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TauDock\nPredicting the binding affinity of small molecules to proteins using deep learning.","metadata":{"papermill":{"duration":0.014343,"end_time":"2024-06-16T12:41:13.954991","exception":false,"start_time":"2024-06-16T12:41:13.940648","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"# Install Libraries\nWe will use the %pip magic command to install our packages.","metadata":{"papermill":{"duration":0.012531,"end_time":"2024-06-16T12:41:13.980774","exception":false,"start_time":"2024-06-16T12:41:13.968243","status":"completed"},"tags":[]}},{"cell_type":"code","source":"%pip install --user -U aiohttp -q\n%pip install --user -U wandb pandas tqdm torchmetrics fastparquet scikit-learn -q\n%pip install --user -U biopython -q\n%pip install --user -U lightning -q","metadata":{"papermill":{"duration":107.901116,"end_time":"2024-06-16T12:43:01.894727","exception":false,"start_time":"2024-06-16T12:41:13.993611","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Imports\nLet's import as needed.","metadata":{"papermill":{"duration":0.013353,"end_time":"2024-06-16T12:43:01.921175","exception":false,"start_time":"2024-06-16T12:43:01.907822","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import pandas as pd\nimport wandb\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport gc\nimport os\nimport random\nimport logging\nimport random\nimport math\nimport lightning as L\n\nfrom Bio.PDB import PDBParser\nfrom Bio.PDB.DSSP import DSSP\nfrom pathlib import Path\nfrom tqdm.notebook import tqdm\nfrom torchmetrics import AveragePrecision\nfrom torchmetrics.functional.classification import multilabel_average_precision\nfrom torch.utils.data import TensorDataset, Dataset, DataLoader, random_split\nfrom torch.nn.utils.rnn import pad_sequence\nfrom torch.optim import AdamW\nfrom torch.optim.lr_scheduler import ReduceLROnPlateau, CyclicLR\nfrom sklearn.model_selection import StratifiedKFold, train_test_split\nfrom lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint, LearningRateMonitor\nfrom lightning.pytorch.loggers import WandbLogger\nfrom kaggle_secrets import UserSecretsClient","metadata":{"papermill":{"duration":6.375044,"end_time":"2024-06-16T12:43:08.309874","exception":false,"start_time":"2024-06-16T12:43:01.934830","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Path Definitions\nLet's hold the path information in some constants.","metadata":{"papermill":{"duration":0.013378,"end_time":"2024-06-16T12:43:08.337166","exception":false,"start_time":"2024-06-16T12:43:08.323788","status":"completed"},"tags":[]}},{"cell_type":"code","source":"MODEL_PATH = Path(\"/kaggle/input/taudock-epochs/taudock-model.ckpt\")\nBINDING_DIR = Path(\"/kaggle/input/binding-encoded\")\nTRAIN_PARQUET = BINDING_DIR / \"downsized_train.parquet\"\nTEST_PARQUET = BINDING_DIR / \"test.parquet\"\nPDB_FILES = BINDING_DIR / \"PDB\"\nFASTA_FILES  = BINDING_DIR / \"FASTA\"\nSEH = PDB_FILES / \"sEH.pdb\"\nBRD4 = PDB_FILES / \"BRD4.pdb\"\nHSA = PDB_FILES / \"HSA.pdb\"\nSEH_FASTA = FASTA_FILES / \"sEH.fasta\"\nBRD4_FASTA = FASTA_FILES / \"BRD4.fasta\"\nHSA_FASTA = FASTA_FILES  / \"HSA.fasta\"","metadata":{"papermill":{"duration":0.021672,"end_time":"2024-06-16T12:43:08.372084","exception":false,"start_time":"2024-06-16T12:43:08.350412","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Load Train Data\nLet's load everything from the train set into Pandas.","metadata":{"papermill":{"duration":0.013048,"end_time":"2024-06-16T12:43:08.398423","exception":false,"start_time":"2024-06-16T12:43:08.385375","status":"completed"},"tags":[]}},{"cell_type":"code","source":"train = pd.read_parquet(TRAIN_PARQUET)\ntrain","metadata":{"papermill":{"duration":1.526555,"end_time":"2024-06-16T12:43:09.938534","exception":false,"start_time":"2024-06-16T12:43:08.411979","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Load Test Data\nLet's load everything from the test set into Pandas.","metadata":{"papermill":{"duration":0.013364,"end_time":"2024-06-16T12:43:09.967041","exception":false,"start_time":"2024-06-16T12:43:09.953677","status":"completed"},"tags":[]}},{"cell_type":"code","source":"test = pd.read_parquet(TEST_PARQUET)\ntest","metadata":{"papermill":{"duration":0.642658,"end_time":"2024-06-16T12:43:10.623078","exception":false,"start_time":"2024-06-16T12:43:09.980420","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Define HyperParameters\nSome constants to be tuned.","metadata":{"papermill":{"duration":0.013784,"end_time":"2024-06-16T12:43:10.651080","exception":false,"start_time":"2024-06-16T12:43:10.637296","status":"completed"},"tags":[]}},{"cell_type":"code","source":"BATCH_SIZE = 512\nWORKERS = 3\nPREFETCH_FACTOR = 2\nLEARNING_RATE = 1e-3\nMAX_LEARNING_RATE = 1e-5\nWEIGHT_DECAY = 1e-6\nHIDDEN_SIZE = 128\nDROPOUT = 0.27\nEPOCHS = 1\nMAX_LENGTH = 142\nDEBUG = False","metadata":{"papermill":{"duration":0.021315,"end_time":"2024-06-16T12:43:10.686190","exception":false,"start_time":"2024-06-16T12:43:10.664875","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Set Precision\nLet's set the precision for tensor cores.","metadata":{}},{"cell_type":"code","source":"torch.set_float32_matmul_precision(\"high\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Download DSSP\nWe should download tools required for secondary structure generation.","metadata":{}},{"cell_type":"code","source":"!yes | apt install wget dssp -y","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Generate Secondary Structure\nLet's use BioPython to generate the secondary structure using DSSP.","metadata":{}},{"cell_type":"code","source":"def dssp_to_tensor(pdb_file, structure, dssp_exec='mkdssp'):\n    parser = PDBParser()\n    structure = parser.get_structure(structure, pdb_file)\n    model = structure[0]\n    dssp = DSSP(model, pdb_file, dssp='mkdssp')\n    aa_mapping = {\n        'A': 1, 'R': 2, 'N': 3, 'D': 4, 'C': 5, 'Q': 6, 'E': 7, 'G': 8,\n        'H': 9, 'I': 10, 'L': 11, 'K': 12, 'M': 13, 'F': 14, 'P': 15,\n        'S': 16, 'T': 17, 'W': 18, 'Y': 19, 'V': 20, 'B': 21, 'Z': 22,\n        'J': 23, 'O': 24, 'U': 25, 'X': 26, '-': 0 \n    }\n    ss_mapping = {'H': 1, 'B': 2, 'E': 3, 'G': 4, 'I': 5, 'T': 6, 'S': 7, 'P': 8, '-': 0}\n    \n    dssp_data = []\n    for key in dssp.keys():\n        value = dssp[key]\n        index = value[0]\n        aa = aa_mapping[value[1].upper()]\n        ss = ss_mapping[value[2].upper()]\n        asa = value[3]\n        phi = value[4]\n        psi = value[5]\n        nh_o1_relidx = value[6]\n        nh_o1_energy = value[7]\n        o_nh1_relidx = value[8]\n        o_nh1_energy = value[9]\n        nh_o2_relidx = value[10]\n        nh_o2_energy = value[11]\n        o_nh2_relidx = value[12]\n        o_nh2_energy = value[13]\n        \n        ss_features = [\n            aa, \n            ss, \n            asa, \n            phi, \n            psi, \n            nh_o1_relidx,\n            nh_o1_relidx,\n            o_nh1_relidx,\n            o_nh1_energy,\n            nh_o2_relidx,\n            nh_o2_energy,\n            o_nh2_relidx,\n            o_nh2_energy\n        ]\n        \n        dssp_data.append(ss_features)\n        \n    # Convert dssp_data to a PyTorch tensor\n    dssp_tensor = torch.tensor(dssp_data, dtype=torch.float32)\n\n    return dssp_tensor\n\n\nseh_tensor = dssp_to_tensor(SEH, \"3I28\")\nbrd4_tensor = dssp_to_tensor(BRD4, \"7USK\")\nhsa_tensor = dssp_to_tensor(HSA, \"1AO6\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Split Dataset\nLet's create the tensors for our datasets.","metadata":{}},{"cell_type":"code","source":"features = [f'enc{i}' for i in range(142)]\ntargets = ['bind1', 'bind2', 'bind3']\n\ntrain_index, val_index = train_test_split(\n    range(len(train)), test_size=0.05, random_state=27\n)\n\n# Convert DataFrames to Tensors\nX_train = torch.tensor(train.loc[train_index, features].values, dtype=torch.int)\ny_train = torch.tensor(train.loc[train_index, targets].values, dtype=torch.float16)\nX_val = torch.tensor(train.loc[val_index, features].values, dtype=torch.int)\ny_val = torch.tensor(train.loc[val_index, targets].values, dtype=torch.float16)\n\n# --- Nonshare Data Splitting ---\nX_nonshare = torch.tensor(test[features].values, dtype=torch.int)\nnonshare_train_index, nonshare_val_index = train_test_split(\n    range(len(X_nonshare)), test_size=len(val_index) / len(train), random_state=27\n)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Define Dataset\nWe need a dataset to use for the dataloaders that combines SMILES and DSSP data.","metadata":{}},{"cell_type":"code","source":"class CombinedDataset(Dataset):\n    def __init__(self, smiles_features, binding_target):\n        super().__init__()\n        self.smiles_features = smiles_features\n        self.binding_target = binding_target\n\n    def __len__(self):\n        return len(self.smiles_features)\n\n    def __getitem__(self, index):\n        smiles = self.smiles_features[index]\n        \n        features = {\n            'smiles': smiles,\n            'seh': seh_tensor,\n            'brd4': brd4_tensor,\n            'hsa': hsa_tensor\n        }\n\n        if self.binding_target is not None:\n            binding = self.binding_target[index]\n            targets = {\n                'binding': binding\n            }\n            return features, targets\n        else:\n            return features, None\n\n\nclass RotatingDataset(Dataset):\n    def __init__(self, share_dataset, non_share_dataset):\n        super().__init__()\n        self.share_dataset = share_dataset\n        self.non_share_dataset = non_share_dataset\n        self.share_len = len(self.share_dataset)\n\n    def __len__(self):\n        return len(self.share_dataset) + len(self.non_share_dataset)\n\n    def __getitem__(self, index):\n        if index < self.share_len:\n            return self.share_dataset[index]\n        else:\n            return self.non_share_dataset[index - self.share_len]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Collation Function\nWe need to define a collation utility function for PyTorch.","metadata":{}},{"cell_type":"code","source":"def collate_combined(batch):\n    smiles_list = []\n    seh_list = []\n    brd4_list = []\n    hsa_list = []\n\n    for sample in batch:\n        smiles_list.append(sample[0]['smiles'])\n        seh_list.append(sample[0]['seh'])\n        brd4_list.append(sample[0]['brd4'])\n        hsa_list.append(sample[0]['hsa'])\n\n    smiles_list[0] = nn.ConstantPad1d((0, MAX_LENGTH - smiles_list[0].shape[0]), 0)(smiles_list[0])\n    smiles_tensors = torch.nn.utils.rnn.pad_sequence(smiles_list, batch_first=True)\n    seh_tensors = torch.stack(seh_list, dim=0)\n    brd4_tensors = torch.stack(brd4_list, dim=0)\n    hsa_tensors = torch.stack(hsa_list, dim=0)\n\n    features = {\n        'smiles': smiles_tensors,\n        'seh': seh_tensors,\n        'brd4': brd4_tensors,\n        'hsa': hsa_tensors\n    }\n\n    binding_list = []\n    share_indices = []\n    for index, sample in enumerate(batch):\n        if sample[1]:\n            binding_list.append(sample[1]['binding'])\n            share_indices.append(index)\n\n    if binding_list:\n        binding_tensors = torch.stack(binding_list, dim=0)\n        share_indices_tensor = torch.tensor(share_indices)\n        targets = {\n            'binding': binding_tensors,\n            'share_indices': share_indices_tensor\n        }\n        return features, targets\n    else:\n        return features, None","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Domain Classifier\nLet's define a simple yet powerful domain classifier.","metadata":{}},{"cell_type":"code","source":"class DomainClassifier(nn.Module):\n    def __init__(self, filter_count, dropout):\n        super().__init__()\n        self.filter_count = filter_count\n        self.dropout = dropout\n        \n        self.classifier = nn.Sequential(\n            nn.Linear(130, 256),\n            nn.ReLU(),\n            nn.Dropout(self.dropout),\n            nn.Linear(256, 256),\n            nn.ReLU(),\n            nn.Dropout(self.dropout),\n            nn.Linear(256, 1),\n        )\n        self.sigmoid = nn.Sigmoid()\n\n    def forward(self, features):\n        predictions = self.sigmoid(self.classifier(features).mean(dim=1))\n        return predictions","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Gradient Reversal\nLet's implement gradient reversal.","metadata":{}},{"cell_type":"code","source":"class GradientReversal(torch.autograd.Function):\n    @staticmethod\n    def forward(ctx, x, lambda_):\n        ctx.lambda_ = lambda_\n        return x.view_as(x)\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        grad_input = grad_output.neg() * ctx.lambda_\n        return grad_input, None","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Model Definition\nNow we define the layers and rules for TauDock.","metadata":{"papermill":{"duration":0.019689,"end_time":"2024-06-16T12:43:24.555218","exception":false,"start_time":"2024-06-16T12:43:24.535529","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class TauDock(L.LightningModule):\n    def __init__(\n        self,\n        input_size=142,\n        embedding_size=37, \n        hidden_size=128,\n        filter_count=64, \n        prediction_count=3,\n        dropout=0.27,\n        lr=1e-3,\n        weight_decay=1e-6,\n        gradient_lambda=0.7,\n    ):\n        super(TauDock, self).__init__()\n        self.save_hyperparameters()\n\n        self.current_lambda = gradient_lambda\n\n        # Metrics\n        self.binding_criterion = nn.BCEWithLogitsLoss()\n        self.domain_criterion = nn.BCELoss()\n        self.train_precision = AveragePrecision(task='multilabel', num_labels=3, average='macro')\n        self.val_precision = AveragePrecision(task='multilabel', num_labels=3, average='macro')\n\n        # --- SMILES Embedding and Encoding ---\n        self.smiles_embedder = nn.Embedding(\n            num_embeddings=self.hparams.embedding_size,\n            embedding_dim=self.hparams.hidden_size,\n            padding_idx=0\n        )\n\n        # --- SMILES Convolvers ---\n        self.smiles_convolver = nn.Sequential(\n            nn.Conv1d(self.hparams.hidden_size, self.hparams.filter_count, kernel_size=5, stride=1, padding=0),\n            nn.BatchNorm1d(self.hparams.filter_count),\n            nn.ReLU(),\n            nn.Dropout(self.hparams.dropout),\n            nn.Conv1d(self.hparams.filter_count, self.hparams.filter_count * 2, kernel_size=5, stride=1, padding=0),\n            nn.BatchNorm1d(self.hparams.filter_count * 2),\n            nn.ReLU(),\n            nn.Dropout(self.hparams.dropout),\n            nn.Conv1d(self.hparams.filter_count * 2, self.hparams.filter_count * 3, kernel_size=5, stride=1, padding=0),\n            nn.BatchNorm1d(self.hparams.filter_count * 3),\n            nn.ReLU(),\n            nn.Dropout(self.hparams.dropout),\n        )\n\n        # --- Protein Convolvers ---\n        self.protein_convolver = nn.ModuleDict({\n            'seh': nn.Sequential(\n                nn.Conv1d(13, self.hparams.filter_count, kernel_size=5, stride=1, padding=2),\n                nn.BatchNorm1d(self.hparams.filter_count),\n                nn.ReLU(),\n                nn.Dropout(self.hparams.dropout),\n                nn.Conv1d(self.hparams.filter_count, self.hparams.filter_count * 2, kernel_size=3, stride=1, padding=1),\n                nn.BatchNorm1d(self.hparams.filter_count * 2),\n                nn.ReLU(),\n                nn.Dropout(self.hparams.dropout),\n                nn.Conv1d(self.hparams.filter_count * 2, self.hparams.filter_count * 3, kernel_size=3, stride=1, padding=1),\n                nn.BatchNorm1d(self.hparams.filter_count * 3),\n                nn.ReLU(),\n                nn.Dropout(self.hparams.dropout),\n            ),\n            'brd4': nn.Sequential(\n                nn.Conv1d(13, self.hparams.filter_count, kernel_size=5, stride=1, padding=2),\n                nn.BatchNorm1d(self.hparams.filter_count),\n                nn.ReLU(),\n                nn.Dropout(self.hparams.dropout),\n                nn.Conv1d(self.hparams.filter_count, self.hparams.filter_count * 2, kernel_size=3, stride=1, padding=1),\n                nn.BatchNorm1d(self.hparams.filter_count * 2),\n                nn.ReLU(),\n                nn.Dropout(self.hparams.dropout),\n                nn.Conv1d(self.hparams.filter_count * 2, self.hparams.filter_count * 3, kernel_size=3, stride=1, padding=1),\n                nn.BatchNorm1d(self.hparams.filter_count * 3),\n                nn.ReLU(),\n                nn.Dropout(self.hparams.dropout),\n            ),\n            'hsa': nn.Sequential(\n                nn.Conv1d(13, self.hparams.filter_count, kernel_size=5, stride=1, padding=2),\n                nn.BatchNorm1d(self.hparams.filter_count),\n                nn.ReLU(),\n                nn.Dropout(self.hparams.dropout),\n                nn.Conv1d(self.hparams.filter_count, self.hparams.filter_count * 2, kernel_size=3, stride=1, padding=1),\n                nn.BatchNorm1d(self.hparams.filter_count * 2),\n                nn.ReLU(),\n                nn.Dropout(self.hparams.dropout),\n                nn.Conv1d(self.hparams.filter_count * 2, self.hparams.filter_count * 3, kernel_size=3, stride=1, padding=1),\n                nn.BatchNorm1d(self.hparams.filter_count * 3),\n                nn.ReLU(),\n                nn.Dropout(self.hparams.dropout),\n            ), \n        })\n        \n        # --- Interaction Layers --- \n        self.interaction_layers = nn.ModuleDict({\n            protein_name: nn.Sequential(\n                nn.Conv1d(self.hparams.filter_count * 3, self.hparams.filter_count * 3, kernel_size=3),\n                nn.BatchNorm1d(self.hparams.filter_count * 3),\n                nn.ReLU(),\n                nn.Dropout(self.hparams.dropout),\n                nn.Conv1d(self.hparams.filter_count * 3, self.hparams.filter_count * 2, kernel_size=3),\n                nn.BatchNorm1d(self.hparams.filter_count * 2),\n                nn.ReLU(),\n                nn.Dropout(self.hparams.dropout),\n                nn.AdaptiveAvgPool1d(1),\n                nn.Flatten(),\n                nn.Linear(self.hparams.filter_count * 2, 256)\n            )\n            for protein_name in ['seh', 'brd4', 'hsa']\n        })\n\n\n        # --- Binding Prediction Layer ---\n        self.binding_predictor = nn.Sequential(\n            nn.Linear(256 * 3, 512),\n            nn.ReLU(),\n            nn.Dropout(self.hparams.dropout),\n            nn.Linear(512, self.hparams.prediction_count)\n        )\n\n        # --- Domain Classifier ---\n        self.domain_classifier = DomainClassifier(\n            self.hparams.filter_count,\n            self.hparams.dropout\n        )\n\n    def forward(\n        self,\n        smiles, # [BS, INPUT_SIZE]\n        seh,    # [BS, 545, 13]\n        brd4,   # [BS, 108, 13]\n        hsa     # [BS, 1156, 13]\n    ):\n        # --- SMILES Embeddings and Encoding ---\n        smiles_embeddings = self.smiles_embedder(smiles) # [BS, INPUT_SIZE, HS]\n        smiles_embeddings = smiles_embeddings.permute(0, 2, 1) # [BS, HS, INPUT_SIZE]\n\n        # --- Convolve SMILES ---\n        smiles_convolved = self.smiles_convolver(smiles_embeddings) # [BS, FILTER_COUNT * 3, INPUT_SIZE]\n\n        # --- Convolve Proteins ---\n        seh_convolved = self.protein_convolver['seh'](seh.permute(0, 2, 1)) # [BS, FILTER_COUNT * 3, 545]\n        brd4_convolved = self.protein_convolver['brd4'](brd4.permute(0, 2, 1)) # [BS, FILTER_COUNT * 3, 108]\n        hsa_convolved = self.protein_convolver['hsa'](hsa.permute(0, 2, 1)) # [BS, FILTER_COUNT * 3, 1156]\n\n        # --- Interaction Layers ---\n        seh_interaction = self.interaction_layers['seh'](torch.cat((smiles_convolved, seh_convolved), dim=2)) # [BS, 256]\n        brd4_interaction = self.interaction_layers['brd4'](torch.cat((smiles_convolved, brd4_convolved), dim=2)) # [BS, 256]\n        hsa_interaction = self.interaction_layers['hsa'](torch.cat((smiles_convolved, hsa_convolved), dim=2)) # [BS, 256]\n\n        # --- Concatenate Interactions ---\n        combined_interactions = torch.cat(\n            (seh_interaction, brd4_interaction, hsa_interaction), \n            dim=1\n        )  # [BS, 256 * 3]\n\n        # --- Predict Binding ---\n        binding_predictions = self.binding_predictor(combined_interactions) # [BS, 3]\n\n        # --- Gradient Reversal ---\n        reversed_features = GradientReversal.apply(smiles_convolved, self.current_lambda)\n        domain_predictions = self.domain_classifier(reversed_features)\n        \n        return binding_predictions, domain_predictions\n\n    def training_step(self, batch, batch_index):\n        features, targets = batch\n\n        # Unpack Features\n        smiles = features['smiles']\n        seh = features['seh']\n        brd4 = features['brd4']\n        hsa = features['hsa']\n\n        # Unpack Targets\n        binding = targets['binding']\n        share_indices = targets['share_indices']\n        \n        binding_logits, domain_predictions = self(\n            smiles,\n            seh,\n            brd4,\n            hsa\n        )\n        \n        binding_loss = self.binding_criterion(binding_logits[share_indices], binding)\n        self.log(\n            'train_binding_loss', \n            binding_loss, \n            on_step=True, \n            on_epoch=True, \n            sync_dist=True,\n            batch_size=binding_logits.size(0)\n        )\n\n        domain_labels = torch.zeros(binding_logits.size(0), dtype=torch.float).to(binding_logits.device)\n        domain_labels[share_indices] = 1\n        domain_loss = self.domain_criterion(domain_predictions, domain_labels.unsqueeze(dim=1))\n        self.log(\n            'train_domain_loss', \n            domain_loss, \n            on_step=True, \n            on_epoch=True, \n            sync_dist=True,\n            batch_size=binding_logits.size(0)\n        )\n        \n        self.train_precision(binding_logits[share_indices], binding.long())\n        self.log(\n            'train_precision', \n            self.train_precision, \n            on_step=True, \n            on_epoch=True, \n            sync_dist=True,\n            batch_size=binding_logits.size(0)\n        )\n\n        loss = binding_loss + domain_loss\n        return loss\n\n    def validation_step(self, batch, batch_index):\n        features, targets = batch\n\n        # Unpack Features\n        smiles = features['smiles']\n        seh = features['seh']\n        brd4 = features['brd4']\n        hsa = features['hsa']\n\n        # Unpack Targets\n        binding = targets['binding']\n        share_indices = targets['share_indices']\n        \n        binding_logits, domain_predictions = self(\n            smiles,\n            seh,\n            brd4,\n            hsa\n        )\n        \n        binding_loss = self.binding_criterion(binding_logits[share_indices], binding)\n        self.log(\n            'validation_binding_loss', \n            binding_loss, \n            on_step=True, \n            on_epoch=True, \n            sync_dist=True,\n            batch_size=binding_logits.size(0)\n        )\n\n        domain_labels = torch.zeros(binding_logits.size(0), dtype=torch.float).to(binding_logits.device)\n        domain_labels[share_indices] = 1\n        domain_loss = self.domain_criterion(domain_predictions, domain_labels.unsqueeze(dim=1))\n        self.log(\n            'validation_domain_loss', \n            domain_loss, \n            on_step=True, \n            on_epoch=True, \n            sync_dist=True,\n            batch_size=binding_logits.size(0)\n        )\n        \n        self.val_precision(binding_logits[share_indices], binding.long())\n        self.log(\n            'validation_precision', \n            self.val_precision, \n            on_step=True, \n            on_epoch=True, \n            sync_dist=True,\n            batch_size=binding_logits.size(0)\n        )\n\n        loss = binding_loss + domain_loss\n        return loss\n\n    def configure_optimizers(self):\n        optimizer = AdamW(\n            self.parameters(), \n            lr=self.hparams.lr,\n            weight_decay=self.hparams.weight_decay,\n            amsgrad=True,\n        )\n        \n        scheduler = CyclicLR(\n            optimizer, \n            base_lr=LEARNING_RATE,\n            max_lr=MAX_LEARNING_RATE,\n            cycle_momentum=False\n        )\n\n        return {\n            'optimizer': optimizer,\n            'lr_scheduler': {\n                'scheduler': scheduler,\n                \"interval\": \"step\",\n                \"frequency\": 2000,\n                'monitor': 'validation_binding_loss'\n            }\n        }\n    \n    def train_dataloader(self):\n        # Create CombinedDatasets\n        train_share_dataset = CombinedDataset(X_train, y_train)\n        train_nonshare_dataset = CombinedDataset(X_nonshare[nonshare_train_index], None)\n        \n        # Create RotatingDatasets\n        train_dataset = RotatingDataset(train_share_dataset, train_nonshare_dataset)\n        \n        # 📦 Training DataLoader\n        train_dataloader = DataLoader(\n            train_dataset,\n            batch_size=BATCH_SIZE,\n            shuffle=True,\n            num_workers=WORKERS,\n            prefetch_factor=PREFETCH_FACTOR,\n            pin_memory=True,\n            collate_fn=collate_combined,\n            drop_last=True\n        )\n        \n        return train_dataloader\n    \n    def val_dataloader(self):\n        # Create CombinedDatasets\n        val_share_dataset = CombinedDataset(X_val, y_val)\n        val_nonshare_dataset = CombinedDataset(X_nonshare[nonshare_val_index], None)\n        \n        # Create RotatingDatasets\n        val_dataset = RotatingDataset(val_share_dataset, val_nonshare_dataset)\n        \n        # 📦 Validation DataLoader\n        val_dataloader = DataLoader(\n            val_dataset,\n            batch_size=BATCH_SIZE,\n            shuffle=True,\n            num_workers=WORKERS,\n            prefetch_factor=PREFETCH_FACTOR,\n            pin_memory=True,\n            collate_fn=collate_combined,\n            drop_last=True\n        )\n        \n        return val_dataloader","metadata":{"papermill":{"duration":0.059394,"end_time":"2024-06-16T12:43:24.633577","exception":false,"start_time":"2024-06-16T12:43:24.574183","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Logging\nLet's load wieghts and biases.","metadata":{"papermill":{"duration":0.019511,"end_time":"2024-06-16T12:43:27.260701","exception":false,"start_time":"2024-06-16T12:43:27.241190","status":"completed"},"tags":[]}},{"cell_type":"code","source":"user_secrets = UserSecretsClient()\ntoken = user_secrets.get_secret(\"wandb\")\nwandb.login(key=token)\n\nwandb_logger = WandbLogger(project='TauDock', log_model=\"all\")","metadata":{"papermill":{"duration":17.57849,"end_time":"2024-06-16T12:43:44.858797","exception":false,"start_time":"2024-06-16T12:43:27.280307","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Training Loop\nLet's start training the model.","metadata":{"papermill":{"duration":0.02053,"end_time":"2024-06-16T12:43:45.458655","exception":false,"start_time":"2024-06-16T12:43:45.438125","status":"completed"},"tags":[]}},{"cell_type":"code","source":"model = TauDock(\n    input_size=MAX_LENGTH,\n    embedding_size=37, \n    hidden_size=HIDDEN_SIZE,\n    filter_count=64, \n    prediction_count=3,\n    dropout=DROPOUT,\n    lr=LEARNING_RATE,\n    weight_decay=WEIGHT_DECAY,\n    gradient_lambda=0.7\n)\n\nif MODEL_PATH.exists():\n    model = TauDock.load_from_checkpoint(MODEL_PATH)\n\nearly_stop_callback = EarlyStopping(\n    monitor=\"validation_binding_loss\", \n    mode=\"min\", \n    patience=5, \n    verbose=True\n)\ncheckpoint_callback = ModelCheckpoint(\n    monitor=\"validation_binding_loss\", \n    dirpath=\"./models\", \n    filename=f\"taudock-model\", \n    save_top_k=5,\n    mode=\"min\"\n)\nlr_monitor = LearningRateMonitor(logging_interval='epoch')\n\ntrainer = L.Trainer(\n    max_epochs=EPOCHS,\n    callbacks=[early_stop_callback, checkpoint_callback, lr_monitor],\n    devices=1,\n    accelerator=\"gpu\",\n    enable_progress_bar=True,\n    logger=wandb_logger,\n)\n\ntrainer.fit(model)","metadata":{"papermill":{"duration":7458.656966,"end_time":"2024-06-16T14:48:04.136250","exception":false,"start_time":"2024-06-16T12:43:45.479284","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Clear Cache\nWe need to reclaim some memory before shutting down training.","metadata":{"papermill":{"duration":0.025036,"end_time":"2024-06-16T14:48:04.186265","exception":false,"start_time":"2024-06-16T14:48:04.161229","status":"completed"},"tags":[]}},{"cell_type":"code","source":"_ = gc.collect()\ntorch.cuda.empty_cache()\n\nwandb.finish()","metadata":{"papermill":{"duration":5.234986,"end_time":"2024-06-16T14:48:09.445328","exception":false,"start_time":"2024-06-16T14:48:04.210342","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]}]}