@@ -101,85 +101,111 @@ def getFeatureMapScore(small_m, large_m, score_mode=FeatMaps.FeatMapScoreMode.Al
101101
102102
103103# This is the main XCOS function
104- def getReverseScores (mols , frags , COS_threshold , writer ):
104+ def getReverseScores (mols , frags , score_threshold , writer ):
105105
106106 for mol in mols :
107-
107+
108108 # Get the bits
109109 compound_bits = getBits (mol )
110110
111111 all_scores = []
112112
113113 for bit in compound_bits :
114+
115+ # Let's remove wildcard atoms
116+ # Removing wildcard atoms does not impact feat score but does lower shape overlay
117+ # For scoring should multiply feat score by number of non-wilcard atoms and use
118+ # all atoms including wildcard for shape overlay
119+ bit_without_wildcard_atoms = Chem .DeleteSubstructs (bit , Chem .MolFromSmarts ('[#0]' ))
120+
121+ # Let's only score bits that have more than one atom (do not count wildcard atoms)
122+ # Get number of bit atoms without wildcard atoms
123+ no_bit_atoms_without_wild_card = bit_without_wildcard_atoms .GetNumAtoms ()
114124
115125 # Get number of bit atoms
116126 no_bit_atoms = bit .GetNumAtoms ()
117127
118- scores = []
119-
120- for frag_mol in frags :
121-
122- # NB reverse SuCOS scoring
123- fm_score = getFeatureMapScore (bit , frag_mol )
124- fm_score = np .clip (fm_score , 0 , 1 )
125- # Change van der Waals radius scale for stricter overlay
126- protrude_dist = rdShapeHelpers .ShapeProtrudeDist (bit , frag_mol , allowReordering = False , vdwScale = 0.2 )
127- protrude_dist = np .clip (protrude_dist , 0 , 1 )
128-
129- # Get frag name for linking to score
130- frag_name = frag_mol .GetProp ('_Name' ).strip ('Mpro-' )
131-
132- # Check if MCS yield > 0 atoms
133- mcs_match = rdFMCS .FindMCS ([bit ,frag_mol ],ringMatchesRingOnly = True ,matchValences = True )
134-
135- # Get number of atoms in MCS match found
136- no_mcs_atoms = Chem .MolFromSmarts (mcs_match .smartsString ).GetNumAtoms ()
137-
138- if no_mcs_atoms == 0 :
139-
140- scores .append ((frag_name , 0 , no_bit_atoms ))
141-
142- if no_mcs_atoms > 0 :
143-
144- # NB reverse SuCOS scoring
145- fm_score = getFeatureMapScore (bit , frag_mol )
146- fm_score = np .clip (fm_score , 0 , 1 )
147-
148- # Change van der Waals radius scale for stricter overlay
149- protrude_dist = rdShapeHelpers .ShapeProtrudeDist (bit , frag_mol ,
150- allowReordering = False ,
151- vdwScale = 0.2 )
152- protrude_dist = np .clip (protrude_dist , 0 , 1 )
153-
154- reverse_SuCOS_score = 0.5 * fm_score + 0.5 * (1 - protrude_dist )
155-
156- scores .append ((frag_name , reverse_SuCOS_score , no_bit_atoms ))
157-
158- all_scores .append (scores )
159-
160- list_dfs = []
161-
162- for score in all_scores :
163-
164- df = pd .DataFrame (data = score , columns = ['Fragment' , 'Score' , 'No_bit_atoms' ])
128+ # Only score if enough info in bit to describe a vector - this will bias against
129+ # cases where frag has long aliphatic chain
130+
131+ if no_bit_atoms_without_wild_card > 1 :
165132
166- # Get maximum scoring fragment for bit match
167- df = df [df ['Score' ] == df ['Score' ].max ()]
168- list_dfs .append (df )
169-
170- final_df = pd .concat (list_dfs )
171-
172- # Score 1: the score is scaled by the number of bit atoms
173- score_1 = (final_df .No_bit_atoms * final_df .Score ).sum ()
174-
175- # Let's only get frags above a threshold
176- final_df = final_df [final_df .Score > COS_threshold ]
177-
178- # Let#s sort the df by increasing score
179- final_df = final_df .sort_values (by = ['Score' ], ascending = False )
180-
181- # Get the unique fragments above threshold
182- all_frags = pd .unique (final_df .Fragment )
133+ scores = []
134+
135+ for frag_mol in frags :
136+
137+ # Get frag name for linking to score
138+ frag_name = frag_mol .GetProp ('_Name' ).strip ('Mpro-' )
139+
140+ # Score only if some common structure shared between bit and fragment.
141+ # Check if MCS yield > 0 atoms
142+ mcs_match = rdFMCS .FindMCS ([bit ,frag_mol ], ringMatchesRingOnly = True , matchValences = True )
143+
144+ # Get mcs_mol from mcs_match
145+ mcs_mol = Chem .MolFromSmarts (mcs_match .smartsString )
146+
147+ # check if frag has MCS mol
148+ mcs_test = frag_mol .HasSubstructMatch (mcs_mol )
149+
150+ if mcs_test :
151+
152+ # Change van der Waals radius scale for stricter overlay
153+ protrude_dist = rdShapeHelpers .ShapeProtrudeDist (bit , frag_mol , allowReordering = False , vdwScale = 0.2 )
154+ protrude_dist = np .clip (protrude_dist , 0 , 1 )
155+
156+ protrude_score = 1 - protrude_dist
157+
158+ # We are comparing small bits relative to large frags
159+ # If overlay poor then assign score of 0
160+ # NB reverse SuCOS scoring. Feat map is also comp
161+ # more expensive
162+
163+ if protrude_score > score_threshold :
164+
165+ fm_score = getFeatureMapScore (bit , frag_mol )
166+ fm_score = np .clip (fm_score , 0 , 1 )
167+
168+ # What about good shape overlay but poor feat match?
169+ # Let's add a cutoff here to prevent good overlays with
170+ # poor feat match - eg. 3 mem ring 2 x C atoms overlay well
171+ # with 2 x aromatic ring Cs
172+
173+ if fm_score > score_threshold :
174+ # Use modified SuCOS score where feat_score scaled by number of bit atoms
175+ # without wildcard atoms and the shape overlay score by the number of bit atoms
176+ # including wildcard atoms
177+ scores .append ((frag_name , protrude_score ,no_bit_atoms ,fm_score ,no_bit_atoms_without_wild_card ))
178+ else :
179+ scores .append ((frag_name ,0 ,no_bit_atoms ,0 ,no_bit_atoms_without_wild_card ))
180+ else :
181+ scores .append ((frag_name ,0 ,no_bit_atoms ,0 ,no_bit_atoms_without_wild_card ))
182+ else :
183+ scores .append ((frag_name ,0 ,no_bit_atoms ,0 ,no_bit_atoms_without_wild_card ))
184+
185+ all_scores .append (scores )
186+
187+ list_dfs = []
188+
189+ for score in all_scores :
190+
191+ df = pd .DataFrame (data = score , columns = ['Fragment' ,'Shape_score' ,'no_bit_atoms' ,'Feat_score' ,'no_bit_atoms_without_wild_card' ])
192+
193+ # Get maximum scoring fragment for bit match
194+ df ['Modified_SuCOS_score' ] = 0.5 * (df .Feat_score * df .no_bit_atoms_without_wild_card ) + 0.5 * (df .Shape_score * df .no_bit_atoms )
195+ df = df [df ['Modified_SuCOS_score' ] == df ['Modified_SuCOS_score' ].max ()]
196+ list_dfs .append (df )
197+
198+ final_df = pd .concat (list_dfs )
199+
200+ # Score 1: the score is scaled by the number of bit atoms
201+ score_1 = final_df .Modified_SuCOS_score .sum ()
202+
203+ # Let's only get frags with a score > 0
204+ #final_df['SuCOS_score'] = 0.5 * final_df.Feat_score + 0.5 * final_df.Shape_score
205+ final_df = final_df [final_df .Modified_SuCOS_score > 0 ]
206+
207+ # Get the unique fragments above threshold
208+ all_frags = pd .unique (final_df .Fragment )
183209
184210 # Add props we want
185211 mol .SetProp (field_XCosRefMols , ',' .join (all_frags ))
@@ -204,8 +230,8 @@ def process(molecules, fragments, writer):
204230 else :
205231 utils .log ('Using' , len (frag_mol_list ), 'fragments. No errors' )
206232
207- #mols, frags, COS_threshold , writer
208- getReverseScores (molecules , frag_mol_list , 0.40 , writer )
233+ #mols, frags, score_threshold , writer
234+ getReverseScores (molecules , frag_mol_list , 0.5 , writer )
209235
210236
211237def main ():
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