Submit a small molecule or a protein and get a calibrated safety
report in seconds — QSAR toxicology across eight endpoints, or
learned immunogenicity and developability screening for biologics.
The models fine-tune themselves as your clinical and
lab results come back.
Research tool. Every output is an in-silico hypothesis on an unvalidated model — a
prioritization aid, not a regulatory or clinical determination.
Built on established science
RDKit featurization
Bemis–Murcko scaffold split
IEDB-trained MHC models
CamSol-style solubility
ESMFold structure refinement
Isotonic + conformal calibration
How it works
One input. The right analysis. A loop that improves.
mltox auto-detects what you submit and routes it to the appropriate model — a
protein is never scored by a small-molecule QSAR. Real-world outcomes feed back in.
01
Submit
Paste a SMILES, InChI, MOL/SDF, or a
FASTA/amino-acid sequence. No account, no setup.
02
Detect & featurize
Format is auto-detected. Small molecules are featurized (RDKit or a pure-Python
fallback); biologics get a sequence-liability pipeline.
03
Model predicts
Per-endpoint gradient-boosted classifiers, or learned per-allele epitope models —
each with calibrated confidence and an applicability-domain gate.
04
Report & feedback
A structured report with rationale. Submit clinical/lab outcomes; weighted
retraining folds them back into the models.
How the loop closes. Every report is a prediction. When a real
result comes back — a clinical readout, an in-vivo study, a bench assay — you submit
it as feedback. Each outcome is stored with a trust weight based on its source
(a clinical trial counts for more than a literature note), then folded into a retrain
over the seed data plus all accumulated feedback. That updated model scores your next
molecule. The more real-world data you feed it, the more it is tuned to your chemistry.
Scope note: the fine-tune loop updates the small-molecule endpoint
models. The biologics epitope models are trained separately on curated IEDB data
and are not retrained on submitted outcomes.
What it screens
Two deliberately distinct engines
The small-molecule models don't apply to a 300-residue protein — so proteins get a
real sequence-based analysis instead of a fake QSAR score.
Small molecules
SMILES · InChI · MOL/SDF → QSAR toxicology report
Eight hazard endpoints with calibrated probability
Structural-alert (toxicophore) matches with rationale
90% split-conformal prediction sets
Applicability-domain novelty gate + 2D depiction
Scaffold-split evaluation (no analog leakage)
Proteins & biologics
FASTA · sequence → immunogenicity + developability
Designs, not just scores — de-immunization candidates
Coverage
Eight toxicology endpoints
Each is an independent binary hazard model; severity weights how much it drives the
overall risk band.
Carcinogenicitytumor formation on chronic exposure
Acute systemic toxicitylow LD50 after single exposure
Cardiotoxicity (hERG)QT prolongation / arrhythmia
Developmental toxicityteratogenicity
Hepatotoxicitydrug-induced liver injury
Mutagenicity (Ames)bacterial reverse mutation
NeurotoxicityCNS / peripheral effects
Skin sensitizationallergic contact dermatitis
Biologics engineering
It doesn't just score — it proposes redesigns
A suite of sequence-aware capabilities for antibody and protein engineers. Each is an
in-silico hypothesis to prioritize wet-lab work, never a verified fix.
Junctional-neoepitope scanning
In fusions and multispecifics, flags T-cell epitopes created at the seam —
present in neither parent domain — across auto-detected linkers.
De-immunization suggester
Proposes conservative BLOSUM62 substitutions the model predicts remove an epitope —
anchors first, Cys/Pro/glyco fixed, with a greedy best-pair fallback.
Antibody CDR-awareness
Detects VH/VL, annotates CDR1/2/3 vs framework, and tags a risk as a redesign target
(CDR) or a humanization gap (framework).
Expanded HLA panels
Selectable epitope panels — HLA-DR, DR/DQ/DP, and a supplementary class-I CD8 panel —
each per-allele CV-AUC gated (0.80–0.93).
CamSol-style solubility
A continuous per-residue intrinsic-solubility profile from hydrophobicity, charge and
β-propensity — flags aggregation-prone regions.
Calibration + conformal
Every endpoint is isotonic-calibrated and carries a 90%-coverage conformal set — a
two-way {clean, hazard} means “can’t distinguish.”
ESMFold surface refinement
Optionally folds the sequence to down-weight buried liabilities, up-weight exposed
ones, and cluster conformational patches. GPU-gated; degrades gracefully.
Plain-language rationale
Every report explains why — the drivers behind the overall band and the
developability call, in language a reviewer can act on.
Honest by design
Calibrated confidence beats a confident guess.
A safety tool that overstates certainty is worse than none. mltox is built to tell you
when it doesn’t know — and to keep its numbers honest about novel chemistry.