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.gitattributes +0 -34
.gitignore +212 -0
LICENSE +21 -0
README.md +15 -3
benchmark_csv/bindingdb.csv +0 -0
benchmark_csv/boiling_point.csv +2001 -0
benchmark_csv/ic_50_subset.csv +0 -0
benchmark_csv/qm9_subset.csv +2001 -0
data/data_handling.py +152 -0
data/featurize.py +121 -0
data/loaders.py +64 -0
data/polyatomic_featurize.py +160 -0
datasets/ecfp_data_bindingdb.pt +3 -0
datasets/ecfp_data_boilingpoint.pt +3 -0
datasets/ecfp_data_esol.pt +3 -0
datasets/ecfp_data_freesolv.pt +3 -0
datasets/ecfp_data_ic50.pt +3 -0
datasets/ecfp_data_lipophil.pt +3 -0
datasets/ecfp_data_qm9.pt +3 -0
datasets/ecfp_test_data_bindingdb.pt +3 -0
datasets/ecfp_test_data_boilingpoint.pt +3 -0
datasets/ecfp_test_data_esol.pt +3 -0
datasets/ecfp_test_data_freesolv.pt +3 -0
datasets/ecfp_test_data_ic50.pt +3 -0
datasets/ecfp_test_data_lipophil.pt +3 -0
datasets/ecfp_test_data_qm9.pt +3 -0
datasets/polyatomic_data_bindingdb.pt +3 -0
datasets/polyatomic_data_boilingpoint.pt +3 -0
datasets/polyatomic_data_esol.pt +3 -0
datasets/polyatomic_data_freesolv.pt +3 -0
datasets/polyatomic_data_ic50.pt +3 -0
datasets/polyatomic_data_lipophil.pt +3 -0
datasets/polyatomic_data_qm9.pt +3 -0
datasets/polyatomic_test_data_bindingdb.pt +3 -0
datasets/polyatomic_test_data_boilingpoint.pt +3 -0
datasets/polyatomic_test_data_esol.pt +3 -0
datasets/polyatomic_test_data_freesolv.pt +3 -0
datasets/polyatomic_test_data_ic50.pt +3 -0
datasets/polyatomic_test_data_lipophil.pt +3 -0
datasets/polyatomic_test_data_qm9.pt +3 -0
datasets/selfies_data_bindingdb.pt +3 -0
datasets/selfies_data_boilingpoint.pt +3 -0
datasets/selfies_data_esol.pt +3 -0
datasets/selfies_data_freesolv.pt +3 -0
datasets/selfies_data_ic50.pt +3 -0
datasets/selfies_data_lipophil.pt +3 -0
datasets/selfies_data_qm9.pt +3 -0
datasets/selfies_test_data_bindingdb.pt +3 -0
datasets/selfies_test_data_boilingpoint.pt +3 -0
datasets/selfies_test_data_esol.pt +3 -0

.gitattributes CHANGED Viewed

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1	*.pt filter=lfs diff=lfs merge=lfs -text

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+# Byte-compiled / optimized / DLL files
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+*.py[codz]
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+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+# Installer logs
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+# Unit test / coverage reports
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+# Scrapy stuff:
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+# Sphinx documentation
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+# PyBuilder
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+# Jupyter Notebook
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+# IPython
+profile_default/
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+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+#poetry.toml
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#   pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.
+#   https://pdm-project.org/en/latest/usage/project/#working-with-version-control
+#pdm.lock
+#pdm.toml
+.pdm-python
+.pdm-build/
+# pixi
+#   Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.
+#pixi.lock
+#   Pixi creates a virtual environment in the .pixi directory, just like venv module creates one
+#   in the .venv directory. It is recommended not to include this directory in version control.
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+__pypackages__/
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+# SageMath parsed files
+*.sage.py
+# Environments
+.env
+.envrc
+.venv
+env/
+venv/
+ENV/
+env.bak/
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+# Spyder project settings
+.spyderproject
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+# mkdocs documentation
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+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+# Abstra
+# Abstra is an AI-powered process automation framework.
+# Ignore directories containing user credentials, local state, and settings.
+# Learn more at https://abstra.io/docs
+.abstra/
+# Visual Studio Code
+#  Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore
+#  that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
+#  and can be added to the global gitignore or merged into this file. However, if you prefer,
+#  you could uncomment the following to ignore the entire vscode folder
+# .vscode/
+# Ruff stuff:
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+# PyPI configuration file
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+# Cursor
+#  Cursor is an AI-powered code editor. `.cursorignore` specifies files/directories to
+#  exclude from AI features like autocomplete and code analysis. Recommended for sensitive data
+#  refer to https://docs.cursor.com/context/ignore-files
+.cursorignore
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+# Marimo
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+marimo/_lsp/
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+all_data_lipophil.pt
+all_data_lipophil_enriched.pt
+richer_lipophil_data.pt

LICENSE ADDED Viewed

	@@ -0,0 +1,21 @@

+MIT License
+Copyright (c) 2025 Rahul Khorana
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md CHANGED Viewed

@@ -1,3 +1,15 @@
----
-license: mit
----

+# PACT-Net (NeurIPS 2025)
+```python
+python main.py --model polyatomic --rep polyatomic --dataset esol
+```
+```
+@inproceedings{khorana2025pactnet,
+  title     = {Topological Feature Compression for Molecular Graph Neural Networks},
+  author    = {Rahul Khorana},
+  booktitle = {Thirty-ninth Conference on Neural Information Processing Systems (NeurIPS 2025) Workshop on AI for Science},
+  year      = {2025},
+  note      = {Poster presentation}
+}
+```

benchmark_csv/bindingdb.csv ADDED Viewed

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benchmark_csv/ic_50_subset.csv ADDED Viewed

The diff for this file is too large to render. See raw diff

benchmark_csv/qm9_subset.csv ADDED Viewed

	@@ -0,0 +1,2001 @@

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data/data_handling.py ADDED Viewed

	@@ -0,0 +1,152 @@

+import os
+import torch
+from torch_geometric.loader import DataLoader
+from pathlib import Path
+import numpy as np
+import multiprocessing
+from joblib import Parallel, delayed
+# --- Assume these are imported from your project ---
+from data.loaders import load_dataset
+from data.featurize import (
+    smiles_to_graph,
+    selfies_to_graph,
+    ecfp_to_graph,
+    smiles_for_gp,
+    selfies_for_gp,
+    ecfp_for_gp,
+)
+from data.polyatomic_featurize import compressed_topsignal_graph_from_smiles
+from models.gnn import GCN, GIN, GAT, GraphSAGE
+from models.polyatomic import PolyatomicNet
+# --- Configuration Dictionaries ---
+# Representation functions for GNN
+REPRESENTATIONS = {
+    "smiles": smiles_to_graph,
+    "selfies": selfies_to_graph,
+    "ecfp": ecfp_to_graph,
+    "polyatomic": compressed_topsignal_graph_from_smiles,
+}
+# Feature functions for GP (numeric vectors)
+GP_FEATURIZERS = {
+    "smiles": smiles_for_gp,
+    "selfies": selfies_for_gp,
+    "ecfp": ecfp_for_gp,
+}
+GNN_MODELS = {
+    "gcn": GCN,
+    "gin": GIN,
+    "gat": GAT,
+    "sage": GraphSAGE,
+    "polyatomic": PolyatomicNet,  # Custom GNN for polyatomic complexes
+}
+try:
+    multiprocessing.set_start_method("fork")
+except RuntimeError:
+    pass
+def featurize_dataset_parallel(X, y, featurizer, n_jobs=None):
+    """Your provided parallel featurization function."""
+    if n_jobs is None:
+        n_jobs = max(1, multiprocessing.cpu_count() - 2)
+    if featurizer.__name__ == "compressed_topsignal_graph_from_smiles":
+        results = Parallel(n_jobs=n_jobs, backend="loky", verbose=10)(
+            delayed(featurizer)(xi, yi) for xi, yi in zip(X, y)
+        )
+    else:
+        results = Parallel(n_jobs=n_jobs, backend="loky", verbose=10)(
+            delayed(featurizer)(xi) for xi in X
+        )
+    # Filter out None results and attach the original y-value
+    data_list = []
+    # This robustly handles cases where some featurizations might fail
+    for i, res in enumerate(results):
+        if res is not None:
+            res.y = torch.tensor([y[i]], dtype=torch.float)
+            data_list.append(res)
+    return data_list
+def prepare_and_load_data(args):
+    """
+    Performs the expensive featurization ONCE and caches the result.
+    Subsequent runs will load the cached file instantly.
+    """
+    root_dir = Path(__file__).parent.parent.resolve().__str__()
+    datasets_dir = root_dir + "/" + "datasets"
+    cache_dir = Path(datasets_dir)
+    if not os.path.exists(cache_dir):
+        cache_dir.mkdir(exist_ok=False)
+    train_cache_file = Path(f"{cache_dir}" + "/" + f"{args.rep}_data_{args.dataset}.pt")
+    test_cache_file = Path(
+        f"{cache_dir}" + "/" + f"{args.rep}_test_data_{args.dataset}.pt"
+    )
+    print(f"train cache file is: {train_cache_file}")
+    print(f"test cache file is: {test_cache_file}")
+    if train_cache_file.exists() and test_cache_file.exists():
+        print(
+            f"INFO: Loading pre-featurized data from cache for dataset '{args.dataset}'..."
+        )
+        train_graphs = torch.load(train_cache_file, weights_only=False)
+        test_graphs = torch.load(test_cache_file, weights_only=False)
+        return train_graphs, test_graphs
+    print("INFO: No cached data found. Starting one-time featurization process...")
+    X_train, X_test, y_train, y_test = load_dataset(args.dataset)
+    featurizer = REPRESENTATIONS[args.rep]
+    print("Featurizing training set (this may take a while)...")
+    train_graphs = featurize_dataset_parallel(X_train, y_train, featurizer)
+    torch.save(train_graphs, train_cache_file)
+    print(f"Saved featurized training data to {train_cache_file}")
+    print("Featurizing test set...")
+    test_graphs = featurize_dataset_parallel(X_test, y_test, featurizer)
+    torch.save(test_graphs, test_cache_file)
+    print(f"Saved featurized test data to {test_cache_file}")
+    return train_graphs, test_graphs
+def get_model_instance(args, params, train_graphs):
+    """Instantiates a model, handling the special case for polyatomic."""
+    model_class = GNN_MODELS[args.model]
+    sample_graph = train_graphs[0]
+    if args.model == "polyatomic":
+        from torch_geometric.utils import degree
+        print("INFO: Calculating degree vector for polyatomic model...")
+        loader = DataLoader(train_graphs, batch_size=params.get("batch_size", 128))
+        deg = torch.zeros(32, dtype=torch.long)
+        for data in loader:
+            d = degree(data.edge_index[1], num_nodes=data.num_nodes, dtype=torch.long)
+            bc = torch.bincount(d, minlength=deg.size(0))
+            if bc.size(0) > deg.size(0):
+                new_deg = torch.zeros(bc.size(0), dtype=torch.long)
+                new_deg[: deg.size(0)] = deg
+                deg = new_deg
+            deg += bc
+        return model_class(
+            node_feat_dim=sample_graph.x.shape[1],
+            edge_feat_dim=sample_graph.edge_attr.shape[1],
+            graph_feat_dim=sample_graph.graph_feats.shape[0],
+            hidden_dim=params["hidden_dim"],
+            deg=deg,
+        )
+    else:
+        return model_class(
+            in_channels=sample_graph.num_node_features,
+            hidden_channels=params["hidden_dim"],
+            out_channels=1,
+        )

data/featurize.py ADDED Viewed

	@@ -0,0 +1,121 @@

+import numpy as np
+import torch
+from rdkit import Chem
+from rdkit.Chem.rdFingerprintGenerator import GetMorganGenerator
+import selfies as sf
+from torch_geometric.data import Data
+from torch_geometric.loader import DataLoader
+mfpgen = GetMorganGenerator(
+    radius=2,
+    countSimulation=False,
+    includeChirality=False,
+    useBondTypes=True,
+    onlyNonzeroInvariants=False,
+    includeRingMembership=True,
+    countBounds=None,
+    fpSize=2048,
+    atomInvariantsGenerator=None,
+    bondInvariantsGenerator=None,
+    includeRedundantEnvironments=False,
+)
+def smiles_to_graph(smiles):
+    mol = Chem.MolFromSmiles(smiles)  # type: ignore
+    if mol is None:
+        return None
+    return mol_to_graph(mol)
+def selfies_to_graph(smiles_string):
+    try:
+        selfies_string = sf.encoder(smiles_string)
+        smiles = sf.decoder(selfies_string)
+        mol = Chem.MolFromSmiles(smiles)  # type: ignore
+        if mol is None:
+            raise ValueError("Decoded SELFIES is invalid")
+        return mol_to_graph(mol)
+    except Exception:
+        fallback = smiles_to_graph(smiles_string)
+        if fallback is None:
+            return None
+        return fallback
+def ecfp_to_graph(smiles_str: str, max_bits: int = 2048, k: int = 2) -> Data | None:
+    mol = Chem.MolFromSmiles(smiles_str)  # type: ignore
+    if mol is None:
+        return None
+    fp = mfpgen.GetFingerprintAsNumPy(mol)
+    active_bits = np.nonzero(fp)[0]
+    n = len(active_bits)
+    if n == 0:
+        return None
+    edge_index = []
+    for i in range(n):
+        for j in range(i + 1, min(i + 1 + k, n)):
+            edge_index.append([i, j])
+            edge_index.append([j, i])
+    edge_index = torch.tensor(edge_index, dtype=torch.long).t().contiguous()
+    x = torch.zeros((n, max_bits), dtype=torch.float)
+    for i, bit_idx in enumerate(active_bits):
+        x[i, bit_idx] = 1.0
+    return Data(x=x, edge_index=edge_index)
+def mol_to_graph(mol):
+    atom_feats = []
+    for atom in mol.GetAtoms():
+        atom_feats.append(
+            [
+                atom.GetAtomicNum(),
+                atom.GetDegree(),
+                atom.GetFormalCharge(),
+                atom.GetIdx(),
+            ]
+        )
+    x = torch.tensor(atom_feats, dtype=torch.float)
+    edge_index = []
+    edge_attr = []
+    for bond in mol.GetBonds():
+        i = bond.GetBeginAtomIdx()
+        j = bond.GetEndAtomIdx()
+        edge_index.append((i, j))
+        edge_index.append((j, i))
+        btype = bond.GetBondTypeAsDouble()
+        edge_attr.append([btype])
+        edge_attr.append([btype])
+    edge_index = torch.tensor(edge_index, dtype=torch.long).t().contiguous()
+    edge_attr = torch.tensor(edge_attr, dtype=torch.float)
+    return Data(x=x, edge_index=edge_index, edge_attr=edge_attr)
+def smiles_for_gp(smiles: str) -> np.ndarray:
+    mol = Chem.MolFromSmiles(smiles)  # type: ignore
+    if mol is None:
+        return np.zeros(mfpgen.GetNumBits(), dtype=np.float32)
+    arr = mfpgen.GetFingerprintAsNumPy(mol)
+    return arr.astype(np.float32)
+def selfies_for_gp(selfies_str, radius=2, n_bits=2048):
+    try:
+        smiles = sf.decoder(selfies_str)
+        assert isinstance(smiles, str)
+        return smiles_for_gp(smiles)
+    except:
+        return np.zeros(n_bits)
+def ecfp_for_gp(smiles_str: str) -> np.ndarray:
+    mol = Chem.MolFromSmiles(smiles_str)  # type: ignore
+    if mol is None:
+        return np.zeros(mfpgen.GetNumBits(), dtype=np.float32)
+    return mfpgen.GetFingerprintAsNumPy(mol).astype(np.float32)
+def graph_native_loader(graph_list, batch_size=32, shuffle=True):
+    return DataLoader(graph_list, batch_size=batch_size, shuffle=shuffle)

data/loaders.py ADDED Viewed

	@@ -0,0 +1,64 @@

+import pandas as pd
+from sklearn.model_selection import train_test_split
+import importlib.resources as pkg_resources
+import polyatomic_complexes
+import numpy as np
+from typing import Tuple
+from pathlib import Path
+def load_dataset(name) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    if name.lower() == "esol":
+        data_path = (
+            pkg_resources.files("polyatomic_complexes.dataset.esol") / "ESOL.csv"
+        )
+        df = pd.read_csv(str(data_path))
+        target_col = "measured log solubility in mols per litre"
+    elif name.lower() == "freesolv":
+        data_path = (
+            pkg_resources.files("polyatomic_complexes.dataset.free_solv")
+            / "FreeSolv.csv"
+        )
+        df = pd.read_csv(str(data_path))
+        target_col = "expt"
+    elif name.lower() == "lipophil":
+        data_path = (
+            pkg_resources.files("polyatomic_complexes.dataset.lipophilicity")
+            / "Lipophilicity.csv"
+        )
+        df = pd.read_csv(str(data_path))
+        target_col = "exp"
+    elif name.lower() == "boilingpoint":
+        data_path = (
+            Path(__file__).parent.parent / "benchmark_csv/boiling_point.csv".__str__()
+        )
+        df = pd.read_csv(data_path)
+        target_col = "boiling_point_K"
+    elif name.lower() == "qm9":
+        data_path = (
+            Path(__file__).parent.parent / "benchmark_csv/qm9_subset.csv".__str__()
+        )
+        df = pd.read_csv(data_path)
+        target_col = "cv"
+    elif name.lower() == "ic50":
+        data_path = (
+            Path(__file__).parent.parent / "benchmark_csv/ic_50_subset.csv".__str__()
+        )
+        df = pd.read_csv(data_path)
+        target_col = "pIC50"
+    elif name.lower() == "bindingdb":
+        data_path = (
+            Path(__file__).parent.parent / "benchmark_csv/bindingdb.csv".__str__()
+        )
+        df = pd.read_csv(data_path)
+        target_col = "pIC50"
+    else:
+        raise ValueError(f"Unknown dataset: {name}")
+    df.dropna(subset=["smiles", target_col], inplace=True)
+    smiles = df["smiles"]
+    targets = df[target_col]
+    X_train, X_test, y_train, y_test = train_test_split(
+        smiles, targets, test_size=0.2, random_state=42
+    )
+    return X_train.to_numpy(), X_test.to_numpy(), y_train.to_numpy(), y_test.to_numpy()

data/polyatomic_featurize.py ADDED Viewed

	@@ -0,0 +1,160 @@

+import torch
+import numpy as np
+from rdkit import Chem
+import networkx as nx
+from collections import defaultdict
+from torch_geometric.data import Data
+from polyatomic_complexes.src.complexes.abstract_complex import AbstractComplex
+from polyatomic_complexes.src.complexes import PolyatomicGeometrySMILE
+def compressed_topsignal_graph_from_smiles(
+    smile: str, y_val: int, topk_lap: int = 5
+) -> Data | None:
+    try:
+        # 1) Abstract complex
+        pg = PolyatomicGeometrySMILE(smile=smile, mode="abstract")
+        ac = pg.smiles_to_geom_complex()
+        assert isinstance(ac, AbstractComplex)
+        # 2) RDKit molecule
+        mol = Chem.MolFromSmiles(smile)  # type: ignore
+        if mol is None:
+            return None
+        # 3) Node features: chain0 value + RDKit descriptors
+        chains = ac.get_raw_k_chains()
+        chain0 = chains.get("chain_0", [])
+        atom_types = [6, 7, 8, 15, 16, 17]
+        hyb_types = [
+            Chem.rdchem.HybridizationType.SP,
+            Chem.rdchem.HybridizationType.SP2,
+            Chem.rdchem.HybridizationType.SP3,
+        ]
+        node_feats = []
+        for atom in mol.GetAtoms():
+            idx = atom.GetIdx()
+            # fallback if chain0 shorter than atom count
+            c0 = float(chain0[idx]) if idx < len(chain0) else 0.0
+            feats = [c0]
+            feats += one_hot(atom.GetAtomicNum(), atom_types)
+            feats += one_hot(atom.GetHybridization(), hyb_types)
+            feats += [
+                float(atom.GetDegree()),
+                float(atom.GetIsAromatic()),
+                float(atom.GetFormalCharge()),
+            ]
+            node_feats.append(feats)
+        x = torch.tensor(node_feats, dtype=torch.float32)
+        n = x.size(0)  # use number of atoms for all subsequent node counts
+        # 4) Edges: abstract bonds + RDKit fallback
+        sk = ac.get_skeleta().get("molecule_skeleta", [[]])[0]
+        zero = next((lst for dim, lst in sk if dim == "0"), [])
+        node_ids = [next(iter(fz))[0] for fz in zero]
+        atom_map = defaultdict(list)
+        for i, nid in enumerate(node_ids):
+            symbol = nid.split("_")[0]
+            atom_map[symbol].append(i)
+        edge_index_list, edge_attr_list = [], []
+        bond_types = [
+            Chem.rdchem.BondType.SINGLE,
+            Chem.rdchem.BondType.DOUBLE,
+            Chem.rdchem.BondType.TRIPLE,
+            Chem.rdchem.BondType.AROMATIC,
+        ]
+        for a1, a2, (btype, order) in ac.get_bonds():
+            bt_val = getattr(Chem.rdchem.BondType, btype, None)
+            for i in atom_map.get(a1, []):
+                for j in atom_map.get(a2, []):
+                    if i < n and j < n:
+                        edge_index_list += [[i, j], [j, i]]
+                        attr = one_hot(bt_val, bond_types) + [float(order), 0.0]
+                        edge_attr_list += [attr, attr]
+        if not edge_index_list:
+            for bond in mol.GetBonds():
+                i, j = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()
+                edge_index_list += [[i, j], [j, i]]
+                attr = one_hot(bond.GetBondType(), bond_types)
+                attr += [float(bond.GetIsConjugated()), float(bond.IsInRing())]
+                edge_attr_list += [attr, attr]
+        edge_index = torch.tensor(edge_index_list, dtype=torch.long).t().contiguous()
+        edge_attr = torch.tensor(edge_attr_list, dtype=torch.float32)
+        # 5) Topology features: centrality + SPD
+        G = nx.Graph()
+        G.add_nodes_from(range(n))
+        G.add_edges_from(edge_index_list)
+        cent = nx.closeness_centrality(G)
+        spd = dict(nx.all_pairs_shortest_path_length(G))
+        cent_vec = [cent.get(i, 0.0) for i in range(n)]
+        spd_vec = [
+            sum(d.values()) / max(len(d), 1) for d in (spd.get(i, {}) for i in range(n))
+        ]
+        cent_t = torch.tensor(cent_vec, dtype=torch.float32).view(n, 1)
+        spd_t = torch.tensor(spd_vec, dtype=torch.float32).view(n, 1)
+        x = torch.cat([x, cent_t, spd_t], dim=1)
+        # print("MANAGED TO CONCAT?")
+        # 6) Graph-level features: chain stats + laplacians
+        g_stats, lap_feats = [], []
+        for k, arr in chains.items():
+            if k == "chain_0":
+                continue
+            a = np.array(arr, dtype=np.float32)
+            g_stats += [a.mean(), a.std()]
+        # print("COMPUTED GRAP STATS")
+        for grp in ac.get_laplacians().get("molecule_laplacians", []):
+            recs = grp if isinstance(grp, list) else [grp]
+            for _, mat in recs:
+                # use dense eigen solver to avoid ARPACK issues
+                M = np.array(mat, dtype=np.float32)
+                # compute eigenvalues of symmetric Laplacian
+                try:
+                    eigs = np.linalg.eigvalsh(M)
+                except Exception:
+                    eigs = np.zeros(M.shape[0], dtype=np.float32)
+                # take smallest non-zero eigenvalues (skip the first zero)
+                nonzero = eigs[eigs > 1e-6]
+                vals = nonzero[:topk_lap] if len(nonzero) >= topk_lap else nonzero
+                # pad to exactly topk_lap
+                if len(vals) < topk_lap:
+                    vals = np.pad(vals, (0, topk_lap - len(vals)))
+                lap_feats += list(vals)
+        # --- 7) Spectral k-chains stats ---
+        spectral = ac.get_spectral_k_chains()
+        spec_feats = []
+        for arr in spectral.values():
+            a = np.array(arr, dtype=np.float32)
+            spec_feats += [a.mean(), a.std()]
+        # --- 8) Betti numbers (components & cycles) ---
+        b0 = nx.number_connected_components(G)
+        b1 = sum(
+            len(nx.cycle_basis(G.subgraph(comp))) for comp in nx.connected_components(G)
+        )
+        # --- 9) Assemble graph_feats ---
+        all_feats = g_stats + lap_feats + spec_feats + [float(b0), float(b1)]
+        graph_feats = torch.tensor(all_feats, dtype=torch.float32)
+        # print("managed to feat?")
+        data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr)
+        data.graph_feats = graph_feats
+        data.y = torch.tensor([y_val], dtype=torch.float)
+        # print(f"SUCCESS for : {smile}")
+        return data
+    except Exception as e:
+        # print(f"Failed {smile}: {e}")
+        return None
+def one_hot(val, choices):
+    return [1.0 if val == c else 0.0 for c in choices]

datasets/ecfp_data_bindingdb.pt ADDED Viewed

	@@ -0,0 +1,3 @@

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+size 134

datasets/ecfp_data_boilingpoint.pt ADDED Viewed

	@@ -0,0 +1,3 @@

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datasets/ecfp_data_esol.pt ADDED Viewed

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datasets/ecfp_data_freesolv.pt ADDED Viewed

	@@ -0,0 +1,3 @@

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datasets/ecfp_data_ic50.pt ADDED Viewed

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datasets/ecfp_data_lipophil.pt ADDED Viewed

	@@ -0,0 +1,3 @@

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+oid sha256:db8d5f3a7641b0547979d1cc822d343d1c6e3531453f2e851ce671ccc0b3849b
+size 135

datasets/ecfp_data_qm9.pt ADDED Viewed

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