saved_models/metadata.json

{
  "training_date": "2026-01-19 14:03:28",
  "model_versions": {
    "tensorflow": "2.16.2",
    "xgboost": "3.1.3",
    "sklearn": "1.4.0",
    "python": "3.10"
  },
  "dataset_info": {
    "num_drugs": 1410,
    "num_diseases": 1573,
    "num_associations": 42200,
    "num_targets": 2018
  },
  "feature_engineering": {
    "morgan_fp_bits": 1024,
    "morgan_fp_radius": 2,
    "target_encoding": "multi-hot",
    "disease_encoding_ml": "one-hot",
    "disease_encoding_dnn": "embedding"
  },
  "model_architecture": {
    "dnn_drug_layers": [
      256,
      128
    ],
    "dnn_embedding_dim": 32,
    "dnn_dropout": 0.3,
    "dnn_l2_reg": 0.001
  },
  "training_config": {
    "negative_sampling": "1:1 balanced",
    "train_test_split": 0.2,
    "random_seed": 42,
    "dnn_epochs": 30,
    "dnn_batch_size": 128,
    "dnn_learning_rate": 0.0001
  }
}