Screen compounds for structural toxicity alerts using PAINS filters, BRENK filters, and NIH filters for safer drug discovery. Learn more
Screen compounds for structural toxicity alerts using PAINS filters, BRENK filters, and NIH filters for safer drug discovery. Learn more
Screen compounds for structural toxicity alerts using PAINS filters, BRENK filters, and NIH filters for safer drug discovery. Learn more
Predict ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties from SMILES strings using machine learning models trained on Therapeutics Data Commons datasets.
Predict 22 ADMET properties from SMILES strings with the native Admetica Chemprop models from Datagrok.
Identify toxic, reactive, and pharmacokinetically problematic molecular fragments using structural alert patterns
Predict toxicity and synthetic accessibility of small molecules using machine learning. eToxPred combines toxicity risk assessment with synthetic accessibility scoring to help prioritize drug candidates.
Screen for lead-like compounds using stricter molecular descriptor criteria than Lipinski or Veber rules for early-stage drug discovery
Screen compounds for Pan-Assay Interference patterns that cause false positives in biological assays
Quantitative estimate for protein-protein interaction inhibitor potential. Evaluates drug-likeness for compounds targeting PPIs.
Veber's Rule predicts oral bioavailability by evaluating molecular weight, LogP, hydrogen bond donors/acceptors, and rotatable bonds
Lipinski's Rule of Five predicts whether compounds will be orally bioavailable by evaluating molecular weight, LogP, hydrogen bond donors, and acceptors.
AF2BIND predicts ligand-binding residues from a protein structure using AlphaFold2 pair representations and a 20-residue bait sequence.
Predict ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties from SMILES strings using machine learning models trained on Therapeutics Data Commons datasets.
Predict 22 ADMET properties from SMILES strings with the native Admetica Chemprop models from Datagrok.
Identify toxic, reactive, and pharmacokinetically problematic molecular fragments using structural alert patterns
Predict toxicity and synthetic accessibility of small molecules using machine learning. eToxPred combines toxicity risk assessment with synthetic accessibility scoring to help prioritize drug candidates.
Screen for lead-like compounds using stricter molecular descriptor criteria than Lipinski or Veber rules for early-stage drug discovery
Screen compounds for Pan-Assay Interference patterns that cause false positives in biological assays
Quantitative estimate for protein-protein interaction inhibitor potential. Evaluates drug-likeness for compounds targeting PPIs.
Veber's Rule predicts oral bioavailability by evaluating molecular weight, LogP, hydrogen bond donors/acceptors, and rotatable bonds
Lipinski's Rule of Five predicts whether compounds will be orally bioavailable by evaluating molecular weight, LogP, hydrogen bond donors, and acceptors.
AF2BIND predicts ligand-binding residues from a protein structure using AlphaFold2 pair representations and a 20-residue bait sequence.
Configure inputs to begin
Set options on the left, then click “Submit job” — or start from an example.
Toxicity screening
Toxicity prediction screens compounds for problematic molecular fragments before expensive biological testing. Rather than predicting specific toxicity endpoints, structural alert filters match known toxic, reactive, or interference-prone substructures curated from decades of medicinal chemistry. This makes them useful throughout drug discovery, from filtering compound libraries and triaging virtual screening hits to tracking safety profiles during lead optimization.
Keep in mind that structural alerts are context-independent: a flagged substructure may be benign in certain scaffolds, and many approved drugs trigger alerts (aspirin flags Brenk patterns for its acetyl group). They also cannot cover every toxic structure or explain the specific toxicity mechanism. Alerts work best as one layer in a broader safety assessment alongside druglikeness filters like Lipinski's Rule of Five, Veber's Rule, and the Lead-Likeness Filter, not as a standalone pass/fail gate.
ToxPred 2.0 is ProteinIQ's own scoring algorithm that combines three established structural alert filter sets into a single weighted risk score:
Each filter set runs its SMARTS patterns against the input molecule. Alerts from all enabled filters combine into a risk score from 0 to 1:
where is the per-alert weight for filter and is the number of detected alerts. Each alert adds its weight to the total, and the score is capped at 1.0. For example, a compound with 2 PAINS alerts and 1 Brenk alert scores .
Compounds are then classified based on their score:
A compound is classified as Safe only when it triggers zero alerts across all filter sets.
ProteinIQ screens compounds for structural toxicity alerts using PAINS, Brenk, and NIH filters directly in the browser with no installation required.
Enter SMILES strings in the text area, one per line. Compound names can be included using tab-separated format (aspirin\tCC(=O)Oc1ccccc1C(=O)O). File upload is supported for .smi, .csv, and .smiles formats, and compounds can also be fetched from PubChem by name or CID.
All three filter sets run by default. To focus on a specific category, toggle off the ones not needed.
| Setting | Description |
|---|---|
PAINS filter | Screen for pan-assay interference compounds (PAINS A, B, C). Default: on. |
Brenk filter | Screen for toxic, reactive, and pharmacokinetically problematic fragments. Default: on. |
NIH filter | Screen for compounds with problematic functional groups (NIH/MLPCN). Default: on. |
If all three are toggled off, the tool falls back to running all of them.
Each compound receives a per-filter alert count, a combined risk score, and a categorical toxicity classification.
| Column | Description |
|---|---|
Name | Compound name (if provided) or SMILES |
SMILES | Input SMILES string |
Toxicity | Categorical classification: Low, Moderate, or High |
Risk score | Weighted composite score from 0 (no alerts) to 1 (maximum risk) |
Total alerts | Sum of alerts across all enabled filters |
PAINS alerts | Number of PAINS substructure matches |
Brenk alerts | Number of Brenk substructure matches |
NIH alerts | Number of NIH substructure matches |
PAINS patterns | Names of matched PAINS patterns |
Brenk patterns | Names of matched Brenk patterns |
NIH patterns | Names of matched NIH patterns |