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Merged
LindeSchoenmaker merged 3 commits into from
Nov 17, 2025
Merged

Add PD AOP models #49

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4626952
initial commit for adding pd models
LindeSchoenmaker Nov 14, 2025
fe6a729
change model files
LindeSchoenmaker Nov 14, 2025
631e78d
add qmrf documents pd models
LindeSchoenmaker Nov 17, 2025
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3 changes: 2 additions & 1 deletion Dockerfile
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@ WORKDIR /usr/src/app

# Install any needed packages specified in requirements.txt
COPY requirements.txt requirements.txt
COPY spock spock
RUN pip install -r requirements.txt

# Copy the current directory contents into the container at /usr/src/app
Expand All @@ -24,7 +25,7 @@ COPY static static
COPY data data
COPY qprf qprf
COPY templates templates
COPY app.py entrypoint.sh ./
COPY app.py ifp.py entrypoint.sh ./

RUN chmod +x /usr/src/app/entrypoint.sh

Expand Down
134 changes: 87 additions & 47 deletions app.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,9 @@
from reportlab.lib.units import inch
from reportlab.platypus import Paragraph, SimpleDocTemplate, Spacer, Table, TableStyle

logger = logging.getLogger(__name__)

import ifp
from qprf.qprf_utils import render_qprf

app = Flask(__name__)
Expand Down Expand Up @@ -62,7 +65,7 @@ class SimilaritySearcher():
def __init__(self):
self.descgen = AllChem.GetMorganGenerator(radius=3)
self.scorer = DataStructs.BulkTanimotoSimilarity

def get_nearest_neighbors(self, smile, ms):
"""_summary_

Expand All @@ -80,9 +83,9 @@ def get_nearest_neighbors(self, smile, ms):
scores = self.scorer(query_fp, target_fingerprints)

id_top = np.argmax(np.array(scores))

return id_top, max(scores)

def extract_model_info(directory):
models_info = []
for d in os.listdir(directory):
Expand All @@ -98,8 +101,6 @@ def extract_model_info(directory):
'target_property_name': state['targetProperties'][0]['py/state']['name'],
'target_property_task': state['targetProperties'][0]['py/state']['task']['py/reduce'][1]['py/tuple'][0],
'feature_calculator': state['featureCalculators'][0]['py/object'].split('.')[-1],
'radius': state['featureCalculators'][0]['py/state']['radius'],
'nBits': state['featureCalculators'][0]['py/state']['nBits'],
'algorithm': state['alg'].split('.')[-1],
'currDir': os.path.join(directory, d),
}
Expand All @@ -119,6 +120,33 @@ def download_qprf():
smile = request.args.get('smile')
return send_file(f"qprf/output/{model}/{smile}.docx", as_attachment=True)

@app.route('/downloadposes')
def download_poses():
smile = request.args.get('smile')
protein_id = request.args.get('protein_id')
return send_file(f"{smile}_{protein_id}.sdf", as_attachment=True)

# def zipdir(path, ziph):
# # ziph is zipfile handle
# for root, dirs, files in os.walk(path):
# for file in files:
# ziph.write(os.path.join(root, file),
# os.path.relpath(os.path.join(root, file),
# os.path.join(path, '..')))



# @app.route('/downloadposes')
# def download_poses():
# logging.basicConfig(filename='myapp.log', level=logging.INFO)
# logger.info('Started')
# store_names = request.args.get('store_names')
# logger.info(store_names)
# for store_name in store_names:
# with zipfile.ZipFile('Python.zip', 'w', zipfile.ZIP_DEFLATED) as zipf:
# zipdir(f'{store_name}/', zipf)
# return send_file('Python.zip', as_attachment=True)

@app.route('/')
@app.route('/predict')
def home():
Expand All @@ -134,23 +162,23 @@ def predict():
uploaded_file = request.files.get('file')
model_names = request.form.getlist('model')
file_name = request.form.get('uploaded_file_name')

logging.debug(f"Received SMILES input: {smiles_input}")
logging.debug(f"Uploaded file: {uploaded_file}")
logging.debug(f"Selected models: {model_names}")
logging.debug(f"Previous uploaded file name: {file_name}")

if not model_names:
logging.error("No model selected.")
return render_template('index.html', models=available_models, error="No model selected.")

smiles_list = []
invalid_smiles = []

# Handle SMILES string input
if smiles_input:
input_smiles = [smile.strip() for smile in smiles_input.split(',')]

# Check if only one SMILES string is entered
if len(input_smiles) == 1:
if Chem.MolFromSmiles(input_smiles[0]) is None: # Check for invalid single SMILES
Expand All @@ -161,7 +189,7 @@ def predict():
else:
invalid_smiles.extend([smile for smile in input_smiles if Chem.MolFromSmiles(smile) is None]) # Collect invalid SMILES
smiles_list.extend([smile for smile in input_smiles if Chem.MolFromSmiles(smile) is not None]) # Collect valid SMILES

# Handle uploaded file
if uploaded_file and uploaded_file.filename != '':
file_name = uploaded_file.filename
Expand All @@ -181,23 +209,24 @@ def predict():
file_smiles = uploaded_df['SMILES'].tolist()
invalid_smiles.extend([smile for smile in file_smiles if Chem.MolFromSmiles(smile) is None]) # Collect invalid SMILES from previous file
smiles_list.extend([smile for smile in file_smiles if Chem.MolFromSmiles(smile) is not None]) # Collect valid SMILES from previous file

logging.debug(f"Final SMILES list: {smiles_list}")
logging.debug(f"Invalid SMILES detected: {invalid_smiles}") # Log invalid SMILES

if not smiles_list and not invalid_smiles:
error_message = "No SMILES strings provided"
logging.error(error_message)
return render_template('index.html', models=available_models, error=error_message)


store_names = []
all_predictions = {}
all_ads = {}
model_info_list = []
data_dict = {
"Model": {},
"Input": {'SMILES': {}, 'Input structure': {}},
"Nearest Neighbor": {'NN structure': {}, 'Similarity': {}, 'Source': {}, 'Experimental value': {}},
"Output": {'Predicted pChEMBL Value': {}, 'Within Applicability Domain': {}, 'Download QPRF': {}}
"Output": {'Predicted pChEMBL Value': {}, 'Within Applicability Domain': {}, 'Download QPRF': {}, "Download Poses": {}}
}
tooltip_dict = {"Model": 'Unique identifier of the model that made the prediction',
"Input structure": '2D depiction of molecule',
Expand All @@ -209,20 +238,25 @@ def predict():
"Experimental value": 'Average of all reported experimental values',
"Predicted pChEMBL Value": 'Model prediction for input molecule. pChEMBL is defined as -log(response). More information available in QMRF & QPRF',
"Within Applicability Domain": 'AD is based on descriptors of training set. An input molecule is within AD if the distance to the training set is lower than a set threshold. More information available in QMRF & QPRF',
"Download QPRF": 'Automatically filled in report about the prediction'
"Download QPRF": 'Automatically filled in report about the prediction',
"Download Poses": 'Get SDF with docked ligand poses'
}

for model_name in model_names:
logging.debug(f"Processing model: {model_name}")
model_path = os.path.join(MODELS_DIR, model_name, f"{model_name}_meta.json")
model = SklearnModel.fromFile(model_path)
ad = []
if getattr(model, 'applicabilityDomain', None):
if model.featureCalculators[0].__class__.__name__ == "DataFrameDescriptorSet":
predictions, ad, lib_name = ifp.predict(smiles_list, model, model.name.replace("_final", ""), "dimorph")
ad = list(ad)
store_names.append(lib_name)
elif getattr(model, 'applicabilityDomain', None):
predictions, ad = model.predictMols(smiles_list, use_applicability_domain=True)
ad = list(ad)
else:
predictions = model.predictMols(smiles_list)

# Check if the model is regression or classification
if model.task.isRegression():
formatted_predictions = [f"{pred[0]:.2f}" for pred in predictions]
Expand All @@ -233,7 +267,7 @@ def predict():
all_predictions[model_name] = formatted_predictions
all_ads[model_name] = ad


# Extract model info for report
with open(model_path, 'r') as meta_file:
meta_data = json.load(meta_file)
Expand All @@ -245,36 +279,34 @@ def predict():
'target_property_name': state['targetProperties'][0]['py/state']['name'],
'target_property_task': state['targetProperties'][0]['py/state']['task']['py/reduce'][1]['py/tuple'][0],
'feature_calculator': state['featureCalculators'][0]['py/object'].split('.')[-1],
'radius': state['featureCalculators'][0]['py/state']['radius'],
'nBits': state['featureCalculators'][0]['py/state']['nBits'],
'algorithm': state['alg'].split('.')[-1]
}
model_info_list.append(model_info)

table_data = []
for i, smile in enumerate(smiles_list):

for i, smile in enumerate(smiles_list):
image_data = smiles_to_image(smile)
if getattr(model, 'applicabilityDomain', None):
row = [image_data] + [smile] + [all_predictions[model][i] + f' ({str(all_ads[model][i])})' for model in model_names]
else:
row = [image_data] + [smile] + [all_predictions[model][i] for model in model_names]

table_data.append(row)

# Update headers
headers = ['Structure', 'SMILES']
tooltips = ['2D depiction of input molecule', 'Line representation of input molecule']

searcher = SimilaritySearcher()
for model_name in model_names:
accession = model_name.split("_")[0]
train_df = pd.read_csv(f'data/{accession}_Data/train_full_model_{accession}.csv').reset_index()
train_smiles = train_df['SMILES'].to_list()
ms = [Chem.MolFromSmiles(x) for x in train_smiles]
model_path = os.path.join(MODELS_DIR, model_name, f"{model_name}_meta.json")
model_path = os.path.join(MODELS_DIR, model_name, f"{model_name}_meta.json")
model = SklearnModel.fromFile(model_path)

if getattr(model, 'applicabilityDomain', None):
if model.task.isRegression():
# Format regression table header
Expand All @@ -294,15 +326,15 @@ def predict():
headers.append(f'Predicted class label ({model_name})')
tooltips.append('Model prediction for input molecule')

for i, smile in enumerate(smiles_list):

for i, smile in enumerate(smiles_list):
image_data = smiles_to_image(smile)
id_top, score = searcher.get_nearest_neighbors(smile, ms)
nearest_neighbor = {}
nn_smiles = train_df.iloc[id_top]['SMILES']
nearest_neighbor["smiles"] = nn_smiles
doc_ids = train_df.iloc[id_top]['all_doc_ids']
doc_ids = doc_ids.split(';')
doc_ids = str(doc_ids).split(';')
doc_links = []
for doc_id in doc_ids:
if doc_id.startswith('PMID:'):
Expand All @@ -311,10 +343,13 @@ def predict():
doc_links.append('https://doi.org/' + doc_id)
elif doc_id.startswith('PubChemAID:'):
doc_links.append(f'https://pubchem.ncbi.nlm.nih.gov/bioassay/{doc_id.lstrip("PubChemAID:")}/')

nearest_neighbor["reference"] = doc_ids
nearest_neighbor["value"] = train_df.iloc[id_top]['pchembl_value_Mean']
nearest_neighbor["predicted_value"] = model.predictMols([nn_smiles])[0][0]
nearest_neighbor["value"] = train_df.iloc[id_top][model_info['target_property_name']]
if model.featureCalculators[0].__class__.__name__ == "DataFrameDescriptorSet":
nearest_neighbor["predicted_value"] = ifp.predict([nn_smiles], model, model.name.replace("_final", ""), "dimorph")[0][0]
else:
nearest_neighbor["predicted_value"] = model.predictMols([nn_smiles])[0][0]
nearest_neighbor["similarity"] = f"Nearest neighbor was found using {searcher.scorer.__name__} based on {searcher.descgen.__class__.__name__}"
image_data_nn = smiles_to_image(nn_smiles)

Expand All @@ -328,22 +363,29 @@ def predict():
data_dict["Nearest Neighbor"]["Source"].setdefault("links", []).append(doc_links)
data_dict["Nearest Neighbor"]["Experimental value"].setdefault("value", []).append(f"{nearest_neighbor['value']:.2f}")
data_dict["Output"]["Predicted pChEMBL Value"].setdefault("value", []).append(all_predictions[model_name][i])
data_dict["Output"]["Within Applicability Domain"].setdefault("value", []).append(all_ads[model_name][i])
if len(all_ads[model_name]) > i:
data_dict["Output"]["Within Applicability Domain"].setdefault("value", []).append(all_ads[model_name][i])
else:
data_dict["Output"]["Within Applicability Domain"].setdefault("value", []).append(None)

data_dict["Output"]["Download QPRF"].setdefault("url", []).append("hi")
if model.featureCalculators[0].__class__.__name__ == "DataFrameDescriptorSet":
data_dict["Output"]["Download Poses"].setdefault("pose", []).append("hi")
error_message = None
if invalid_smiles:
error_message = f"Invalid SMILES, could not be processed: {', '.join(invalid_smiles)}" # Mention invalid SMILES in error message

return render_template('index.html',
models=available_models,
data_dict=data_dict,
headers=headers,
tooltips=tooltips,
data=table_data,
tooltips_extensive = tooltip_dict,
smiles_input=smiles_input,
model_names=model_names,
file_name=file_name,

return render_template('index.html',
models=available_models,
data_dict=data_dict,
headers=headers,
tooltips=tooltips,
data=table_data,
tooltips_extensive = tooltip_dict,
smiles_input=smiles_input,
model_names=model_names,
file_name=file_name,
store_names=store_names,
error=error_message)
except Exception:
logging.exception("An error occurred while processing the request.")
Expand All @@ -367,8 +409,6 @@ def create_report(model_info_list, headers, table_data):
elements.append(Paragraph(f"Target Property Name: {model_info['target_property_name']}", styles['Normal']))
elements.append(Paragraph(f"Target Property Task: {model_info['target_property_task']}", styles['Normal']))
elements.append(Paragraph(f"Feature Calculator: {model_info['feature_calculator']}", styles['Normal']))
elements.append(Paragraph(f"Radius: {model_info['radius']}", styles['Normal']))
elements.append(Paragraph(f"nBits: {model_info['nBits']}", styles['Normal']))
elements.append(Paragraph(f"Algorithm: {model_info['algorithm']}", styles['Normal']))
elements.append(Spacer(1, 12))

Expand Down
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