Profile small-molecule ADMET properties across 22 Chemprop-based prediction models. Learn more
Configure inputs to begin
Set options on the left, then click “Submit job” — or start from an example.
Ethanol (simple alcohol)
Caffeine (drug molecule)
Multiple compounds
Profile small-molecule ADMET properties across 22 Chemprop-based prediction models. Learn more
Configure inputs to begin
Set options on the left, then click “Submit job” — or start from an example.
Ethanol (simple alcohol)
Caffeine (drug molecule)
Multiple compounds
Admetica is Datagrok's open-source ADMET prediction toolkit for small molecules. In ProteinIQ, the current tool follows the active published runtime surface available in the published admetica==1.4.1 package and the published web runtime: you can predict 22 pharmacokinetic and toxicity properties from SMILES strings.
Admetica uses Chemprop graph neural networks trained per endpoint. Each model predicts one experimentally grounded ADMET property, so the tool is useful for early compound triage, lead optimization, and comparing candidates before synthesis or assay work.
Paste one SMILES per line, upload an .sdf, .csv, .smi, .smiles, or .txt file, or fetch structures from PubChem. Tab-delimited name<TAB>SMILES input is supported if you want to preserve your own identifiers.
| Format | Description |
|---|---|
| Plain SMILES | One compound per line; generated row identifiers remain unique |
| Tab-delimited | compound_name<TAB>SMILES |
| SMI | Standard SMILES name records |
| SDF | Multiple structures from a structure-data file |
| CSV | Table containing a required smiles column and optional ids, id, name, compound_id, or compound identifier column |
| PubChem fetch | Resolve a compound name or CID to SMILES |
ProteinIQ supports the native property subset selector. By default, all 22 available models are selected. You can deselect endpoints you do not need to reduce result columns and run only the models relevant to your screen.
| Group | Property |
|---|---|
| Absorption | Caco2 |
| Absorption | Lipophilicity |
| Absorption | Solubility |
| Absorption | PGP-Inhibitor |
| Absorption | PGP-Substrate |
| Distribution | PPBR |
| Distribution | VDss |
| Metabolism | CYP1A2-Inhibitor |
| Metabolism | CYP2C9-Inhibitor |
| Metabolism | CYP2C19-Inhibitor |
| Metabolism | CYP2D6-Inhibitor |
| Metabolism | CYP3A4-Inhibitor |
| Metabolism | CYP1A2-Substrate |
| Metabolism | CYP2C9-Substrate |
| Metabolism | CYP2C19-Substrate |
| Metabolism | CYP2D6-Substrate |
| Metabolism | CYP3A4-Substrate |
| Excretion | CL-Hepa |
| Excretion | CL-Micro |
| Excretion | Half-Life |
| Toxicity | hERG |
| Toxicity | LD50 |
Results are returned as a spreadsheet with one row per molecule. The published runtime lowercases its prediction column keys; ProteinIQ preserves those keys and applies readable display labels in the spreadsheet.
| Column | Meaning |
|---|---|
ids | ProteinIQ transport identifier for the submitted row |
smiles | Submitted SMILES string |
caco2 | Predicted Caco-2 permeability |
lipophilicity | Predicted lipophilicity |
solubility | Predicted aqueous solubility |
pgp-inhibitor | P-glycoprotein inhibitor prediction |
pgp-substrate | P-glycoprotein substrate prediction |
ppbr | Plasma protein binding rate |
vdss | Volume of distribution at steady state |
cyp1a2-inhibitor | CYP1A2 inhibition prediction |
cyp2c9-inhibitor | CYP2C9 inhibition prediction |
cyp2c19-inhibitor | CYP2C19 inhibition prediction |
cyp2d6-inhibitor | CYP2D6 inhibition prediction |
cyp3a4-inhibitor | CYP3A4 inhibition prediction |
cyp1a2-substrate | CYP1A2 substrate prediction |
cyp2c9-substrate | CYP2C9 substrate prediction |
cyp2c19-substrate | CYP2C19 substrate prediction |
cyp2d6-substrate | CYP2D6 substrate prediction |
cyp3a4-substrate | CYP3A4 substrate prediction |
cl-hepa | Hepatocyte clearance prediction |
cl-micro | Microsome clearance prediction |
half-life | Half-life prediction |
herg | hERG liability prediction |
ld50 | Acute toxicity prediction |
If you run only a subset of models, the output contains only ids, smiles, and the selected native property columns.
Admetica mixes regression and classification endpoints. Continuous outputs such as caco2, solubility, cl-hepa, cl-micro, half-life, and ld50 should be interpreted in the context of the published training data and published endpoint definitions. Classification-style outputs such as the CYP, P-gp, and herg endpoints indicate predicted liabilities or substrate behavior for the named target.
Use the predictions as triage signals rather than hard acceptance rules. Compounds outside the model training domain, including unusual chemotypes and larger non-drug-like structures, can produce less reliable estimates.
this tool intentionally stays thin. ProteinIQ preserves the native runtime predictions and returns the currently available 22-model surface. The tool keeps ids and smiles for workflow tracking, but it does not add ProteinIQ-only descriptor columns to the exported result table.
admetica==1.4.1 runtime surface rather than every asset present in the original repository.
Predict ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties from SMILES strings using machine learning models trained on Therapeutics Data Commons datasets.
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.
Identify toxic, reactive, and pharmacokinetically problematic molecular fragments using structural alert patterns
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.
Screen compounds for structural toxicity alerts using PAINS, Brenk, and NIH filters. For focused screening, see PAINS Filter, Brenk Filter, or Veber's Rule.
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.
Admetica is Datagrok's open-source ADMET prediction toolkit for small molecules. In ProteinIQ, the current tool follows the active published runtime surface available in the published admetica==1.4.1 package and the published web runtime: you can predict 22 pharmacokinetic and toxicity properties from SMILES strings.
Admetica uses Chemprop graph neural networks trained per endpoint. Each model predicts one experimentally grounded ADMET property, so the tool is useful for early compound triage, lead optimization, and comparing candidates before synthesis or assay work.
Paste one SMILES per line, upload an .sdf, .csv, .smi, .smiles, or .txt file, or fetch structures from PubChem. Tab-delimited name<TAB>SMILES input is supported if you want to preserve your own identifiers.
| Format | Description |
|---|---|
| Plain SMILES | One compound per line; generated row identifiers remain unique |
| Tab-delimited | compound_name<TAB>SMILES |
| SMI | Standard SMILES name records |
| SDF | Multiple structures from a structure-data file |
| CSV | Table containing a required smiles column and optional ids, id, name, compound_id, or compound identifier column |
| PubChem fetch | Resolve a compound name or CID to SMILES |
ProteinIQ supports the native property subset selector. By default, all 22 available models are selected. You can deselect endpoints you do not need to reduce result columns and run only the models relevant to your screen.
| Group | Property |
|---|---|
| Absorption | Caco2 |
| Absorption | Lipophilicity |
| Absorption | Solubility |
| Absorption | PGP-Inhibitor |
| Absorption | PGP-Substrate |
| Distribution | PPBR |
| Distribution | VDss |
| Metabolism | CYP1A2-Inhibitor |
| Metabolism | CYP2C9-Inhibitor |
| Metabolism | CYP2C19-Inhibitor |
| Metabolism | CYP2D6-Inhibitor |
| Metabolism | CYP3A4-Inhibitor |
| Metabolism | CYP1A2-Substrate |
| Metabolism | CYP2C9-Substrate |
| Metabolism | CYP2C19-Substrate |
| Metabolism | CYP2D6-Substrate |
| Metabolism | CYP3A4-Substrate |
| Excretion | CL-Hepa |
| Excretion | CL-Micro |
| Excretion | Half-Life |
| Toxicity | hERG |
| Toxicity | LD50 |
Results are returned as a spreadsheet with one row per molecule. The published runtime lowercases its prediction column keys; ProteinIQ preserves those keys and applies readable display labels in the spreadsheet.
| Column | Meaning |
|---|---|
ids | ProteinIQ transport identifier for the submitted row |
smiles | Submitted SMILES string |
caco2 | Predicted Caco-2 permeability |
lipophilicity | Predicted lipophilicity |
solubility | Predicted aqueous solubility |
pgp-inhibitor | P-glycoprotein inhibitor prediction |
pgp-substrate | P-glycoprotein substrate prediction |
ppbr | Plasma protein binding rate |
vdss | Volume of distribution at steady state |
cyp1a2-inhibitor | CYP1A2 inhibition prediction |
cyp2c9-inhibitor | CYP2C9 inhibition prediction |
cyp2c19-inhibitor | CYP2C19 inhibition prediction |
cyp2d6-inhibitor | CYP2D6 inhibition prediction |
cyp3a4-inhibitor | CYP3A4 inhibition prediction |
cyp1a2-substrate | CYP1A2 substrate prediction |
cyp2c9-substrate | CYP2C9 substrate prediction |
cyp2c19-substrate | CYP2C19 substrate prediction |
cyp2d6-substrate | CYP2D6 substrate prediction |
cyp3a4-substrate | CYP3A4 substrate prediction |
cl-hepa | Hepatocyte clearance prediction |
cl-micro | Microsome clearance prediction |
half-life | Half-life prediction |
herg | hERG liability prediction |
ld50 | Acute toxicity prediction |
If you run only a subset of models, the output contains only ids, smiles, and the selected native property columns.
Admetica mixes regression and classification endpoints. Continuous outputs such as caco2, solubility, cl-hepa, cl-micro, half-life, and ld50 should be interpreted in the context of the published training data and published endpoint definitions. Classification-style outputs such as the CYP, P-gp, and herg endpoints indicate predicted liabilities or substrate behavior for the named target.
Use the predictions as triage signals rather than hard acceptance rules. Compounds outside the model training domain, including unusual chemotypes and larger non-drug-like structures, can produce less reliable estimates.
this tool intentionally stays thin. ProteinIQ preserves the native runtime predictions and returns the currently available 22-model surface. The tool keeps ids and smiles for workflow tracking, but it does not add ProteinIQ-only descriptor columns to the exported result table.
admetica==1.4.1 runtime surface rather than every asset present in the original repository.Predict ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties from SMILES strings using machine learning models trained on Therapeutics Data Commons datasets.
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.
Identify toxic, reactive, and pharmacokinetically problematic molecular fragments using structural alert patterns
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.
Screen compounds for structural toxicity alerts using PAINS, Brenk, and NIH filters. For focused screening, see PAINS Filter, Brenk Filter, or Veber's Rule.
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.