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")  # Aspirin

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"),
    # See examples/basic_example.py for full pipeline setup
}
pipeline = EnsemblePipeline(pipelines)

Step 3: Run Predictions

Execute predictions for each ADMET property.

# BBB penetration
bbb_result = pipeline.run(molecule=molecule, task="BBBP")

# Side effects (27 categories)
sider_result = pipeline.run(molecule=molecule, task="SIDER")

# Regression properties
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:

# Change device from "cuda:0" to "cpu"
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

admet-prediction