Add DTI Tutorial

_toc.yml

@@ -23,3 +23,4 @@ parts:
 - caption: Tutorials
   chapters:
   - file: tutorials/brain-disorder-diagnosis/notebook
+  - file: tutorials/drug-target-interaction/notebook-cross-domain

tutorials/drug-target-interaction/configs.py

@@ -0,0 +1,109 @@
+from yacs.config import CfgNode
+
+_C = CfgNode()
+
+# ---------------------------------------------------------------------------- #
+# DATA setting
+# ---------------------------------------------------------------------------- #
+_C.DATA = CfgNode()
+_C.DATA.DATASET = None  # Name of the dataset to use
+_C.DATA.SPLIT = None  # Data splitting strategy
+
+# ---------------------------------------------------------------------------- #
+# Drug feature extractor
+# ---------------------------------------------------------------------------- #
+_C.DRUG = CfgNode()
+_C.DRUG.NODE_IN_FEATS = 7  # Number of input node features
+_C.DRUG.NODE_IN_EMBEDDING = (
+    128  # Dimensionality of input node features after linear transformation
+)
+_C.DRUG.PADDING = True  # Whether to apply padding
+_C.DRUG.HIDDEN_LAYERS = [
+    128,
+    128,
+    128,
+]  # Sizes of hidden layers in the GCN feature extractor
+_C.DRUG.MAX_NODES = 290  # Max number of nodes to pad to (used when PADDING=True)
+
+# ---------------------------------------------------------------------------- #
+# Protein feature extractor
+# ---------------------------------------------------------------------------- #
+_C.PROTEIN = CfgNode()
+_C.PROTEIN.NUM_FILTERS = [
+    128,
+    128,
+    128,
+]  # Number of filters in each convolutional layer
+_C.PROTEIN.KERNEL_SIZE = [3, 6, 9]  # Kernel size for each convolutional layer
+_C.PROTEIN.EMBEDDING_DIM = 128  # Dimension of character embedding for amino acids
+_C.PROTEIN.PADDING = True  # Whether to apply zero-padding to the embedding
+
+# ---------------------------------------------------------------------------- #
+# BCN setting
+# ---------------------------------------------------------------------------- #
+_C.BCN = CfgNode()
+_C.BCN.HEADS = 2  # Number of attention heads in the Bilinear Attention Network
+
+# ---------------------------------------------------------------------------- #
+# MLP decoder
+# ---------------------------------------------------------------------------- #
+_C.DECODER = CfgNode()
+_C.DECODER.NAME = "MLP"  # Decoder type
+_C.DECODER.IN_DIM = 256  # Input dimension to the MLP (typically fused BAN feature size)
+_C.DECODER.HIDDEN_DIM = 512  # Hidden layer size in the MLP
+_C.DECODER.OUT_DIM = 128  # Output dimension before the final classification layer
+_C.DECODER.BINARY = 1  # Number of output classes
+
+# ---------------------------------------------------------------------------- #
+# SOLVER
+# ---------------------------------------------------------------------------- #
+_C.SOLVER = CfgNode()
+_C.SOLVER.MAX_EPOCH = 100  # Total number of training epochs
+_C.SOLVER.BATCH_SIZE = 64  # Batch size for training and evaluation
+_C.SOLVER.NUM_WORKERS = 0  # Number of subprocesses for data loading
+_C.SOLVER.LEARNING_RATE = 5e-5  # Learning rate for the main model
+_C.SOLVER.DA_LEARNING_RATE = (
+    1e-3  # Learning rate for the domain adaptation (if DA is enabled)
+)
+_C.SOLVER.SEED = 2048  # Random seed for reproducibility
+
+# ---------------------------------------------------------------------------- #
+# RESULT
+# ---------------------------------------------------------------------------- #
+_C.RESULT = CfgNode()
+_C.RESULT.SAVE_MODEL = True  # Whether to save model checkpoints during training
+
+# ---------------------------------------------------------------------------- #
+# Domain adaptation
+# ---------------------------------------------------------------------------- #
+_C.DA = CfgNode()
+_C.DA.TASK = (
+    False  # False = in-domain splitting task, True = cross-domain splitting task
+)
+_C.DA.METHOD = "CDAN"  # Domain adaptation method to use
+_C.DA.USE = False  # Whether to enable domain adaptation
+_C.DA.INIT_EPOCH = 10  # Number of epochs to wait before applying domain adaptation
+_C.DA.LAMB_DA = 1  # Initial value of λ (lambda) used to weight the domain adaptation loss in the total loss   # Total loss = model loss + λ * domain loss
+_C.DA.RANDOM_LAYER = False  # Whether to use a random projection layer in CDAN
+_C.DA.ORIGINAL_RANDOM = False  # If True, uses the original RandomLayer from the CDAN paper (multi-input form)  # If False, uses a simplified linear layer implementation.
+_C.DA.RANDOM_DIM = None  # Output dimensionality of the random layer (only used if RANDOM_LAYER is True)
+_C.DA.USE_ENTROPY = True  # Whether to use entropy-based weighting when computing domain adversarial loss
+
+# ---------------------------------------------------------------------------- #
+# Comet config, ignore it If not installed.
+# ---------------------------------------------------------------------------- #
+_C.COMET = CfgNode()
+_C.COMET.USE = (
+    True  # Enable Comet logging (set True if Comet is installed and configured)
+)
+_C.COMET.PROJECT_NAME = "drugban-23-May"  # Comet project name (if applicable)
+_C.COMET.EXPERIMENT_NAME = None  # Optional experiment name (e.g., 'drugban-run-1')
+_C.COMET.TAG = None  # Comet tags (optional)
+_C.COMET.API_KEY = ""  # Comet API key (leave blank if unused)
+
+
+# ---------------------------------------------------------------------------- #
+# Function to return a clone of the default config
+# ---------------------------------------------------------------------------- #
+def get_cfg_defaults():
+    return _C.clone()

tutorials/drug-target-interaction/experiments/DA_cross_domain.yaml

@@ -0,0 +1,30 @@
+# This is for cross-domain experiments using DrugBAN with domain adaptation.
+
+DATA:
+  DATASET: "bindingdb" # bindingdb, biosnap
+  SPLIT: "cluster"
+
+SOLVER:
+  BATCH_SIZE: 32
+  MAX_EPOCH: 100
+  LEARNING_RATE: 1e-4
+  DA_LEARNING_RATE: 5e-5
+  SEED: 20
+
+DA:
+  TASK: True
+  USE: True
+  METHOD: "CDAN"
+  USE_ENTROPY: False
+  RANDOM_LAYER: True
+  ORIGINAL_RANDOM: True
+  RANDOM_DIM: 256
+  INIT_EPOCH: 10
+
+DECODER:
+  BINARY: 2
+
+# Config below only when you use comet
+COMET:
+  EXPERIMENT_NAME: "DA_cross_domain"
+  TAG: "DrugBAN_CDAN"

tutorials/drug-target-interaction/experiments/non_DA_cross_domain.yaml

@@ -0,0 +1,24 @@
+# This is for cross-domain experiments using DrugBAN without domain adaptation.
+
+
+DATA:
+  DATASET: "bindingdb" # bindingdb, biosnap
+  SPLIT: "cluster"
+
+SOLVER:
+  BATCH_SIZE: 32
+  MAX_EPOCH: 100
+  LEARNING_RATE: 5e-5
+  SEED: 20
+
+DA:
+  TASK: True
+  USE: False
+
+DECODER:
+  BINARY: 2
+
+# Config below only when you use comet
+COMET:
+  EXPERIMENT_NAME: "Non_DA_cross_domain"
+  TAG: "DrugBAN_Vanilla"

tutorials/drug-target-interaction/experiments/non_DA_in_domain.yaml

@@ -0,0 +1,23 @@
+# This is for in-domain experiments using DrugBAN without domain adaptation.
+
+DATA:
+  DATASET: "bindingdb"  # bindingdb, biosnap
+  SPLIT: "random"       # random
+
+SOLVER:
+  BATCH_SIZE: 64
+  MAX_EPOCH: 100
+  LEARNING_RATE: 5e-5
+  SEED: 20
+
+DA:
+  TASK: False
+  USE: False
+
+DECODER:
+  BINARY: 1
+
+# Config below only when you use comet
+COMET:
+  EXPERIMENT_NAME: "Non_DA_in_domain"
+  TAG: "DrugBAN_Vanilla"