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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jie8WKDQi0yq",
"outputId": "75e42623-a543-4745-b6d6-ff9a78880de9"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting torch-geometric\n",
" Downloading torch_geometric-2.6.1-py3-none-any.whl.metadata (63 kB)\n",
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/63.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.1/63.1 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: aiohttp in /usr/local/lib/python3.11/dist-packages (from torch-geometric) (3.12.14)\n",
"Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch-geometric) (2025.3.0)\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->torch-geometric) (2.5.0)\n",
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"Requirement already satisfied: typing-extensions>=4.2 in /usr/local/lib/python3.11/dist-packages (from aiosignal>=1.4.0->aiohttp->torch-geometric) (4.14.1)\n",
"Downloading torch_geometric-2.6.1-py3-none-any.whl (1.1 MB)\n",
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.1 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.5/1.1 MB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: torch-geometric\n",
"Successfully installed torch-geometric-2.6.1\n",
"Collecting rdkit\n",
" Downloading rdkit-2025.3.3-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (4.0 kB)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from rdkit) (2.0.2)\n",
"Requirement already satisfied: Pillow in /usr/local/lib/python3.11/dist-packages (from rdkit) (11.3.0)\n",
"Downloading rdkit-2025.3.3-cp311-cp311-manylinux_2_28_x86_64.whl (34.9 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m34.9/34.9 MB\u001b[0m \u001b[31m74.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: rdkit\n",
"Successfully installed rdkit-2025.3.3\n"
]
}
],
"source": [
"!pip install torch-geometric\n",
"!pip install rdkit"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wI8TNU1g12RQ"
},
"source": [
"with 3d molecular bond prediction, time embedding, and attention layers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "YtMdfjZm144m",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f5da6dc0-4b54-436b-d39c-d54fead8c10c"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 0: Loss = 397.1285, Noise Loss = 113.3167, Bond Loss = 283.8118\n",
"Epoch 1: Loss = 266.2317, Noise Loss = 68.7714, Bond Loss = 197.4603\n",
"Epoch 2: Loss = 221.7601, Noise Loss = 56.5115, Bond Loss = 165.2486\n",
"Epoch 3: Loss = 190.6335, Noise Loss = 47.5783, Bond Loss = 143.0552\n",
"Epoch 4: Loss = 163.8749, Noise Loss = 39.0206, Bond Loss = 124.8543\n",
"Epoch 5: Loss = 149.0785, Noise Loss = 31.0315, Bond Loss = 118.0470\n",
"Epoch 6: Loss = 147.7367, Noise Loss = 33.4656, Bond Loss = 114.2711\n",
"Epoch 7: Loss = 141.2191, Noise Loss = 29.1597, Bond Loss = 112.0594\n",
"Epoch 8: Loss = 130.4628, Noise Loss = 22.0213, Bond Loss = 108.4415\n",
"Epoch 9: Loss = 126.8406, Noise Loss = 22.6980, Bond Loss = 104.1426\n",
"Epoch 10: Loss = 124.9823, Noise Loss = 23.6870, Bond Loss = 101.2953\n"
]
}
],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import numpy as np\n",
"import random\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from torch_geometric.data import Data\n",
"from torch_geometric.nn import MessagePassing\n",
"from torch_geometric.utils import add_self_loops\n",
"from rdkit import Chem\n",
"from rdkit.Chem import AllChem, Descriptors\n",
"import math\n",
"\n",
"# -------- UTILS: Molecule Processing with 3D Coordinates --------\n",
"def smiles_to_graph(smiles):\n",
" mol = Chem.MolFromSmiles(smiles)\n",
" if mol is None:\n",
" return None\n",
" mol = Chem.AddHs(mol)\n",
" try:\n",
" AllChem.EmbedMolecule(mol, AllChem.ETKDG())\n",
" AllChem.UFFOptimizeMolecule(mol)\n",
" except:\n",
" return None\n",
"\n",
" conf = mol.GetConformer()\n",
" atoms = mol.GetAtoms()\n",
" bonds = mol.GetBonds()\n",
"\n",
" node_feats = []\n",
" pos = []\n",
" edge_index = []\n",
" edge_attrs = []\n",
"\n",
" for atom in atoms:\n",
" # Normalize atomic number\n",
" node_feats.append([atom.GetAtomicNum() / 100.0])\n",
" position = conf.GetAtomPosition(atom.GetIdx())\n",
" pos.append([position.x, position.y, position.z])\n",
"\n",
" for bond in bonds:\n",
" start = bond.GetBeginAtomIdx()\n",
" end = bond.GetEndAtomIdx()\n",
" edge_index.append([start, end])\n",
" edge_index.append([end, start])\n",
" bond_type = bond.GetBondType()\n",
" bond_class = {\n",
" Chem.BondType.SINGLE: 0,\n",
" Chem.BondType.DOUBLE: 1,\n",
" Chem.BondType.TRIPLE: 2,\n",
" Chem.BondType.AROMATIC: 3\n",
" }.get(bond_type, 0)\n",
" edge_attrs.extend([[bond_class], [bond_class]])\n",
"\n",
" return Data(\n",
" x=torch.tensor(node_feats, dtype=torch.float),\n",
" pos=torch.tensor(pos, dtype=torch.float),\n",
" edge_index=torch.tensor(edge_index, dtype=torch.long).t().contiguous(),\n",
" edge_attr=torch.tensor(edge_attrs, dtype=torch.long)\n",
" )\n",
"\n",
"# -------- EGNN Layer --------\n",
"class EGNNLayer(MessagePassing):\n",
" def __init__(self, node_dim):\n",
" super().__init__(aggr='add')\n",
" self.node_mlp = nn.Sequential(\n",
" nn.Linear(node_dim * 2 + 1, 128),\n",
" nn.ReLU(),\n",
" nn.Linear(128, node_dim)\n",
" )\n",
" self.coord_mlp = nn.Sequential(\n",
" nn.Linear(1, 128),\n",
" nn.ReLU(),\n",
" nn.Linear(128, 1)\n",
" )\n",
"\n",
" def forward(self, x, pos, edge_index):\n",
" edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))\n",
" self.coord_updates = torch.zeros_like(pos)\n",
" x_out, coord_out = self.propagate(edge_index, x=x, pos=pos)\n",
" return x_out, pos + coord_out\n",
"\n",
" def message(self, x_i, x_j, pos_i, pos_j):\n",
" edge_vec = pos_j - pos_i\n",
" dist = ((edge_vec**2).sum(dim=-1, keepdim=True) + 1e-8).sqrt()\n",
" h = torch.cat([x_i, x_j, dist], dim=-1)\n",
" edge_msg = self.node_mlp(h)\n",
" coord_update = self.coord_mlp(dist) * edge_vec\n",
" return edge_msg, coord_update\n",
"\n",
" def message_and_aggregate(self, adj_t, x):\n",
" raise NotImplementedError(\"This EGNN layer does not support sparse adjacency matrices.\")\n",
"\n",
" def aggregate(self, inputs, index):\n",
" edge_msg, coord_update = inputs\n",
" aggr_msg = torch.zeros(index.max() + 1, edge_msg.size(-1), device=edge_msg.device).index_add_(0, index, edge_msg)\n",
" aggr_coord = torch.zeros(index.max() + 1, coord_update.size(-1), device=coord_update.device).index_add_(0, index, coord_update)\n",
" return aggr_msg, aggr_coord\n",
"\n",
" def update(self, aggr_out, x):\n",
" msg, coord_update = aggr_out\n",
" return x + msg, coord_update\n",
"\n",
"# -------- Time Embedding --------\n",
"class TimeEmbedding(nn.Module):\n",
" def __init__(self, embed_dim):\n",
" super().__init__()\n",
" self.net = nn.Sequential(\n",
" nn.Linear(1, 32),\n",
" nn.ReLU(),\n",
" nn.Linear(32, embed_dim)\n",
" )\n",
"\n",
" def forward(self, t):\n",
" return self.net(t.view(-1, 1).float() / 1000)\n",
"\n",
"# -------- Olfactory Conditioning --------\n",
"class OlfactoryConditioner(nn.Module):\n",
" def __init__(self, num_labels, embed_dim):\n",
" super().__init__()\n",
" self.embedding = nn.Linear(num_labels, embed_dim)\n",
"\n",
" def forward(self, labels):\n",
" return self.embedding(labels.float())\n",
"\n",
"# -------- EGNN Diffusion Model --------\n",
"class EGNNDiffusionModel(nn.Module):\n",
" def __init__(self, node_dim, embed_dim):\n",
" super().__init__()\n",
" self.time_embed = TimeEmbedding(embed_dim)\n",
" self.egnn1 = EGNNLayer(node_dim + embed_dim * 2)\n",
" self.egnn2 = EGNNLayer(node_dim + embed_dim * 2)\n",
" self.bond_predictor = nn.Sequential(\n",
" nn.Linear((node_dim + embed_dim * 2) * 2, 64),\n",
" nn.ReLU(),\n",
" nn.Linear(64, 4)\n",
" )\n",
"\n",
" def forward(self, x_t, pos, edge_index, t, cond_embed):\n",
" batch_size = x_t.size(0)\n",
" t_embed = self.time_embed(t).expand(batch_size, -1)\n",
" cond_embed = cond_embed.expand(batch_size, -1)\n",
" x_input = torch.cat([x_t, cond_embed, t_embed], dim=1)\n",
" x1, pos1 = self.egnn1(x_input, pos, edge_index)\n",
" x2, pos2 = self.egnn2(x1, pos1, edge_index)\n",
" edge_feats = torch.cat([x2[edge_index[0]], x2[edge_index[1]]], dim=1)\n",
" bond_logits = self.bond_predictor(edge_feats)\n",
" return x2[:, :x_t.shape[1]], bond_logits\n",
"\n",
"# -------- Noise and Training --------\n",
"def add_noise(x_0, noise, t):\n",
" return x_0 + noise * (t / 1000.0)\n",
"\n",
"\n",
"def plot_data(mu, sigma, color, title):\n",
" all_losses = np.array(mu)\n",
" sigma_losses = np.array(sigma)\n",
" x = np.arange(len(mu))\n",
" plt.plot(x, all_losses, f'{color}-')\n",
" plt.fill_between(x, all_losses - sigma_losses, all_losses + sigma_losses, color=color, alpha=0.2)\n",
" plt.legend(['Mean Loss', 'Variance of Loss'])\n",
" plt.xlabel('Epoch')\n",
" plt.ylabel('Loss')\n",
" plt.title(title)\n",
" plt.show()\n",
"\n",
"\n",
"def train(model, conditioner, dataset, epochs=10):\n",
" model.train()\n",
" conditioner.train()\n",
" optimizer = torch.optim.Adam(list(model.parameters()) + list(conditioner.parameters()), lr=1e-4)\n",
" ce_loss = nn.CrossEntropyLoss()\n",
" torch.autograd.set_detect_anomaly(True)\n",
" all_bond_losses: list = []\n",
" all_noise_losses: list = []\n",
" all_losses: list = []\n",
" all_sigma_bond_losses: list = []\n",
" all_sigma_noise_losses: list = []\n",
" all_sigma_losses: list = []\n",
"\n",
" for epoch in range(epochs):\n",
" total_bond_loss = 0\n",
" total_noise_loss = 0\n",
" total_loss = 0\n",
" sigma_bond_losses: list = []\n",
" sigma_noise_losses: list = []\n",
" sigma_losses: list = []\n",
"\n",
" for data in dataset:\n",
" x_0, pos, edge_index, edge_attr, labels = data.x, data.pos, data.edge_index, data.edge_attr.view(-1), data.y\n",
" if torch.any(edge_attr >= 4) or torch.any(edge_attr < 0) or torch.any(torch.isnan(x_0)):\n",
" continue # skip corrupted data\n",
" t = torch.tensor([random.randint(1, 1000)])\n",
" noise = torch.randn_like(x_0)\n",
" x_t = add_noise(x_0, noise, t)\n",
" cond_embed = conditioner(labels)\n",
" pred_noise, bond_logits = model(x_t, pos, edge_index, t, cond_embed)\n",
" loss_noise = F.mse_loss(pred_noise, noise)\n",
" loss_bond = ce_loss(bond_logits, edge_attr)\n",
" loss = loss_noise + loss_bond\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
" optimizer.step()\n",
" total_bond_loss += loss_bond.item()\n",
" total_noise_loss += loss_noise.item()\n",
" total_loss += loss.item()\n",
" sigma_bond_losses.append(loss_bond.item())\n",
" sigma_noise_losses.append(loss_noise.item())\n",
" sigma_losses.append(loss.item())\n",
"\n",
" all_bond_losses.append(total_bond_loss)\n",
" all_noise_losses.append(total_noise_loss)\n",
" all_losses.append(total_loss)\n",
" all_sigma_bond_losses.append(torch.std(torch.tensor(sigma_bond_losses)))\n",
" all_sigma_noise_losses.append(torch.std(torch.tensor(sigma_noise_losses)))\n",
" all_sigma_losses.append(torch.std(torch.tensor(sigma_losses)))\n",
" print(f\"Epoch {epoch}: Loss = {total_loss:.4f}, Noise Loss = {total_noise_loss:.4f}, Bond Loss = {total_bond_loss:.4f}\")\n",
"\n",
" plot_data(mu=all_bond_losses, sigma=all_sigma_bond_losses, color='b', title=\"Bond Loss\")\n",
" plot_data(mu=all_noise_losses, sigma=all_sigma_noise_losses, color='r', title=\"Noise Loss\")\n",
" plot_data(mu=all_losses, sigma=all_sigma_losses, color='g', title=\"Total Loss\")\n",
"\n",
" plt.plot(all_bond_losses)\n",
" plt.plot(all_noise_losses)\n",
" plt.plot(all_losses)\n",
" plt.legend(['Bond Loss', 'Noise Loss', 'Total Loss'])\n",
" plt.xlabel('Epoch')\n",
" plt.ylabel('Loss')\n",
" plt.title('Training Loss Over Epochs')\n",
" plt.show()\n",
" return model, conditioner\n",
"\n",
"\n",
"# -------- Generation --------\n",
"def temperature_scaled_softmax(logits, temperature=1.0):\n",
" logits = logits / temperature\n",
" return torch.softmax(logits, dim=0)\n",
"\n",
"\n",
"from rdkit.Chem import Draw\n",
"from rdkit import RDLogger\n",
"RDLogger.DisableLog('rdApp.*') # Suppress RDKit warnings\n",
"\n",
"def sample_batch(model, conditioner, label_vec, steps=1000, batch_size=4):\n",
" mols = []\n",
" for _ in range(batch_size):\n",
" x_t = torch.randn((10, 1))\n",
" pos = torch.randn((10, 3))\n",
" edge_index = torch.randint(0, 10, (2, 20))\n",
"\n",
" for t in reversed(range(1, steps + 1)):\n",
" cond_embed = conditioner(label_vec.unsqueeze(0))\n",
" pred_x, bond_logits = model(x_t, pos, edge_index, torch.tensor([t]), cond_embed)\n",
" x_t = x_t - pred_x * (1.0 / steps)\n",
"\n",
" x_t = x_t * 100.0\n",
" x_t.relu_()\n",
" atom_types = torch.clamp(x_t.round(), 1, 118).int().squeeze().tolist()\n",
" allowed_atoms = [6, 7, 8, 9, 15, 16, 17] # C, N, O, F, P, S, Cl\n",
" bond_logits.relu_()\n",
"\n",
" mol = Chem.RWMol()\n",
" idx_map = {}\n",
" for i, atomic_num in enumerate(atom_types):\n",
" if atomic_num not in allowed_atoms:\n",
" continue\n",
" try:\n",
" atom = Chem.Atom(int(atomic_num))\n",
" idx_map[i] = mol.AddAtom(atom)\n",
" except Exception:\n",
" continue\n",
"\n",
" if len(idx_map) < 2:\n",
" continue\n",
"\n",
" bond_type_map = {\n",
" 0: Chem.BondType.SINGLE,\n",
" 1: Chem.BondType.DOUBLE,\n",
" 2: Chem.BondType.TRIPLE,\n",
" 3: Chem.BondType.AROMATIC\n",
" }\n",
"\n",
" added = set()\n",
" for i in range(edge_index.shape[1]):\n",
" a = int(edge_index[0, i])\n",
" b = int(edge_index[1, i])\n",
" if a != b and (a, b) not in added and (b, a) not in added and a in idx_map and b in idx_map:\n",
" try:\n",
" bond_type = bond_type_map.get(bond_preds[i], Chem.BondType.SINGLE)\n",
" mol.AddBond(idx_map[a], idx_map[b], bond_type)\n",
" added.add((a, b))\n",
" except Exception:\n",
" continue\n",
"\n",
" try:\n",
" mol = mol.GetMol()\n",
" Chem.SanitizeMol(mol)\n",
" mols.append(mol)\n",
" except Exception:\n",
" continue\n",
" return mols\n",
"\n",
"\n",
"def sample(model, conditioner, label_vec, is_constrained=True, steps=1000, debug=True):\n",
" x_t = torch.randn((10, 1))\n",
" pos = torch.randn((10, 3))\n",
" edge_index = torch.randint(0, 10, (2, 20))\n",
"\n",
" for t in reversed(range(1, steps + 1)):\n",
" cond_embed = conditioner(label_vec.unsqueeze(0))\n",
" pred_x, bond_logits = model(x_t, pos, edge_index, torch.tensor([t]), cond_embed)\n",
" bond_logits = temperature_scaled_softmax(bond_logits, temperature=(1/t))\n",
" x_t = x_t - pred_x * (1.0 / steps)\n",
"\n",
" x_t = x_t * 100.0\n",
" x_t.relu_()\n",
" atom_types = torch.clamp(x_t.round(), 1, 118).int().squeeze().tolist()\n",
" ## Try limiting to only the molecules that the Scentience sensors can detect\n",
" allowed_atoms = [6, 7, 8, 9, 15, 16, 17] # C, N, O, F, P, S, Cl\n",
" bond_logits.relu_()\n",
" bond_preds = torch.argmax(bond_logits, dim=-1).tolist()\n",
" if debug:\n",
" print(f\"\\tcond_embed: {cond_embed}\")\n",
" print(f\"\\tx_t: {x_t}\")\n",
" print(f\"\\tprediction: {x_t}\")\n",
" print(f\"\\tbond logits: {bond_logits}\")\n",
" print(f\"\\tatoms: {atom_types}\")\n",
" print(f\"\\tbonds: {bond_preds}\")\n",
"\n",
" mol = Chem.RWMol()\n",
" idx_map = {}\n",
" for i, atomic_num in enumerate(atom_types):\n",
" if is_constrained and atomic_num not in allowed_atoms:\n",
" continue\n",
" try:\n",
" atom = Chem.Atom(int(atomic_num))\n",
" idx_map[i] = mol.AddAtom(atom)\n",
" except Exception:\n",
" continue\n",
"\n",
" if len(idx_map) < 2:\n",
" print(\"Molecule too small or no valid atoms after filtering.\")\n",
" return \"\"\n",
"\n",
" bond_type_map = {\n",
" 0: Chem.BondType.SINGLE,\n",
" 1: Chem.BondType.DOUBLE,\n",
" 2: Chem.BondType.TRIPLE,\n",
" 3: Chem.BondType.AROMATIC\n",
" }\n",
"\n",
" added = set()\n",
" for i in range(edge_index.shape[1]):\n",
" a = int(edge_index[0, i])\n",
" b = int(edge_index[1, i])\n",
" if a != b and (a, b) not in added and (b, a) not in added and a in idx_map and b in idx_map:\n",
" try:\n",
" bond_type = bond_type_map.get(bond_preds[i], Chem.BondType.SINGLE)\n",
" mol.AddBond(idx_map[a], idx_map[b], bond_type)\n",
" added.add((a, b))\n",
" except Exception:\n",
" continue\n",
" try:\n",
" mol = mol.GetMol()\n",
" Chem.SanitizeMol(mol)\n",
" smiles = Chem.MolToSmiles(mol)\n",
" img = Draw.MolToImage(mol)\n",
" img.show()\n",
" print(f\"Atom types: {atom_types}\")\n",
" print(f\"Generated SMILES: {smiles}\")\n",
" return smiles\n",
" except Exception as e:\n",
" print(f\"Sanitization error: {e}\")\n",
" return \"\"\n",
"\n",
"\n",
"# -------- Validation --------\n",
"def validate_molecule(smiles):\n",
" mol = Chem.MolFromSmiles(smiles)\n",
" if mol is None:\n",
" return False, {}\n",
" return True, {\"MolWt\": Descriptors.MolWt(mol), \"LogP\": Descriptors.MolLogP(mol)}\n",
"\n",
"# -------- Load Data --------\n",
"def load_goodscents_subset(filepath=\"/content/curated_GS_LF_merged_4983.csv\",\n",
" index=200,\n",
" shuffle=True\n",
" ):\n",
" df = pd.read_csv(filepath)\n",
" if shuffle:\n",
" df = df.sample(frac=1).reset_index(drop=True)\n",
" if index > 0:\n",
" df = df.head(index)\n",
" else:\n",
" df = df.tail(-1*index)\n",
" descriptor_cols = df.columns[2:]\n",
" smiles_list, label_map = [], {}\n",
" for _, row in df.iterrows():\n",
" smiles = row[\"nonStereoSMILES\"]\n",
" labels = row[descriptor_cols].astype(int).tolist()\n",
" if smiles and any(labels):\n",
" smiles_list.append(smiles)\n",
" label_map[smiles] = labels\n",
" return smiles_list, label_map, list(descriptor_cols)\n",
"\n",
"\n",
"# -------- Main --------\n",
"if __name__ == '__main__':\n",
" SHOULD_BATCH: bool = False\n",
" smiles_list, label_map, label_names = load_goodscents_subset(index=500)\n",
" num_labels = len(label_names)\n",
" dataset = []\n",
" for smi in smiles_list:\n",
" g = smiles_to_graph(smi)\n",
" if g:\n",
" g.y = torch.tensor(label_map[smi])\n",
" dataset.append(g)\n",
" model = EGNNDiffusionModel(node_dim=1, embed_dim=8)\n",
" conditioner = OlfactoryConditioner(num_labels=num_labels, embed_dim=8)\n",
" train_success: bool = False\n",
" while not train_success:\n",
" try:\n",
" model, conditioner = train(model, conditioner, dataset, epochs=100)\n",
" train_success = True\n",
" break\n",
" except IndexError:\n",
" print(\"Index Error on training. Trying again.\")\n",
" test_label_vec = torch.zeros(num_labels)\n",
" if \"floral\" in label_names:\n",
" test_label_vec[label_names.index(\"floral\")] = 0\n",
" if \"fruity\" in label_names:\n",
" test_label_vec[label_names.index(\"fruity\")] = 1\n",
" if \"musky\" in label_names:\n",
" test_label_vec[label_names.index(\"musky\")] = 0\n",
"\n",
" model.eval()\n",
" conditioner.eval()\n",
" if SHOULD_BATCH:\n",
" new_smiles_list = sample_batch(model, conditioner, label_vec=test_label_vec)\n",
" for new_smiles in new_smiles_list:\n",
" print(new_smiles)\n",
" valid, props = validate_molecule(new_smiles)\n",
" print(f\"Generated SMILES: {new_smiles}\\nValid: {valid}, Properties: {props}\")\n",
" else:\n",
" new_smiles = sample(model, conditioner, label_vec=test_label_vec)\n",
" print(new_smiles)\n",
" valid, props = validate_molecule(new_smiles)\n",
" print(f\"Generated SMILES: {new_smiles}\\nValid: {valid}, Properties: {props}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "M8zT_FJzj7j3"
},
"outputs": [],
"source": [
"torch.save(model, 'egnn.pth')\n",
"torch.save(model.state_dict(), 'egnn_state_dict.pth')\n",
"torch.save(conditioner, 'olfactory_conditioner.pth')\n",
"torch.save(conditioner.state_dict(), 'olfactory_conditioner_state_dict.pth')"
]
}
],
"metadata": {
"colab": {
"machine_shape": "hm",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |