Update app.py

app.py

@@ -6,7 +6,7 @@ import textwrap
 from email.mime.multipart import MIMEMultipart
 from email.mime.text import MIMEText
 from email.utils import formatdate, make_msgid
-from functools import cache, partial
+from functools import cache
 from math import pi
 from time import sleep, time
 from uuid import uuid4
@@ -30,14 +30,16 @@ from requests.adapters import HTTPAdapter, Retry
 from markdown import markdown
 from rdkit import Chem
 from rdkit.Chem import AllChem, Draw, RDConfig, PandasTools, Descriptors, rdMolDescriptors, rdmolops, Lipinski, Crippen
+from rdkit.Chem.Features.ShowFeats import _featColors
 from rdkit.Chem.Scaffolds import MurckoScaffold
-import seaborn as sns
+import py3Dmol
 
 from bokeh.models import Legend, NumberFormatter, BooleanFormatter, HTMLTemplateFormatter, LegendItem
 from bokeh.palettes import Category20c_20
 from bokeh.plotting import figure
 from bokeh.transform import cumsum
 from bokeh.resources import INLINE
+import seaborn as sns
 import panel as pn
 
 from apscheduler.schedulers.background import BackgroundScheduler
@@ -158,6 +160,63 @@ visibility: hidden
 """
 
 
+class View3DmolCell(py3Dmol.view):
+    def __init__(self, width=640, height=480):
+        divid = "3dmolviewer_UNIQUEID"
+        self.uniqueid = None
+        if type(width) == int:
+            width = '%dpx'%width
+        if type(height) == int:
+            height = '%dpx'%height
+        self.startjs = '''<div id="%s"  style="position: relative; width: %s; height: %s;">
+            </div>\n''' % (divid, width, height)
+        self.startjs += '<script>\n'
+        self.endjs = '</script>'
+
+        self.updatejs = ''
+        self.viewergrid = None
+
+        self.startjs += 'viewer_UNIQUEID = $3Dmol.createViewer(document.getElementById("%s"),{backgroundColor:"white"});\n' % divid
+        self.startjs += "viewer_UNIQUEID.zoomTo();\n"
+        self.endjs = "viewer_UNIQUEID.render();\n" + self.endjs
+
+
+FEAT_FACTORY = AllChem.BuildFeatureFactory(os.path.join(RDConfig.RDDataDir, 'BaseFeatures.fdef'))
+
+
+def rgb_to_hex(rgb):
+    rgb = tuple(round(i * 255) for i in rgb)
+    return '#{:02x}{:02x}{:02x}'.format(rgb[0], rgb[1], rgb[2])
+
+
+def mol_to_pharm3d(mol, mode='html'):
+    AllChem.Compute2DCoords(mol)
+
+    feat_factory = AllChem.BuildFeatureFactory(os.path.join(RDConfig.RDDataDir, 'BaseFeatures.fdef'))
+    feats = feat_factory.GetFeaturesForMol(mol)
+
+    view = View3DmolCell(width=400, height=400)
+    for feat in feats:
+        pos = feat.GetPos()
+        color = _featColors.get(feat.GetFamily(), (.5, .5, .5))
+        view.addSphere({
+            'center': {'x': pos.x,'y': pos.y,'z': pos.z},
+            'radius': 0.5,
+            'color': rgb_to_hex(color)
+        })
+
+    mol_block = Chem.MolToMolBlock(mol)
+    view.addModel(mol_block, 'sdf')
+    view.setStyle({'stick': {}})
+    view.zoomTo()
+
+    match mode:
+        case 'html':
+            return view.write_html()
+        # case 'png':
+        #     return view.png()
+
+
 class HelpTip:
     def __new__(cls, text):
         return gr.HTML(
@@ -917,7 +976,7 @@ def submit_predict(predict_filepath, task, preset, target_family, opts, job_info
             def max_sim(smiles):
                 return max_tanimoto_similarity(smiles, seen_smiles_with_fp=pos_compounds_df)
 
-            prediction_df[['Max. Tanimoto Similarity to Known Target Ligands', 
+            prediction_df[['Max. Tanimoto Similarity to Known Target Ligands',
                            'Max. Tanimoto Similarity Target Ligand']] = (
                 prediction_df['X1'].parallel_apply(max_sim).apply(pd.Series)
             )
@@ -932,7 +991,7 @@ def submit_predict(predict_filepath, task, preset, target_family, opts, job_info
                 compound_targets = df_training.loc[df_training['X1'] == compound]
                 return max_sequence_identity(x2, seen_fastas=compound_targets)
 
-            prediction_df[['Max. Sequence Identity to Known Ligand Targets', 
+            prediction_df[['Max. Sequence Identity to Known Ligand Targets',
                            'Max. Sequence Identity Ligand Target']] = (
                 prediction_df['X1^'].parallel_apply(calculate_max_sequence_identity).apply(pd.Series)
             )
@@ -1147,6 +1206,10 @@ def create_html_report(df, file=None, task=None, opts=(), progress=gr.Progress(t
                 f'<div style="overflow:auto; height: 300px; font-family: Courier !important;">{table_html}</div>')
 
     else:
+        if 'Exclude Pharmacophore 3D' not in opts:
+            df_html['Pharmacophore'] = df_html['Compound'].parallel_apply(
+                lambda x: mol_to_pharm3d(x, mode='html') if not pd.isna(x) else x)
+
         image_zoom_formatter = HTMLTemplateFormatter(template='<div class="image-zoom-viewer"><%= value %></div>')
         uniprot_id_formatter = HTMLTemplateFormatter(
             template='<% if (value == value) { '  # Check if value is not NaN
@@ -1167,11 +1230,12 @@ def create_html_report(df, file=None, task=None, opts=(), progress=gr.Progress(t
             'Interaction Probability': {'type': 'progress', 'max': 1.0, 'legend': True},
             'Compound': image_zoom_formatter,
             'Scaffold': image_zoom_formatter,
+            'Pharmacophore': {'type': 'executeScriptFormatter'},
             'Target FASTA': {'type': 'textarea', 'width': 60},
             'Target ID': uniprot_id_formatter,
             'Compound ID': pubchem_id_formatter,
-            'Max. Sequence Identity Target': uniprot_id_formatter,
-            'Max. Tanimoto Similarity Compound': pubchem_id_formatter,
+            'Max. Tanimoto Similarity Target Ligand': uniprot_id_formatter,
+            'Max. Sequence Identity Ligand Target': pubchem_id_formatter,
         }
         formatters = {**bool_formatters, **float_formatters, **other_formatters}
 
@@ -1181,7 +1245,7 @@ def create_html_report(df, file=None, task=None, opts=(), progress=gr.Progress(t
         report_table = pn.widgets.Tabulator(
             df_html, formatters=formatters,
             frozen_columns=[col for col in df_html.columns if col in [
-                'Target ID', 'Compound ID', 'Compound', 'Scaffold'
+                'Target ID', 'Compound ID', 'Compound', 'Scaffold', 'Pharmacophore'
             ]],
             disabled=True, sizing_mode='stretch_both', pagination='local', page_size=30)
 
@@ -2215,7 +2279,6 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
                     infer_csv_prompt: gr.Button(visible=False),
                     infer_library_prompt: gr.Button(visible=True),
                 }
-        match upload_type:
             case "Upload a CSV file containing paired compound-protein data":
                 return {
                     pair_upload: gr.Column(visible=True),