PyG ChemProp Implementation#


“ChemProp” is a simple but effective Graph Neural Network (GNN) for Molecular Property Prediction, first used successfully in anti-biotic discovery in 2019 by Yang et al. Like SPGNNs, it provides an alternative way to represent molecules as 3D graphs with nodes (atoms) and edges (bonds) instead of a 1D string representation (“SMILES”), which can provide added functionality.

Here, we will briefly overview the implementation of the original ChemProp model oce uses which is adapted from Takigawa’s Github repository. We will discuss its functionality with oce’s BaseModel structure and compare our results to the original ChemProp’s results.

ChemProp Model Training#

In this example, we will train a ChemProp model on the HIV dataset from Stanford OGB.

import io
import sys
import zipfile

import pandas as pd
import requests
from sklearn.metrics import accuracy_score, roc_auc_score
from sklearn.model_selection import train_test_split

import olorenchemengine as oce

Next, we create the dataset, check our model’s definition, and fit it to training data. oce’s backend takes care of all of this in just a few lines of code, from train-test splits to preprocessing to SMILES-to-graph conversions.

data_dir = "./data"
data_url = ""
r = requests.get(data_url)
z = zipfile.ZipFile(io.BytesIO(r.content))
df = pd.read_csv(f"{data_dir}/hiv/mapping/mol.csv.gz")

X_train, X_test, y_train, y_test = train_test_split(df["smiles"], df["HIV_active"], test_size=0.2, random_state=42)

model = oce.ChemPropModel(), y_train)

Now, we’ll evaluate the results of our training/fitting on the test set, and see if we can achieve a better accuracy than the original example dataset, which has an ROC_AUC score (auc) of 0.679 and an Accuracy score (acc) of 0.968.

y_pred = model.predict(X_test)

auc = roc_auc_score(y_test, y_pred)
acc = accuracy_score(y_test, (y_pred > 0.5).astype(int))
print(f"test auc={auc:.6} acc={acc:.6}", file=sys.stderr)
8226it [00:22, 364.44it/s]
100%|██████████| 165/165 [00:10<00:00, 16.49it/s]
test auc=0.704605 acc=0.963895