| name | admet-prediction |
| description | Predict comprehensive ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties for drug candidate molecules using GraphMVP ensemble models. Use this skill when: (1) Predicting blood-brain barrier penetration, (2) Assessing side effect profiles, (3) Estimating Caco-2 permeability, half-life, or LD50 toxicity, (4) Evaluating drug-likeness and safety of molecules.
|
| license | MIT |
| category | admet-prediction |
| tags | ["admet","toxicity","drug-discovery","pharmacokinetics","graphmvp"] |
ADMET Prediction
Predict comprehensive ADMET properties for drug candidate molecules using GraphMVP ensemble models.
When to Use
- User asks to predict ADMET properties for a molecule
- User provides a drug candidate and wants safety assessment
- User needs blood-brain barrier penetration prediction
- User wants to evaluate toxicity (LD50) or side effects (SIDER)
- User requests pharmacokinetic properties (half-life, Caco-2)
Workflow
Step 1: Load Molecule
Create molecule from SMILES string.
from open_biomed.data import Molecule
molecule = Molecule.from_smiles("CC(=O)OC1=CC=CC=C1C(=O)O")
Step 2: Build ADMET Pipeline
Initialize ensemble pipeline with all GraphMVP checkpoints.
from open_biomed.core.pipeline import InferencePipeline, EnsemblePipeline
pipelines = {
"BBBP": InferencePipeline(
task="molecule_property_prediction", model="graphmvp",
model_ckpt="./checkpoints/server/graphmvp-BBBP.ckpt",
additional_config="./configs/dataset/bbbp.yaml", device="cuda:0"),
"SIDER": InferencePipeline(
task="molecule_property_prediction", model="graphmvp",
model_ckpt="./checkpoints/server/graphmvp-SIDER.ckpt",
additional_config="./configs/dataset/sider.yaml", device="cuda:0"),
}
pipeline = EnsemblePipeline(pipelines)
Step 3: Run Predictions
Execute predictions for each ADMET property.
bbb_result = pipeline.run(molecule=molecule, task="BBBP")
sider_result = pipeline.run(molecule=molecule, task="SIDER")
caco2_result = pipeline.run(molecule=molecule, task="caco2_wang")
half_life_result = pipeline.run(molecule=molecule, task="half_life_obach")
ld50_result = pipeline.run(molecule=molecule, task="ld50_zhu")
Expected Outputs
| Task | Output Type | Description |
|---|
| BBBP | float [0-1] | Probability of BBB penetration |
| SIDER | list[27 floats] | Side effect probabilities per category |
| caco2_wang | float | Log permeability (cm/s) |
| half_life_obach | float | Log half-life (hours) |
| ld50_zhu | float | Log LD50 (mg/kg) |
Interpretation Guide
BBB Penetration
| Value | Interpretation |
|---|
| > 0.5 | Likely crosses BBB |
| < 0.5 | Unlikely to cross BBB |
Caco-2 Permeability
| Value (log cm/s) | Interpretation |
|---|
| > -5 | High absorption |
| -6 to -5 | Moderate absorption |
| < -6 | Low absorption |
LD50 Toxicity
| Value (log mg/kg) | Toxicity Level |
|---|
| < 1 | Highly toxic (<10 mg/kg) |
| 1-2 | Moderately toxic (10-100 mg/kg) |
| 2-3 | Slightly toxic (100-1000 mg/kg) |
| > 3 | Low toxicity (>1000 mg/kg) |
SIDER Side Effects
Values range 0-1. Categories with > 0.7 indicate high risk of that side effect.
Error Handling
Checkpoint Not Found
Symptom: FileNotFoundError: graphmvp-*.ckpt
Solution: Ensure checkpoints exist in ./checkpoints/server/:
ls checkpoints/server/graphmvp-*.ckpt
CUDA Out of Memory
Symptom: RuntimeError: CUDA out of memory
Solution: Use CPU instead:
device="cpu"
Invalid SMILES
Symptom: Molecule fails to parse
Solution: Validate SMILES format or use molecule name lookup via PubChem.
Example
Input: aspirin (CC(=O)OC1=CC=CC=C1C(=O)O)
Output:
BBB Penetration: 0.19 (does NOT cross BBB)
Caco-2: -4.68 (moderate absorption)
Half-life: -7.06 (short half-life)
LD50: 2.06 (moderate toxicity ~115 mg/kg)
Top Side Effects:
Skin disorders: 0.80
Nervous system: 0.78
Gastrointestinal: 0.78
See Also
examples/basic_example.py - Full runnable example with all propertiesreferences/sider_categories.md - Complete SIDER category listreferences/interpretation.md - Detailed interpretation guidelines
