Train a DTI model by pairing a drug and a protein encoder, tune train_epoch, LR, batch_size and cls_hidden_dims, then save the checkpoint with its config.

DeepPurpose supports 50+ neural architecture combinations from 15+ compound encoders and 7+ protein encoders. This guide covers training DTI prediction models.

Basic training loop

from DeepPurpose import DTI as models
from DeepPurpose import utils, dataset

# Load and process data
X_drugs, X_targets, y = dataset.load_process_DAVIS(
    path='./data',
    binary=False,
    convert_to_log=True,
    threshold=30
)

# Process data with chosen encoders
train, val, test = utils.data_process(
    X_drug=X_drugs,
    X_target=X_targets,
    y=y,
    drug_encoding="Morgan",
    target_encoding="CNN",
    split_method="random",
    frac=[0.7, 0.1, 0.2],
)

# Configure and generate model
config = utils.generate_config(
    drug_encoding="Morgan",
    target_encoding="CNN",
    cls_hidden_dims=[1024, 1024, 512],
    train_epoch=100,
    LR=0.001,
    batch_size=256,
    cnn_target_filters=[32, 64, 96],
    cnn_target_kernels=[4, 8, 12],
)

model = models.model_initialize(**config)
model.train(train, val, test)
model.save_model("outputs/model")

Architecture options

DeepPurpose uses a modular architecture where drug and target encoders are combined with a classifier head:

Drug SMILES --> [Drug Encoder] --\
                                  +--> [Classifier MLP] --> Affinity Score
Protein Seq --> [Target Encoder]-/

Common encoder combinations:

Morgan + AAC: Fast baseline, good for small datasets
CNN + CNN: Balanced performance and speed
Transformer + Transformer: Best for large datasets
MPNN + CNN: Graph-based drug representation with sequence-based protein encoding

Hyperparameter tips

Key hyperparameters to tune:

Parameter	Default	Description
`train_epoch`	100	Number of training epochs
`LR`	0.001	Learning rate
`batch_size`	256	Training batch size
`cls_hidden_dims`	[1024, 1024, 512]	Classifier MLP hidden layer sizes
`cnn_drug_filters`	[32, 64, 96]	CNN filter sizes for drug encoder
`cnn_target_filters`	[32, 64, 96]	CNN filter sizes for target encoder
`transformer_n_layer_drug`	8	Transformer layers for drug encoder
`transformer_n_layer_target`	8	Transformer layers for target encoder

Tuning recommendations:

Start with small epochs (10-20) and increase after verifying loss trends.
Use batch_size to fill GPU memory without OOM; scale LR with batch size cautiously.
For small datasets (<1000 samples), use simpler encoders (Morgan, AAC) and smaller hidden dims.
For GNN encoders (MPNN, DGL_*), reduce batch size as they are more memory-intensive.

Validation and metrics

DeepPurpose automatically evaluates on validation data during training:

# Training automatically reports validation metrics
model.train(train, val, test)

# For manual evaluation
from DeepPurpose import utils
y_pred = model.predict(test)

# Regression metrics
from sklearn.metrics import mean_squared_error, r2_score
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

# Classification metrics (if binary=True)
from sklearn.metrics import roc_auc_score, average_precision_score
auc = roc_auc_score(y_test, y_pred)
aupr = average_precision_score(y_test, y_pred)

The training process logs:

Training and validation loss per epoch
MSE, Pearson correlation, Concordance Index (CI) for regression
AUROC, AUPRC for classification tasks

Checkpoints

Save model.pt, encoder configs, and the command/args used.
Keep the tokenizer/featurizer versions with the checkpoint for reproducibility.

Resource guidance

CPU-only runs: lower batch size (e.g., 64) and fewer workers.
GPU runs: ensure CUDA toolkit matches torch; set TORCH_CUDA_ARCH_LIST if building from source.

Multi-task and virtual screening

DeepPurpose also supports:

# Virtual screening: predict all drug-target pairs
from DeepPurpose import virtual_screening
result = virtual_screening.virtual_screening(
    X_drugs, X_targets, model, drug_names, target_names
)

# Drug repurposing for a specific target
from DeepPurpose import oneliner
oneliner.repurpose(
    target='MKKFFDSRREQGGSGLGSGSSGGGGSTSGLGSGYIGRVFGIGRQQVTVDEVLAEGGFAIVFLVRTSNGMKCALKRMFVNNEHDLQVCKREIQIMRDLSGHKNIVGYIDSSINNVSSGDVWEVLILMDFCRGGQVVNLMNQRLQTGFTENEVLQIFCDTCEAVARLHQCKTPIIHRDLKVENILLHDRGHYVLCDFGSATNKFQNPQTEGVNAVEDEIKKYTTLSYRAPEMVNLYSGKIITTKADIWALGCLLYKLCYFTLPFGESQVAICDGNFTIPDNSRYSQDMHCLIRYMLEPDPDKRPDIYQVSYFSFKLLKKECPIPNVQNSPIPAKLPEPVKASEAAAKKTQPKARLTDPIPTTETSIAPRQRPKAGQTQPNPGILPIQPALTPRKRATVQPPPQAAGSSNQPGLLASVPQPKPQAPPSQPLPQTQAKQPQAPPTPQQTPSTQAQGLPAQAQATPQHQQQLFLKQQQQQQQPPPAQQQPAGTFYQQQQAQTQQFQAVHPATQKPAIAQFPVVSQGGSQQQLMQNFYQQQQQQQQQQQQQQLATALHQQQLMTQQAALQQKPTMAAGQQPQPQPAAAPQPAPAQEPAIQAPVRQQPKVQTTPPPAVQGQKVGSLTPPSSPKTQRAGHRRILSDVTHSAVFGVPASKSTQLLQAAAAEASLNKSKSATTTPSGSPRTSQQNVYNPSEGSTWNPFDDDNFSKLTAEELLNKDFAKLGEGKHPEKLGGSAESLIPGFQSTQGDAFATTSFSAGTAEKRKGGQTVDSGLPLLSVSDPFIPLQVPDAPEKLIEGLKSPDTSLLLPDLLPMTDPFGSTSDAVIEKADVAVESLIPGLEPPVPQRLPSQTESVTSNRTDSLTGEDSLLDCSLLSNPTTDLLEEFAPTAISAPVHKAAEDSNLISGFDVPEGSDKVAEDEFDPIPVLITKNPQGGHSRNSSGSSESSLPNLARSLLLVDQLIDL',
    target_name='JAK3',
    drug_names=drug_list,
    save_path='./repurposing_result'
)

Training & tuning