole profile against 3,365 approved drugs while fading weak or out-of-domain spokes.
OECD QMRF and QPRF
Export model-level and prediction-level documentation as PDF or editable DOCX; the files support review but do not guarantee regulatory acceptance.
Batch, API, and grounded questions
Screen up to 300 submitted entries per request, integrate through REST/CLI/Python, and ask questions grounded only in the report.
Workflow
One assessment, four review steps
Move from a controlled structure to an endpoint-level decision without losing the evidence, uncertainty, model version, or report context.
Define the use
Name the compound set, discovery stage, endpoints, comparator, decision owner, and action that may change.
Submit a structure
Draw it, search a public name, paste SMILES/InChI/MOL, or upload a .smi or .sdf library.
Inspect the whole report
Review hazards, potency, ADME/PK, physicochemistry, analogue evidence, uncertainty, and domain status.
Compare, export, or feed back
Rank the library, export PDF/JSON/QMRF/QPRF, or submit a confirmed small-molecule outcome for retraining.
Decision confidence
Separate a confident-looking score from a defensible decision
MLTox keeps four different questions visible so reviewers can tell whether a prediction should inform the decision, trigger more evidence, or be withheld.
- 01
Does the endpoint model earn trust?
Start with endpoint-specific evaluation, calibration, dataset design, and the exact model version rather than the displayed probability alone.
- 02
Is this prediction decisive?
Inspect the 90% split-conformal set. Coverage is marginal across the data distribution, not a guarantee for this molecule.
- 03
Does the model recognize this chemistry?
Check applicability, nearby evidence, scaffold coverage, and conflicts before carrying the result into a series decision.
- 04
What action does the evidence support?
Record whether to advance, redesign, run a targeted assay, seek expert review, or abstain, together with the reason.
Team workflow
Built for a cross-functional discovery decision
The record is shared. Each discipline keeps its own judgment and responsibility.
Medicinal chemistry
Compare and redesign
Bring endpoint findings into series and design reviews.
DMPK and discovery safety
Focus evidence
Prioritize assays and challenge uncertain results.
Computational toxicology
Review methods
Inspect versions, applicability, calibration, and disagreement.
Project toxicology and informatics
Carry the rationale
Connect candidate reviews, reports, and scientific systems.
Focused evaluation
Prove one discovery decision before expanding the platform
Start with one historical program and a time-held-out compound set. Test compound and series prioritization against current tools, expert review, and assay plans.
Discuss a small-molecule pilotMeasures that matter
- Useful disagreement with the current process.
- Review time and evidence inspection burden.
- Appropriate escalation of unsupported cases.
- Whether the result improves the next action.
- A required exit: adopt, revise and rerun, or stop.