Upload 8 files

src/.ipynb_checkpoints/01_install_packages-checkpoint.sh

@@ -0,0 +1,32 @@
+
+# Install LiabilityPredictor
+
+# clone LiabilityPredictor project
+# it says you can use assay_liability_calculator.py to make
+# predictions using the model, but as of 2/2025, this script is broken
+# so instead use src/predict_liability.py instead
+
+cd src
+# git clone https://github.com/jimmyjbling/LiabilityPredictor.git
+# cd LiabilityPredictor
+
+# pip install -r requirements.txt
+# cd ..
+
+
+## h2o is an AutoML platform
+pip install -f http://h2o-release.s3.amazonaws.com/h2o/latest_stable_Py.html h2o
+
+# on OSX I had to also install the java JDK
+# brew install java
+# I then got an error that I was able to resolve using this
+# https://stackoverflow.com/a/65601197
+# sudo ln -sfn /opt/homebrew/opt/openjdk/libexec/openjdk.jdk \
+#      /Library/Java/JavaVirtualMachines/openjdk.jdk
+
+
+##  datamol / molfeat
+pip install molfeat
+pip install datamol
+
+

src/01_install_packages.sh

@@ -0,0 +1,32 @@
+
+# Install LiabilityPredictor
+
+# clone LiabilityPredictor project
+# it says you can use assay_liability_calculator.py to make
+# predictions using the model, but as of 2/2025, this script is broken
+# so instead use src/predict_liability.py instead
+
+cd src
+# git clone https://github.com/jimmyjbling/LiabilityPredictor.git
+# cd LiabilityPredictor
+
+# pip install -r requirements.txt
+# cd ..
+
+
+## h2o is an AutoML platform
+pip install -f http://h2o-release.s3.amazonaws.com/h2o/latest_stable_Py.html h2o
+
+# on OSX I had to also install the java JDK
+# brew install java
+# I then got an error that I was able to resolve using this
+# https://stackoverflow.com/a/65601197
+# sudo ln -sfn /opt/homebrew/opt/openjdk/libexec/openjdk.jdk \
+#      /Library/Java/JavaVirtualMachines/openjdk.jdk
+
+
+##  datamol / molfeat
+pip install molfeat
+pip install datamol
+
+

src/02_load_data.py

@@ -0,0 +1,26 @@
+import datasets
+import yaml
+
+import pyarrow as pa
+import pyarrow.parquet as pq
+from sklearn.model_selection import train_test_split
+import pandas as pd
+
+with open("parameters.yaml") as parameters_file:
+    parameters = yaml.safe_load(parameters_file)
+
+
+df = pd.read_csv("hf://datasets/aanyam/ESSENCEDock_595Project/ESSENCEDock_dataset_final.csv")
+
+train_df, test_df = train_test_split(df, test_size=0.2, stratify=df['Target Name'], random_state=42)
+
+data_train = train_df[train_df["Target Name"].isin(parameters['targets'])]
+pq.write_table(
+    pa.Table.from_pandas(data_train),
+    "intermediate_data/data_train.parquet")
+
+
+data_test = test_df[test_df["Target Name"].isin(parameters['targets'])]
+pq.write_table(
+    pa.Table.from_pandas(data_test),
+    "intermediate_data/data_test.parquet")

src/03_datamol.py

@@ -0,0 +1,27 @@
+import pandas as pd
+import numpy as np
+
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+from molfeat.calc import FPCalculator
+from molfeat.trans import MoleculeTransformer
+
+calc = FPCalculator("ecfp")
+mol_transf = MoleculeTransformer(calc, n_jobs=5)
+
+data_train_moldata = pq.read_table("intermediate_data/data_train.parquet").to_pandas()
+data_train_features = mol_transf(data_train_moldata["SMILES"].values)
+data_train_features = np.stack(data_train_features)
+data_train_features = pd.DataFrame(data_train_features)
+pq.write_table(
+    pa.Table.from_pandas(data_train_features),
+    "intermediate_data/data_train_features.parquet")
+
+data_test_moldata = pq.read_table("intermediate_data/data_test.parquet").to_pandas()
+data_test_features = mol_transf(data_test_moldata['SMILES'].values)
+data_test_features = np.stack(data_test_features)
+data_test_features = pd.DataFrame(data_test_features)
+pq.write_table(
+    pa.Table.from_pandas(data_test_features),
+    "intermediate_data/data_test_features.parquet")

src/04_join_data.R

@@ -0,0 +1,42 @@
+### Train
+data_train_moldata <- arrow::read_parquet(
+   "intermediate_data/data_train.parquet")
+data_train_features <- arrow::read_parquet(
+   "intermediate_data/data_train_features.parquet")
+
+names(data_train_features) <- paste0(
+  "feature_", names(data_train_features))
+
+data_train <- data_train_moldata |>
+   dplyr::mutate(
+     dplyr::across(
+       dplyr::everything(),
+       ~tidyr::replace_na(.x, 0))) |>
+   dplyr::bind_cols(
+     data_train_features) 
+
+data_train |>
+  arrow::write_parquet(
+    "intermediate_data/data_train_joined.parquet")
+
+
+### Test
+data_test_moldata <- arrow::read_parquet(
+   "intermediate_data/data_test.parquet")
+data_test_features <- arrow::read_parquet(
+   "intermediate_data/data_test_features.parquet")
+
+names(data_test_features) <- paste0(
+  "feature_", names(data_test_features))
+
+data_test <- data_test_moldata |>
+   dplyr::mutate(
+     dplyr::across(
+       dplyr::everything(),
+       ~tidyr::replace_na(.x, 0))) |>
+   dplyr::bind_cols(
+     data_test_features) 
+
+data_test |>
+  arrow::write_parquet(
+    "intermediate_data/data_test_joined.parquet")

src/05_predict_outcomes_CXCR4.py

@@ -0,0 +1,66 @@
+import pandas as pd
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+import h2o
+from h2o.automl import H2OAutoML
+from h2o.frame import H2OFrame
+import pickle
+import os
+
+# Define the target
+target = "CXCR4"  # Replace with WEE1 or CXCR4 as needed
+
+# Start the H2O cluster
+h2o.init()
+
+# Load datasets
+data_train = pq.read_table("intermediate_data/data_train_joined.parquet").to_pandas()
+data_test = pq.read_table("intermediate_data/data_test_joined.parquet").to_pandas()
+
+# Filter data for the selected target
+data_train = data_train[data_train["Target Name"] == target]
+data_test = data_test[data_test["Target Name"] == target]
+
+# Define target column
+target_column = " RMSD_Energy"
+
+# Identify feature columns: all columns after "LF_score" (inclusive)
+start_idx = list(data_train.columns).index("LF_score")
+feature_columns = data_train.columns[start_idx:].tolist()
+feature_columns = [col for col in feature_columns if col != " RMSD_Energy"]
+
+# Convert to H2OFrame
+train_h2o = H2OFrame(data_train)
+test_h2o = H2OFrame(data_test)
+
+# train_h2o[target_column] = train_h2o[target_column].asnumeric()
+# test_h2o[target_column] = test_h2o[target_column].asnumeric()
+
+# train_h2o[target_column] = train_h2o[target_column].asfactor()
+# test_h2o[target_column] = test_h2o[target_column].asfactor()
+
+# Train AutoML model
+aml = H2OAutoML(max_models=1, seed=42)
+aml.train(x=feature_columns, y=target_column, training_frame=train_h2o)
+
+# Save the trained model
+top_model = aml.leader
+model_path = h2o.save_model(top_model, path = f"intermediate_data/top_model{target}", force=True)
+
+# Generate predictions
+for dataset_name, dataset_h2o, dataset_df in [
+    ("train", train_h2o, data_train),
+    ("test", test_h2o, data_test),
+]:
+    predictions = aml.leader.predict(dataset_h2o).as_data_frame()
+    dataset_df["predictions"] = predictions["predict"]
+
+    # Save predictions
+    output_path = f"intermediate_data/data_{dataset_name}_{target}_pred.parquet"
+    pq.write_table(pa.Table.from_pandas(dataset_df), output_path)
+
+# Shutdown H2O cluster
+h2o.shutdown(prompt=False)
+
+print(f"Completed training and predictions for {target}")

src/05_predict_outcomes_MK14.py

@@ -0,0 +1,75 @@
+import pandas as pd
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+import h2o
+from h2o.automl import H2OAutoML
+from h2o.frame import H2OFrame
+import pickle
+import os
+import matplotlib.pyplot as plt
+
+# Define the target
+target = "MK14"  # Replace with WEE1 or CXCR4 as needed
+
+# Start the H2O cluster
+h2o.init()
+
+# Load datasets
+data_train = pq.read_table("intermediate_data/data_train_joined.parquet").to_pandas()
+data_test = pq.read_table("intermediate_data/data_test_joined.parquet").to_pandas()
+
+# Filter data for the selected target
+data_train = data_train[data_train["Target Name"] == target]
+data_test = data_test[data_test["Target Name"] == target]
+
+# Define target column
+target_column = " RMSD_Energy"
+
+# Identify feature columns: all columns after "LF_score" (inclusive)
+start_idx = list(data_train.columns).index("LF_score")
+feature_columns = data_train.columns[start_idx:].tolist()
+feature_columns = [col for col in feature_columns if col != " RMSD_Energy"]
+
+# Convert to H2OFrame
+train_h2o = H2OFrame(data_train)
+test_h2o = H2OFrame(data_test)
+
+# train_h2o[target_column] = train_h2o[target_column].asnumeric()
+# test_h2o[target_column] = test_h2o[target_column].asnumeric()
+
+# train_h2o[target_column] = train_h2o[target_column].asfactor()
+# test_h2o[target_column] = test_h2o[target_column].asfactor()
+
+# Train AutoML model
+aml = H2OAutoML(max_models=1, seed=42)
+aml.train(x=feature_columns, y=target_column, training_frame=train_h2o)
+
+# Save the trained model
+top_model = aml.leader
+model_path = h2o.save_model(top_model, path = f"intermediate_data/top_model{target}", force=True)
+
+# Generate predictions
+for dataset_name, dataset_h2o, dataset_df in [
+    ("train", train_h2o, data_train),
+    ("test", test_h2o, data_test),
+]:
+    predictions = aml.leader.predict(dataset_h2o).as_data_frame()
+    dataset_df["predictions"] = predictions["predict"]
+
+    # Save predictions
+    output_path = f"intermediate_data/data_{dataset_name}_{target}_pred.parquet"
+    pq.write_table(pa.Table.from_pandas(dataset_df), output_path)
+
+test_h2o_no_smiles = test_h2o.drop("SMILES")
+
+# Analysis
+top_model.explain(test_h2o_no_smiles)
+plt.savefig(
+	f"product/model_summary_{target}_{parameters["date_code"]}.pdf",
+        format = "pdf", bbox_inches = "tight")
+
+# Shutdown H2O cluster
+h2o.shutdown(prompt=False)
+
+print(f"Completed training and predictions for {target}")

src/05_predict_outcomes_WEE1.py

@@ -0,0 +1,66 @@
+import pandas as pd
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+import h2o
+from h2o.automl import H2OAutoML
+from h2o.frame import H2OFrame
+import pickle
+import os
+
+# Define the target
+target = "WEE1"  # Replace with WEE1 or CXCR4 as needed
+
+# Start the H2O cluster
+h2o.init()
+
+# Load datasets
+data_train = pq.read_table("intermediate_data/data_train_joined.parquet").to_pandas()
+data_test = pq.read_table("intermediate_data/data_test_joined.parquet").to_pandas()
+
+# Filter data for the selected target
+data_train = data_train[data_train["Target Name"] == target]
+data_test = data_test[data_test["Target Name"] == target]
+
+# Define target column
+target_column = " RMSD_Energy"
+
+# Identify feature columns: all columns after "LF_score" (inclusive)
+start_idx = list(data_train.columns).index("LF_score")
+feature_columns = data_train.columns[start_idx:].tolist()
+feature_columns = [col for col in feature_columns if col != " RMSD_Energy"]
+
+# Convert to H2OFrame
+train_h2o = H2OFrame(data_train)
+test_h2o = H2OFrame(data_test)
+
+# train_h2o[target_column] = train_h2o[target_column].asnumeric()
+# test_h2o[target_column] = test_h2o[target_column].asnumeric()
+
+# train_h2o[target_column] = train_h2o[target_column].asfactor()
+# test_h2o[target_column] = test_h2o[target_column].asfactor()
+
+# Train AutoML model
+aml = H2OAutoML(max_models=1, seed=42)
+aml.train(x=feature_columns, y=target_column, training_frame=train_h2o)
+
+# Save the trained model
+top_model = aml.leader
+model_path = h2o.save_model(top_model, path = f"intermediate_data/top_model{target}", force=True)
+
+# Generate predictions
+for dataset_name, dataset_h2o, dataset_df in [
+    ("train", train_h2o, data_train),
+    ("test", test_h2o, data_test),
+]:
+    predictions = aml.leader.predict(dataset_h2o).as_data_frame()
+    dataset_df["predictions"] = predictions["predict"]
+
+    # Save predictions
+    output_path = f"intermediate_data/data_{dataset_name}_{target}_pred.parquet"
+    pq.write_table(pa.Table.from_pandas(dataset_df), output_path)
+
+# Shutdown H2O cluster
+h2o.shutdown(prompt=False)
+
+print(f"Completed training and predictions for {target}")