Load a DeepPurpose checkpoint, predict one drug-target pair or a CSV batch, run virtual_screening or oneliner.repurpose, and export the network to ONNX.

DeepPurpose provides flexible inference options for drug-target interaction (DTI) prediction. This guide covers loading trained models and running predictions.

Load a saved model

from DeepPurpose import DTI as models

# Load a trained model
model = models.model_pretrained(path_dir="outputs/model")

# Or use a pretrained model from the model zoo
model = models.model_pretrained(model='MPNN_CNN_BindingDB')

DeepPurpose includes several pretrained models trained on benchmark datasets:

MPNN_CNN_DAVIS: MPNN drug encoder + CNN target encoder, trained on DAVIS
Morgan_CNN_BindingDB: Morgan fingerprints + CNN, trained on BindingDB
Transformer_CNN_BindingDB: Transformer + CNN, trained on BindingDB

Single prediction

For quick predictions on individual drug-target pairs:

from DeepPurpose import utils

# Single drug-target pair prediction
drug_smiles = "CC(=O)Oc1ccccc1C(=O)O"  # Aspirin
target_sequence = "MKKFFDSRREQGGSGLGSGSSGGGGSTSGLGSGYIGRVFGIGRQQVTVDEVLAE..."

# Encode and predict
X_drug, X_target, _ = utils.data_process(
    X_drug=[drug_smiles],
    X_target=[target_sequence],
    y=[0],  # Dummy label for processing
    drug_encoding="Morgan",
    target_encoding="CNN",
    split_method='no_split'
)

score = model.predict(X_drug, X_target)
print(f"Predicted binding affinity: {score[0]:.4f}")

Batch predict

import pandas as pd
from DeepPurpose import utils

data = pd.read_csv("inputs/predict.csv")

# Process data for prediction (no split needed)
X_drug, X_target, _ = utils.data_process(
    X_drug=data["smiles"].tolist(),
    X_target=data["protein"].tolist(),
    y=[0] * len(data),  # Dummy labels
    drug_encoding="Morgan",
    target_encoding="CNN",
    split_method='no_split'
)

scores = model.predict(X_drug, X_target)
data["predicted_affinity"] = scores
data.to_csv("outputs/predictions.csv", index=False)

Virtual screening

Screen a library of compounds against one or more targets:

from DeepPurpose import virtual_screening

# Screen drug library against target(s)
result = virtual_screening.virtual_screening(
    X_drugs=drug_smiles_list,
    X_targets=target_sequence_list,
    model=model,
    drug_names=drug_names,
    target_names=target_names,
    result_folder="./screening_results"
)

# Results sorted by predicted affinity
print(result.head(10))

Export to ONNX (optional)

If your model uses torch-based encoders, export for serving:

import torch

dummy_drug = torch.randn(1, model.drug_encoder.output_size).cuda()
dummy_target = torch.randn(1, model.target_encoder.output_size).cuda()

torch.onnx.export(
    model.network,
    (dummy_drug, dummy_target),
    "outputs/model.onnx",
    input_names=["drug", "target"],
    output_names=["score"],
    opset_version=17,
)

Run metadata

Log model path, git commit, and DeepPurpose/torch versions.
Include the encoding types (drug_encoding, target_encoding) with outputs.
Save a SUMMARY.json with run args and the number of predictions produced.

Drug repurposing workflow

DeepPurpose provides a streamlined one-liner API for drug repurposing:

from DeepPurpose import oneliner

# Repurpose approved drugs for a new target
oneliner.repurpose(
    target='MKKFFDSRREQGGSGLGSGSSGGGGSTSGLGSGYIGRVFGIGRQQVTVDEVLAE...',
    target_name='SARS-CoV-2 3CL Protease',
    drug_names=['Remdesivir', 'Hydroxychloroquine', 'Lopinavir'],
    save_path='./repurposing_results'
)

The results include ranked predictions with confidence scores for potential drug candidates.

Canvas Visualization

When a DeepPurpose job completes, a result card appears on the canvas.

Card size: 300x250px
Border color: Brown (#795548)
Visualization: Scatter chart of binding probability predictions

Tier 2 Metrics

Binding probability (%)
Interaction score
Drug SMILES
Target sequence snippet

Inference & export