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Capability Domain
Predict toxicity from molecular structure, analyze metabolites, check structural alerts, and compute PK parameters — connecting the chemistry of a molecule to its safety liabilities.
Molecular intelligence answers: what does this molecule's structure tell us about how it will behave in the body? Starting from a SMILES string, AlgoVigilance runs the full molecular pipeline — structural alerts, toxicity predictions, metabolite enumeration, and pharmacokinetic modeling.
SMILES (Simplified Molecular Input Line Entry System) is the text encoding of a molecular structure. AlgoVigilance parses SMILES into a molecular graph, computes Morgan (ECFP4) and topological fingerprints, and uses them as inputs to downstream predictions.
ECFP4 Fingerprint
Circular fingerprint capturing atom environments up to 4 bonds — basis for similarity search
Topological FP
Path-based fingerprint for substructure-aware comparison against known toxicophores
Molecular Descriptors
MW, LogP, HBD, HBA, TPSA, rotatable bonds — inputs to PK and toxicity models
Structural alerts are functional groups or substructures associated with toxicity, reactivity, or poor drug-like properties. AlgoVigilance applies the Brenk rule set (105 fragments), PAINS (Pan Assay Interference Compounds), and Michael acceptor filters.
| Rule Set | Fragments | Concern |
|---|---|---|
| Brenk Rules | 105 | Mutagenicity, genotoxicity, metabolic instability, reactive metabolite formation |
| PAINS Filters | 480 | Pan-assay interference — false positives in HTS, not necessarily toxic |
| Michael Acceptors | 26 | Electrophilic fragments that covalently modify proteins — idiosyncratic toxicity risk |
| Ames Alerts | 44 | Substructures associated with bacterial mutagenicity in Ames test |
| hERG Alerts | 18 | Cardiac ion channel (hERG) binding — QT prolongation and arrhythmia risk |
Quantitative Structure-Activity Relationship (QSAR) models trained on ChEMBL bioassay data predict toxicity endpoints from molecular descriptors. AlgoVigilance reports predictions with confidence intervals derived from the training set applicability domain.
Hepatotoxicity (DILI)
Drug-induced liver injury probability from molecular features — cross-referenced with FDA DILI concern labels
Cardiotoxicity (hERG)
hERG IC50 prediction — QT prolongation potential score with TdP risk stratification
Mutagenicity (Ames)
Bacterial mutagenicity prediction — ICH S2(R1) relevance assessment
Renal Toxicity
Nephrotoxicity probability from renal tubular transporter interactions (OAT1, OAT3, OCT2)
Pharmacokinetic parameters determine drug exposure — and exposure determines the probability of both efficacy and toxicity. AlgoVigilance computes PK parameters from molecular descriptors and known experimental data for structurally similar compounds.
Primary PK Parameters
Hepatic + renal clearance from in vitro data
Tissue partitioning from lipophilicity and protein binding
Derived from CL and Vd — governs dosing interval
Derived PK Parameters
Area under the curve — total drug exposure
Peak concentration at steady state — toxicity threshold comparison
Saturation kinetics — nonlinear PK risk flag
Reactive metabolites are the proximate cause of many idiosyncratic adverse drug reactions. AlgoVigilance enumerates Phase I (CYP-mediated oxidation, reduction) and Phase II (glucuronidation, sulfation) metabolites using Reactome pathway data, then applies structural alert filters to each predicted metabolite. This identifies bioactivation liability — where a safe parent molecule is converted to a reactive species in vivo.
5 Computation Steps
SMILES parsing, structural alert detection (Brenk/PAINS/hERG), toxicity prediction, PK modeling (AUC, CL, Michaelis-Menten), fingerprint similarity
ChEMBL Bioassay Data
2.4M compounds with experimental activity data — the training and reference base for all QSAR and PK predictions
3 Pathway Sources
UniProt protein annotations, Reactome metabolic pathways, and PharmGKB CYP substrate tables for metabolite enumeration
Data Sources
Connect your AI agent to mcp.nexvigilant.com and run the full 5-step molecular pipeline from SMILES to PK parameters.