updated demo

app.py

@@ -65,6 +65,7 @@ models_enabled = [
     "MHG-GED",
     "MolFormer",
     "SMI-TED",
+    "SMI-SSED",
     "Mordred",
     "MorganFingerprint",
 ]

models/fm4m.py

@@ -31,6 +31,7 @@ sys.path.append("models/")
 from models.selfies_ted.load import SELFIES as bart
 from models.mhg_model import load as mhg
 from models.smi_ted.smi_ted_light.load import load_smi_ted
+from models.smi_ssed.load import load_smi_ssed
 
 import mordred
 from mordred import Calculator, descriptors
@@ -58,13 +59,15 @@ def avail_models_data():
     models = [{"Name": "bart","Model Name": "SELFIES-TED","Description": "BART model for string based SELFIES modality", "Timestamp": "2024-06-21 12:32:20"},
   {"Name": "mol-xl","Model Name": "MolFormer", "Description": "MolFormer model for string based SMILES modality", "Timestamp": "2024-06-21 12:35:56"},
   {"Name": "mhg", "Model Name": "MHG-GED","Description": "Molecular hypergraph model", "Timestamp": "2024-07-10 00:09:42"},
-  {"Name": "smi-ted", "Model Name": "SMI-TED","Description": "SMILES based encoder decoder model", "Timestamp": "2024-07-10 00:09:42"}]
+  {"Name": "smi-ted", "Model Name": "SMI-TED","Description": "SMILES based encoder decoder model", "Timestamp": "2024-07-10 00:09:42"},
+  {"Name": "smi-ssed", "Model Name": "SMI-SSED","Description": "SMILES based encoder decoder model", "Timestamp": "2024-07-10 00:09:42"}]
 
 
 def avail_models(raw=False):
     global models
 
     models = [{"Name": "smi-ted", "Model Name": "SMI-TED","Description": "SMILES based encoder decoder model"},
+              {"Name": "smi-ssed", "Model Name": "SMI-SSED","Description": "SMILES based encoder decoder model"},
               {"Name": "bart","Model Name": "SELFIES-TED","Description": "BART model for string based SELFIES modality"},
               {"Name": "mol-xl","Model Name": "MolFormer", "Description": "MolFormer model for string based SMILES modality"},
               {"Name": "mhg", "Model Name": "MHG-GED","Description": "Molecular hypergraph model"},
@@ -201,7 +204,7 @@ avail_models_data()
 
 
 def get_representation(train_data,test_data,model_type, return_tensor=True):
-    alias = {"MHG-GED": "mhg", "SELFIES-TED": "bart", "MolFormer": "mol-xl", "Molformer": "mol-xl", "SMI-TED": "smi-ted"}
+    alias = {"MHG-GED": "mhg", "SELFIES-TED": "bart", "MolFormer": "mol-xl", "Molformer": "mol-xl", "SMI-TED": "smi-ted", "SMI-SSED": "smi-ssed"}
     if model_type in alias.keys():
         model_type = alias[model_type]
 
@@ -230,6 +233,12 @@ def get_representation(train_data,test_data,model_type, return_tensor=True):
             x_batch = model.encode(train_data, return_torch=return_tensor)
             x_batch_test = model.encode(test_data, return_torch=return_tensor)
 
+    elif model_type == "smi-ssed":
+        model = load_smi_ssed(folder='', ckpt_filename='')
+        with torch.no_grad():
+            x_batch = model.encode(train_data, return_torch=return_tensor)
+            x_batch_test = model.encode(test_data, return_torch=return_tensor)
+
     elif model_type == "mol-xl":
         model = AutoModel.from_pretrained("ibm/MoLFormer-XL-both-10pct", deterministic_eval=True,
                                           trust_remote_code=True)
@@ -305,7 +314,7 @@ def get_representation(train_data,test_data,model_type, return_tensor=True):
 
 def single_modal(model,dataset=None, downstream_model=None, params=None, x_train=None, x_test=None, y_train=None, y_test=None):
     print(model)
-    alias = {"MHG-GED":"mhg", "SELFIES-TED": "bart", "MolFormer":"mol-xl", "Molformer": "mol-xl", "SMI-TED": "smi-ted"}
+    alias = {"MHG-GED":"mhg", "SELFIES-TED": "bart", "MolFormer":"mol-xl", "Molformer": "mol-xl", "SMI-TED": "smi-ted", "SMI-SSED": "smi-ssed"}
     data = avail_models(raw=True)
     df = pd.DataFrame(data)
     #print(list(df["Name"].values))
@@ -334,11 +343,11 @@ def single_modal(model,dataset=None, downstream_model=None, params=None, x_train
         print("Custom Dataset")
         #return
         components = dataset.split(",")
-        train_data = pd.read_csv(components[0])[components[2]]
-        test_data = pd.read_csv(components[1])[components[2]]
+        train_data = pd.read_csv(components[0])[components[2]][:100]
+        test_data = pd.read_csv(components[1])[components[2]][:50]
 
-        y_batch = pd.read_csv(components[0])[components[3]]
-        y_batch_test = pd.read_csv(components[1])[components[3]]
+        y_batch = pd.read_csv(components[0])[components[3]][:100]
+        y_batch_test = pd.read_csv(components[1])[components[3]][:50]
 
 
         x_batch,  x_batch_test = get_representation(train_data,test_data,model_type)
@@ -601,11 +610,11 @@ def multi_modal(model_list,dataset=None, downstream_model=None,params=None, x_tr
     elif x_train==None:
         predefined = False
         components = dataset.split(",")
-        train_data = pd.read_csv(components[0])[components[2]]
-        test_data = pd.read_csv(components[1])[components[2]]
+        train_data = pd.read_csv(components[0])[components[2]][:100]
+        test_data = pd.read_csv(components[1])[components[2]][:50]
 
-        y_batch = pd.read_csv(components[0])[components[3]]
-        y_batch_test = pd.read_csv(components[1])[components[3]]
+        y_batch = pd.read_csv(components[0])[components[3]][:100]
+        y_batch_test = pd.read_csv(components[1])[components[3]][:50]
 
         print("Custom Dataset loaded")
     else:
@@ -619,7 +628,7 @@ def multi_modal(model_list,dataset=None, downstream_model=None,params=None, x_tr
     df = pd.DataFrame(data)
     list(df["Name"].values)
 
-    alias = {"MHG-GED":"mhg", "SELFIES-TED": "bart", "MolFormer":"mol-xl",  "Molformer": "mol-xl","SMI-TED":"smi-ted", "Mordred": "Mordred", "MorganFingerprint": "MorganFingerprint"}
+    alias = {"MHG-GED":"mhg", "SELFIES-TED": "bart", "MolFormer":"mol-xl",  "Molformer": "mol-xl","SMI-TED":"smi-ted","SMI-SSED":"smi-ssed", "Mordred": "Mordred", "MorganFingerprint": "MorganFingerprint"}
     #if set(model_list).issubset(list(df["Name"].values)):
     if set(model_list).issubset(list(alias.keys())):
         for i, model in enumerate(model_list):

models/smi_ssed/bert_vocab_curated.txt

@@ -0,0 +1,2393 @@
+<bos>
+<eos>
+<pad>
+<mask>
+C
+c
+(
+)
+1
+O
+N
+2
+=
+n
+3
+[C@H]
+[C@@H]
+F
+S
+4
+Cl
+-
+o
+s
+[nH]
+#
+/
+Br
+[C@]
+[C@@]
+[N+]
+[O-]
+5
+\
+.
+I
+6
+[S@]
+[S@@]
+P
+[N-]
+[Si]
+7
+[n+]
+[2H]
+8
+[NH+]
+B
+9
+[C-]
+[Na+]
+[Cl-]
+[c-]
+[CH]
+%10
+[NH2+]
+[P+]
+[B]
+[I-]
+%11
+[CH2-]
+[O+]
+[NH3+]
+[C]
+[Br-]
+[IH2]
+[S-]
+[cH-]
+%12
+[nH+]
+[B-]
+[K+]
+[Sn]
+[Se]
+[CH-]
+[HH]
+[Y]
+[n-]
+[CH3-]
+[SiH]
+[S+]
+%13
+[SiH2]
+[Li+]
+[NH-]
+%14
+[Na]
+[CH2]
+[O-2]
+[U+2]
+[W]
+[Al]
+[P@]
+[Fe+2]
+[PH+]
+%15
+[Cl+3]
+[Zn+2]
+[Ir]
+[Mg+2]
+[Pt+2]
+[OH2+]
+[As]
+[Fe]
+[OH+]
+[Zr+2]
+[3H]
+[Ge]
+[SiH3]
+[OH-]
+[NH4+]
+[Cu+2]
+[P@@]
+p
+[Pt]
+%16
+[Ca+2]
+[Zr]
+[F-]
+[C+]
+[Ti]
+[P-]
+[V]
+[se]
+[U]
+[O]
+[Ni+2]
+[Zn]
+[Co]
+[Ni]
+[Pd+2]
+[Cu]
+%17
+[Cu+]
+[Te]
+[H+]
+[CH+]
+[Li]
+[Pd]
+[Mo]
+[Ru+2]
+[o+]
+[Re]
+[SH+]
+%18
+[Ac]
+[Cr]
+[NH2-]
+[K]
+[13CH2]
+[c]
+[Zr+4]
+[Tl]
+[13C]
+[Mn]
+[N@+]
+[Hg]
+[Rh]
+[Ti+4]
+[Sb]
+[Co+2]
+[Ag+]
+[Ru]
+%19
+[N@@+]
+[Ti+2]
+[Al+3]
+[Pb]
+[I+]
+[18F]
+[s+]
+[Rb+]
+[Ba+2]
+[H-]
+[Fe+3]
+[Ir+3]
+[13cH]
+%20
+[AlH2]
+[Au+]
+[13c]
+[SH2+]
+[Sn+2]
+[Mn+2]
+[Si-]
+[Ag]
+[N]
+[Bi]
+%21
+[In]
+[CH2+]
+[Y+3]
+[Ga]
+%22
+[Co+3]
+[Au]
+[13CH3]
+[Mg]
+[Cs+]
+[W+2]
+[Hf]
+[Zn+]
+[Se-]
+[S-2]
+[Ca]
+[pH]
+[ClH+]
+[Ti+3]
+%23
+[Ru+]
+[SH-]
+[13CH]
+[IH+]
+[Hf+4]
+[Rf]
+[OH3+]
+%24
+[Pt+4]
+[Zr+3]
+[PH3+]
+[Sr+2]
+[Cd+2]
+[Cd]
+%25
+[Os]
+[BH-]
+[Sn+4]
+[Cr+3]
+[Ru+3]
+[PH2+]
+[Rh+2]
+[V+2]
+%26
+[Gd+3]
+[Pb+2]
+[PH]
+[Hg+]
+[Mo+2]
+[AlH]
+[Sn+]
+%27
+[Pd+]
+b
+[Rh+3]
+[Hg+2]
+[15NH]
+[14C]
+%28
+[Mn+3]
+[Si+]
+[SeH]
+[13C@H]
+[NH]
+[Ga+3]
+[SiH-]
+[13C@@H]
+[Ce]
+[Au+3]
+[Bi+3]
+[15N]
+%29
+[BH3-]
+[14cH]
+[Ti+]
+[Gd]
+[cH+]
+[Cr+2]
+[Sb-]
+%30
+[Be+2]
+[Al+]
+[te]
+[11CH3]
+[Sm]
+[Pr]
+[La]
+%31
+[Al-]
+[Ta]
+[125I]
+[BH2-]
+[Nb]
+[Si@]
+%32
+[14c]
+[Sb+3]
+[Ba]
+%33
+[Os+2]
+[Si@@]
+[La+3]
+[15n]
+[15NH2]
+[Nd+3]
+%34
+[14CH2]
+[18O]
+[Nd]
+[GeH]
+[Ni+3]
+[Eu]
+[Dy+3]
+[Sc]
+%36
+[Se-2]
+[As+]
+%35
+[AsH]
+[Tb]
+[Sb+5]
+[Se+]
+[Ce+3]
+[c+]
+[In+3]
+[SnH]
+[Mo+4]
+%37
+[V+4]
+[Eu+3]
+[Hf+2]
+%38
+[Pt+]
+[p+]
+[123I]
+[Tl+]
+[Sm+3]
+%39
+[Yb+3]
+%40
+[Yb]
+[Os+]
+%41
+[10B]
+[Sc+3]
+[Al+2]
+%42
+[Sr]
+[Tb+3]
+[Po]
+[Tc]
+[PH-]
+[AlH3]
+[Ar]
+[U+4]
+[SnH2]
+[Cl+2]
+[si]
+[Fe+]
+[14CH3]
+[U+3]
+[Cl+]
+%43
+[GeH2]
+%44
+[Er+3]
+[Mo+3]
+[I+2]
+[Fe+4]
+[99Tc]
+%45
+[11C]
+%46
+[SnH3]
+[S]
+[Te+]
+[Er]
+[Lu+3]
+[11B]
+%47
+%48
+[P]
+[Tm]
+[Th]
+[Dy]
+[Pr+3]
+[Ta+5]
+[Nb+5]
+[Rb]
+[GeH3]
+[Br+2]
+%49
+[131I]
+[Fm]
+[Cs]
+[BH4-]
+[Lu]
+[15nH]
+%50
+[Ru+6]
+[b-]
+[Ho]
+[Th+4]
+[Ru+4]
+%52
+[14CH]
+%51
+[Cr+6]
+[18OH]
+[Ho+3]
+[Ce+4]
+[Bi+2]
+[Co+]
+%53
+[Yb+2]
+[Fe+6]
+[Be]
+%54
+[SH3+]
+[Np]
+[As-]
+%55
+[14C@@H]
+[Ir+2]
+[GaH3]
+[p-]
+[GeH4]
+[Sn+3]
+[Os+4]
+%56
+[14C@H]
+[sH+]
+[19F]
+[Eu+2]
+[TlH]
+%57
+[Cr+4]
+%58
+[B@@-]
+[SiH+]
+[At]
+[Am]
+[Fe+5]
+[AsH2]
+[Si+4]
+[B@-]
+[Pu]
+[SbH]
+[P-2]
+[Tm+3]
+*
+%59
+[se+]
+[IH-]
+%60
+[oH+]
+[1H]
+[15N+]
+[124I]
+[S@@+]
+[P-3]
+[H]
+[IH2+]
+[TeH]
+[Xe]
+[PH4+]
+[Cr+]
+[Cm]
+[I+3]
+%61
+[Nb+2]
+[Ru+5]
+%62
+[Ta+2]
+[Tc+4]
+[CH3+]
+[Pm]
+[Si@H]
+[No]
+%63
+[Cr+5]
+[Th+2]
+[Zn-2]
+[13C@]
+[Lr]
+%64
+[99Tc+3]
+%65
+[13C@@]
+%66
+[Fe-]
+[17O]
+[siH]
+[Sb+]
+[OH]
+[IH]
+[11CH2]
+[Cf]
+[SiH2+]
+[Gd+2]
+[In+]
+[Si@@H]
+[Mn+]
+[99Tc+4]
+[Ga-]
+%67
+[S@+]
+[Ge+4]
+[Tl+3]
+[16OH]
+%68
+[2H-]
+[Ra]
+[si-]
+[NiH2]
+[P@@H]
+[Rh+]
+[12C]
+[35S]
+[32P]
+[SiH2-]
+[AlH2+]
+[16O]
+%69
+[BiH]
+[BiH2]
+[Zn-]
+[BH]
+[Tc+3]
+[Ir+]
+[Ni+]
+%70
+[InH2]
+[InH]
+[Nb+3]
+[PbH]
+[Bi+]
+%71
+[As+3]
+%72
+[18O-]
+[68Ga+3]
+%73
+[Pa]
+[76Br]
+[Tc+5]
+[pH+]
+[64Cu+2]
+[Ru+8]
+%74
+[PH2-]
+[Si+2]
+[17OH]
+[RuH]
+[111In+3]
+[AlH+]
+%75
+%76
+[W+]
+[SbH2]
+[PoH]
+[Ru-]
+[XeH]
+[Tc+2]
+[13C-]
+[Br+]
+[Pt-2]
+[Es]
+[Cu-]
+[Mg+]
+[3HH]
+[P@H]
+[ClH2+]
+%77
+[SH]
+[Au-]
+[2HH]
+%78
+[Sn-]
+[11CH]
+[PdH2]
+0
+[Os+6]
+%79
+[Mo+]
+%80
+[al]
+[PbH2]
+[64Cu]
+[Cl]
+[12CH3]
+%81
+[Tc+7]
+[11c]
+%82
+[Li-]
+[99Tc+5]
+[He]
+[12c]
+[Kr]
+[RuH+2]
+[35Cl]
+[Pd-2]
+[GaH2]
+[4H]
+[Sg]
+[Cu-2]
+[Br+3]
+%83
+[37Cl]
+[211At]
+[IrH+2]
+[Mt]
+[Ir-2]
+[In-]
+[12cH]
+[12CH2]
+[RuH2]
+[99Tc+7]
+%84
+[15n+]
+[ClH2+2]
+[16N]
+[111In]
+[Tc+]
+[Ru-2]
+[12CH]
+[si+]
+[Tc+6]
+%85
+%86
+[90Y]
+[Pd-]
+[188Re]
+[RuH+]
+[NiH]
+[SiH3-]
+[14n]
+[CH3]
+[14N]
+[10BH2]
+%88
+%89
+%90
+[34S]
+[77Br]
+[GaH]
+[Br]
+[Ge@]
+[B@@H-]
+[CuH]
+[SiH4]
+[3H-]
+%87
+%91
+%92
+[67Cu]
+[I]
+[177Lu]
+[ReH]
+[67Ga+3]
+[Db]
+[177Lu+3]
+[AlH2-]
+[Si+3]
+[Ti-2]
+[RuH+3]
+[al+]
+[68Ga]
+[2H+]
+[B@H-]
+[WH2]
+[OsH]
+[Ir-3]
+[AlH-]
+[Bk]
+[75Se]
+[14C@]
+[Pt-]
+[N@@H+]
+[Nb-]
+[13NH2]
+%93
+[186Re]
+[Tb+4]
+[PtH]
+[IrH2]
+[Hg-2]
+[AlH3-]
+[PdH+]
+[Md]
+[RhH+2]
+[11cH]
+[Co-2]
+[15N-]
+[ZrH2]
+%94
+[Hg-]
+[127I]
+[AsH2+]
+[MoH2]
+[Te+4]
+[14C@@]
+[As+5]
+[SnH+3]
+[Ge@@]
+[6Li+]
+[WH]
+[Ne]
+[14NH2]
+[14NH]
+[12C@@H]
+[Os+7]
+[RhH]
+[Al-3]
+[SnH+]
+[15NH3+]
+[Zr+]
+[197Hg+]
+%95
+%96
+[90Y+3]
+[Os-2]
+[98Tc+5]
+[15NH3]
+[bH-]
+[33P]
+[Zr-2]
+[15O]
+[Rh-]
+[PbH3]
+[PH2]
+[Ni-]
+[CuH+]
+%97
+%98
+%99
+[Os+5]
+[PtH+]
+[ReH4]
+[16NH]
+[82Br]
+[W-]
+[18F-]
+[15NH4+]
+[Se+4]
+[SeH-]
+[SH4]
+[67Cu+2]
+[12C@H]
+[AsH3]
+[HgH]
+[10B-]
+[99Tc+6]
+[117Sn+4]
+[Te@]
+[P@+]
+[35SH]
+[SeH+]
+[Ni-2]
+[Al-2]
+[TeH2]
+[Bh]
+[99Tc+2]
+[Os+8]
+[PH-2]
+[7Li+]
+[14nH]
+[AlH+2]
+[18FH]
+[SnH4]
+[18O-2]
+[IrH]
+[13N]
+[Te@@]
+[Rh-3]
+[15NH+]
+[AsH3+]
+[SeH2]
+[AsH+]
+[CoH2]
+[16NH2]
+[AsH-]
+[203Hg+]
+[P@@+]
+[166Ho+3]
+[60Co+3]
+[13CH2-]
+[SeH2+]
+[75Br]
+[TlH2]
+[80Br]
+[siH+]
+[Ca+]
+[153Sm+3]
+[PdH]
+[225Ac]
+[13CH3-]
+[AlH4-]
+[FeH]
+[13CH-]
+[14C-]
+[11C-]
+[153Sm]
+[Re-]
+[te+]
+[13CH4]
+[ClH+2]
+[8CH2]
+[99Mo]
+[ClH3+3]
+[SbH3]
+[25Mg+2]
+[16N+]
+[SnH2+]
+[PH4]
+[11C@H]
+[122I]
+[Re-2]
+[RuH2+2]
+[ZrH]
+[Bi-]
+[Pr+]
+[Rn]
+[Fr]
+[36Cl]
+[18o]
+[YH]
+[79Br]
+[121I]
+[113In+3]
+[InH4-]
+[TaH]
+[RhH2]
+[Ta-]
+[67Ga]
+[ZnH+]
+[SnH2-]
+[OsH2]
+[16F]
+[FeH2]
+[14O]
+[PbH2+2]
+[BH2]
+[6H]
+[125Te]
+[197Hg]
+[TaH2]
+[TaH3]
+[76As]
+[Nb-2]
+[14N+]
+[125I-]
+[33S]
+[IH2+2]
+[NH2]
+[PtH2]
+[MnH]
+[19C]
+[17F]
+[1H-]
+[SnH4+2]
+[Mn-2]
+[15NH2+]
+[TiH2]
+[ReH7]
+[Cd-2]
+[Fe-3]
+[SH2]
+[17O-]
+[siH-]
+[CoH+]
+[VH]
+[10BH]
+[Ru-3]
+[13O]
+[5H]
+[CoH]
+[PH5]
+[15n-]
+[153Gd]
+[12C@]
+[11CH3-]
+[IrH3]
+[RuH3]
+[74Se]
+[Se@]
+[Hf+]
+[77Se]
+[166Ho]
+[59Fe+2]
+[203Hg]
+[18OH-]
+[8CH]
+[12C@@]
+[11CH4]
+[15C]
+[249Cf]
+[PbH4]
+[64Zn]
+[PH3]
+[99Tc+]
+[14c-]
+[149Pm]
+[IrH4]
+[Se@@]
+[13OH]
+[14CH3-]
+[28Si]
+[Rh-2]
+[Fe-2]
+[131I-]
+[51Cr]
+[62Cu+2]
+[81Br]
+[121Sb]
+[7Li]
+[89Zr+4]
+[SbH3+]
+[11C@@H]
+[98Tc]
+[59Fe+3]
+[BiH2+]
+[SbH+]
+[TiH]
+[14NH3]
+[15OH]
+[119Sn]
+[201Hg]
+[MnH+]
+[201Tl]
+[51Cr+3]
+[123I-]
+[MoH]
+[AlH6-3]
+[MnH2]
+[WH3]
+[213Bi+3]
+[SnH2+2]
+[123IH]
+[13CH+]
+[Zr-]
+[74As]
+[13C+]
+[32P+]
+[KrH]
+[SiH+2]
+[ClH3+2]
+[13NH]
+[9CH2]
+[ZrH2+2]
+[87Sr+2]
+[35s]
+[239Pu]
+[198Au]
+[241Am]
+[203Hg+2]
+[V+]
+[YH2]
+[SH5]
+[195Pt]
+[203Pb]
+[RuH4]
+[ThH2]
+[AuH]
+[66Ga+3]
+[11B-]
+[F]
+[24Na+]
+[85Sr+2]
+[201Tl+]
+[14CH4]
+[32S]
+[TeH2+]
+[ClH2+3]
+[AgH]
+[Ge@H]
+[44Ca+2]
+[Os-]
+[31P]
+[15nH+]
+[SbH4]
+[TiH+]
+[Ba+]
+[57Co+2]
+[Ta+]
+[125IH]
+[77As]
+[129I]
+[Fe-4]
+[Ta-2]
+[19O]
+[12O]
+[BiH3]
+[237Np]
+[252Cf]
+[86Y]
+[Cr-2]
+[89Y]
+[195Pt+2]
+[si+2]
+[58Fe+2]
+[Hs]
+[S@@H]
+[OsH6]
+[GdH2]
+[IH3]
+[8CH4]
+[164Dy+3]
+[47Ca+2]
+[57Co]
+[NbH2]
+[ReH2]
+[ZnH2]
+[CrH2]
+[17NH]
+[ZrH3]
+[RhH3]
+[12C-]
+[18O+]
+[Bi-2]
+[ClH4+3]
+[Ni-3]
+[Ag-]
+[111In-]
+[Mo-2]
+[55Fe+3]
+[204Hg+]
+[35Cl-]
+[211Pb]
+[75Ge]
+[8B]
+[TeH3]
+[SnH3+]
+[Zr-3]
+[28F]
+[249Bk]
+[169Yb]
+[34SH]
+[6Li]
+[94Tc]
+[197Au]
+[195Pt+4]
+[169Yb+3]
+[32Cl]
+[82Se]
+[159Gd+3]
+[213Bi]
+[CoH+2]
+[36S]
+[35P]
+[Ru-4]
+[Cr-3]
+[60Co]
+[1H+]
+[18CH2]
+[Cd-]
+[152Sm+3]
+[106Ru]
+[238Pu]
+[220Rn]
+[45Ca+2]
+[89Sr+2]
+[239Np]
+[90Sr+2]
+[137Cs+]
+[165Dy]
+[68GaH3]
+[65Zn+2]
+[89Zr]
+[BiH2+2]
+[62Cu]
+[165Dy+3]
+[238U]
+[105Rh+3]
+[70Zn]
+[12B]
+[12OH]
+[18CH]
+[17CH]
+[OsH3]
+[SbH-]
+[SH6]
+[AlH2-2]
+[42K]
+[76Br-]
+[71As]
+[NbH3]
+[ReH3]
+[OsH-]
+[WH4]
+[MoH3]
+[OsH4]
+[RuH6]
+[PtH3]
+[CuH2]
+[CoH3]
+[TiH4]
+[64Zn+2]
+[Si-2]
+[79BrH]
+[14CH2-]
+[PtH2+2]
+[Os-3]
+[29Si]
+[Ti-]
+[Se+6]
+[22Na+]
+[42K+]
+[131Cs+]
+[86Rb+]
+[134Cs+]
+[209Po]
+[208Po]
+[81Rb+]
+[203Tl+]
+[Zr-4]
+[148Sm]
+[147Sm]
+[37Cl-]
+[12CH4]
+[Ge@@H]
+[63Cu]
+[13CH2+]
+[AsH2-]
+[CeH]
+[SnH-]
+[UH]
+[9c]
+[21CH3]
+[TeH+]
+[57Co+3]
+[8BH2]
+[12BH2]
+[19BH2]
+[9BH2]
+[YbH2]
+[CrH+2]
+[208Bi]
+[152Gd]
+[61Cu]
+[115In]
+[60Co+2]
+[13NH2-]
+[120I]
+[18OH2]
+[75SeH]
+[SbH2+]
+[144Ce]
+[16n]
+[113In]
+[22nH]
+[129I-]
+[InH3]
+[32PH3]
+[234U]
+[235U]
+[59Fe]
+[82Rb+]
+[65Zn]
+[244Cm]
+[147Pm]
+[91Y]
+[237Pu]
+[231Pa]
+[253Cf]
+[127Te]
+[187Re]
+[236Np]
+[235Np]
+[72Zn]
+[253Es]
+[159Dy]
+[62Zn]
+[101Tc]
+[149Tb]
+[124I-]
+[SeH3+]
+[210Pb]
+[40K]
+[210Po]
+[214Pb]
+[218Po]
+[214Po]
+[7Be]
+[212Pb]
+[205Pb]
+[209Pb]
+[123Te]
+[202Pb]
+[72As]
+[201Pb]
+[70As]
+[73Ge]
+[200Pb]
+[198Pb]
+[66Ga]
+[73Se]
+[195Pb]
+[199Pb]
+[144Ce+3]
+[235U+2]
+[90Tc]
+[114In+3]
+[128I]
+[100Tc+]
+[82Br-]
+[191Pt+2]
+[191Pt+4]
+[193Pt+4]
+[31PH3]
+[125I+2]
+[131I+2]
+[125Te+4]
+[82Sr+2]
+[149Sm]
+[81BrH]
+[129Xe]
+[193Pt+2]
+[123I+2]
+[Cr-]
+[Co-]
+[227Th+4]
+[249Cf+3]
+[252Cf+3]
+[187Os]
+[16O-]
+[17O+]
+[16OH-]
+[98Tc+7]
+[58Co+2]
+[69Ga+3]
+[57Fe+2]
+[43K+]
+[16C]
+[52Fe+3]
+[SeH5]
+[194Pb]
+[196Pb]
+[197Pb]
+[213Pb]
+[9B]
+[19B]
+[11CH-]
+[9CH]
+[20OH]
+[25OH]
+[8cH]
+[TiH+3]
+[SnH6+3]
+[N@H+]
+[ZnH]
+[VH3]
+[52Mn+2]
+[64Ga]
+[13B]
+[216Bi]
+[117Sn+2]
+[232Th]
+[SnH+2]
+[BiH5]
+[77Kr]
+[103Cd]
+[62Ni]
+[LaH3]
+[SmH3]
+[EuH3]
+[MoH5]
+[64Ni]
+[66Zn]
+[68Zn]
+[186W]
+[FeH4]
+[MoH4]
+[HgH2]
+[15NH2-]
+[UH2]
+[204Hg]
+[GaH4-]
+[ThH4]
+[WH6]
+[PtH4]
+[VH2]
+[UH3]
+[FeH3]
+[RuH5]
+[BiH4]
+[80Br-]
+[CeH3]
+[37ClH]
+[157Gd+3]
+[205Tl]
+[203Tl]
+[62Cu+]
+[64Cu+]
+[61Cu+]
+[37SH2]
+[30Si]
+[28Al]
+[19OH2]
+[8He]
+[6He]
+[153Pm]
+[209Bi]
+[66Zn+2]
+[10CH4]
+[191Ir]
+[66Cu]
+[16O+]
+[25O]
+[10c]
+[Co-3]
+[Sn@@]
+[17OH-]
+[206Po]
+[204Po]
+[202Po]
+[201Po]
+[200Po]
+[199Po]
+[198Po]
+[197Po]
+[196Po]
+[195Po]
+[194Po]
+[193Po]
+[192Po]
+[191Po]
+[190Po]
+[217Po]
+[BiH4-]
+[TeH4]
+[222Ra]
+[62Ga]
+[39Ar]
+[144Sm]
+[58Fe]
+[153Eu]
+[85Rb]
+[171Yb]
+[172Yb]
+[114Cd]
+[51Fe]
+[142Ce]
+[207Tl]
+[92Mo]
+[115Sn]
+[140Ce]
+[202Hg]
+[180W]
+[182W]
+[183W]
+[184W]
+[96Mo]
+[47Ti]
+[111Cd]
+[143Nd]
+[145Nd]
+[126Te]
+[128Te]
+[130Te]
+[185Re]
+[97Mo]
+[98Mo]
+[183Re]
+[52V]
+[80Se]
+[87Kr]
+[137Xe]
+[196Au]
+[146Ce]
+[88Kr]
+[51Ti]
+[138Xe]
+[112Cd]
+[116Sn]
+[120Sn]
+[28SiH3]
+[35S-]
+[15NH-]
+[13CH3+]
+[34S+]
+[34s]
+[SiH4-]
+[100Tc+5]
+[NiH2+2]
+[239Th]
+[186Lu]
+[AuH3]
+[I@@-]
+[XeH2]
+[B+]
+[16CH2]
+[8C]
+[TaH5]
+[FeH4-]
+[19C@H]
+[10NH]
+[FeH6-3]
+[22CH]
+[25N]
+[25N+]
+[25N-]
+[21CH2]
+[18cH]
+[113I]
+[ScH3]
+[30PH3]
+[43Ca+2]
+[41Ca+2]
+[106Cd]
+[122Sn]
+[18CH3]
+[58Co+3]
+[98Tc+4]
+[70Ge]
+[76Ge]
+[108Cd]
+[116Cd]
+[130Xe]
+[94Mo]
+[124Sn]
+[186Os]
+[188Os]
+[190Os]
+[192Os]
+[106Pd]
+[110Pd]
+[120Te]
+[132Ba]
+[134Ba]
+[136Ba]
+[136Ce]
+[138Ce]
+[156Dy]
+[158Dy]
+[160Dy]
+[163Dy]
+[162Er]
+[164Er]
+[167Er]
+[176Hf]
+[26Mg]
+[144Nd]
+[150Nd]
+[41K]
+[46Ti]
+[48Ti]
+[49Ti]
+[50Ti]
+[170Yb]
+[173Yb]
+[91Zr]
+[92Zr]
+[96Zr]
+[34S-]
+[CuH2-]
+[38Cl]
+[25Mg]
+[51V]
+[93Nb]
+[95Mo]
+[45Sc]
+[123Sb]
+[139La]
+[9Be]
+[99Y+3]
+[99Y]
+[156Ho]
+[67Zn]
+[144Ce+4]
+[210Tl]
+[42Ca]
+[54Fe]
+[193Ir]
+[92Nb]
+[141Cs]
+[52Cr]
+[35ClH]
+[46Ca]
+[139Cs]
+[65Cu]
+[71Ga]
+[60Ni]
+[16NH3]
+[148Nd]
+[72Ge]
+[161Dy]
+[49Ca]
+[43Ca]
+[8Be]
+[48Ca]
+[44Ca]
+[120Xe]
+[80Rb]
+[215At]
+[180Re]
+[146Sm]
+[19Ne]
+[74Kr]
+[134La]
+[76Kr]
+[219Fr]
+[121Xe]
+[220Fr]
+[216At]
+[223Ac]
+[218At]
+[37Ar]
+[135I]
+[110Cd]
+[94Tc+7]
+[86Y+3]
+[135I-]
+[15O-2]
+[151Eu+3]
+[161Tb+3]
+[197Hg+2]
+[109Cd+2]
+[191Os+4]
+[170Tm+3]
+[205Bi+3]
+[233U+4]
+[126Sb+3]
+[127Sb+3]
+[132Cs+]
+[136Eu+3]
+[136Eu]
+[125Sn+4]
+[175Yb+3]
+[100Mo]
+[22Ne]
+[13c-]
+[13NH4+]
+[17C]
+[9C]
+[31S]
+[31SH]
+[133I]
+[126I]
+[36SH]
+[30S]
+[32SH]
+[19CH2]
+[19c]
+[18c]
+[15F]
+[10C]
+[RuH-]
+[62Zn+2]
+[32ClH]
+[33ClH]
+[78BrH]
+[12Li+]
+[12Li]
+[233Ra]
+[68Ge+4]
+[44Sc+3]
+[91Y+3]
+[106Ru+3]
+[PoH2]
+[AtH]
+[55Fe]
+[233U]
+[210PoH2]
+[230Th]
+[228Th]
+[222Rn]
+[35SH2]
+[227Th]
+[192Ir]
+[133Xe]
+[81Kr]
+[95Zr]
+[240Pu]
+[54Mn]
+[103Ru]
+[95Nb]
+[109Cd]
+[141Ce]
+[85Kr]
+[110Ag]
+[58Co]
+[241Pu]
+[234Th]
+[140La]
+[63Ni]
+[152Eu]
+[132IH]
+[226Rn]
+[154Eu]
+[36ClH]
+[228Ac]
+[155Eu]
+[106Rh]
+[243Am]
+[227Ac]
+[243Cm]
+[236U]
+[144Pr]
+[232U]
+[32SH2]
+[88Y]
+[82BrH]
+[135IH]
+[242Cm]
+[115Cd]
+[242Pu]
+[46Sc]
+[56Mn]
+[234Pa]
+[41Ar]
+[147Nd]
+[187W]
+[151Sm]
+[59Ni]
+[233Pa]
+[52Mn]
+[94Nb]
+[219Rn]
+[236Pu]
+[13NH3]
+[93Zr]
+[51Cr+6]
+[TlH3]
+[123Xe]
+[160Tb]
+[170Tm]
+[182Ta]
+[175Yb]
+[93Mo]
+[143Ce]
+[191Os]
+[126IH]
+[48V]
+[113Cd]
+[47Sc]
+[181Hf]
+[185W]
+[143Pr]
+[191Pt]
+[181W]
+[33PH3]
+[97Ru]
+[97Tc]
+[111Ag]
+[169Er]
+[107Pd]
+[103Ru+2]
+[34SH2]
+[137Ce]
+[242Am]
+[117SnH2]
+[57Ni]
+[239U]
+[60Cu]
+[250Cf]
+[193Au]
+[69Zn]
+[55Co]
+[139Ce]
+[127Xe]
+[159Gd]
+[56Co]
+[177Hf]
+[244Pu]
+[38ClH]
+[142Pr]
+[199Hg]
+[179Hf]
+[178Hf]
+[237U]
+[156Eu]
+[157Eu]
+[105Ru]
+[171Tm]
+[199Au]
+[155Sm]
+[80BrH]
+[108Ag]
+[128IH]
+[48Sc]
+[45Ti]
+[176Lu]
+[121SnH2]
+[148Pm]
+[57Fe]
+[10BH3]
+[96Tc]
+[133IH]
+[143Pm]
+[105Rh]
+[130IH]
+[134IH]
+[131IH]
+[71Zn]
+[105Ag]
+[97Zr]
+[235Pu]
+[231Th]
+[109Pd]
+[93Y]
+[190Ir]
+[135Xe]
+[53Mn]
+[134Ce]
+[234Np]
+[240Am]
+[246Cf]
+[240Cm]
+[241Cm]
+[226Th]
+[39ClH]
+[229Th]
+[245Cm]
+[240U]
+[240Np]
+[249Cm]
+[243Pu]
+[145Pm]
+[199Pt]
+[246Bk]
+[193Pt]
+[230U]
+[250Cm]
+[44Ti]
+[175Hf]
+[254Fm]
+[255Fm]
+[257Fm]
+[92Y]
+[188Ir]
+[171Lu]
+[257Md]
+[247Bk]
+[121IH]
+[250Bk]
+[179Lu]
+[224Ac]
+[195Hg]
+[244Am]
+[246Pu]
+[194Au]
+[252Fm]
+[173Hf]
+[246Cm]
+[135Ce]
+[49Cr]
+[248Cf]
+[247Cm]
+[248Cm]
+[174Ta]
+[176Ta]
+[154Tb]
+[172Ta]
+[177Ta]
+[175Ta]
+[180Ta]
+[158Tb]
+[115Ag]
+[189Os]
+[251Cf]
+[145Pr]
+[147Pr]
+[76BrH]
+[102Rh]
+[238Np]
+[185Os]
+[246Am]
+[233Np]
+[166Dy]
+[254Es]
+[244Cf]
+[193Os]
+[245Am]
+[245Bk]
+[239Am]
+[238Am]
+[97Nb]
+[245Pu]
+[254Cf]
+[188W]
+[250Es]
+[251Es]
+[237Am]
+[182Hf]
+[258Md]
+[232Np]
+[238Cm]
+[60Fe]
+[109Pd+2]
+[234Pu]
+[141Ce+3]
+[136Nd]
+[136Pr]
+[173Ta]
+[110Ru]
+[147Tb]
+[253Fm]
+[139Nd]
+[178Re]
+[177Re]
+[200Au]
+[182Re]
+[156Tb]
+[155Tb]
+[157Tb]
+[161Tb]
+[161Ho]
+[167Tm]
+[173Lu]
+[179Ta]
+[171Er]
+[44Sc]
+[49Sc]
+[49V]
+[51Mn]
+[90Nb]
+[88Nb]
+[88Zr]
+[36SH2]
+[174Yb]
+[178Lu]
+[179W]
+[83BrH]
+[107Cd]
+[75BrH]
+[62Co]
+[48Cr]
+[63Zn]
+[102Ag]
+[154Sm]
+[168Er]
+[65Ni]
+[137La]
+[187Ir]
+[144Pm]
+[146Pm]
+[160Gd]
+[166Yb]
+[162Dy]
+[47V]
+[141Nd]
+[141Sm]
+[166Er]
+[150Sm]
+[146Eu]
+[149Eu]
+[174Lu]
+[17NH3]
+[102Ru]
+[170Hf]
+[188Pt]
+[61Ni]
+[56Ni]
+[149Gd]
+[151Gd]
+[141Pm]
+[147Gd]
+[146Gd]
+[161Er]
+[103Ag]
+[145Eu]
+[153Tb]
+[155Dy]
+[184Re]
+[180Os]
+[182Os]
+[186Pt]
+[181Os]
+[181Re]
+[151Tb]
+[178Ta]
+[178W]
+[189Pt]
+[194Hg]
+[145Sm]
+[150Tb]
+[132La]
+[158Gd]
+[104Ag]
+[193Hg]
+[94Ru]
+[137Pr]
+[155Ho]
+[117Cd]
+[99Ru]
+[146Nd]
+[218Rn]
+[95Y]
+[79Kr]
+[120IH]
+[138Pr]
+[100Pd]
+[166Tm]
+[90Mo]
+[151Nd]
+[231U]
+[138Nd]
+[89Nb]
+[98Nb]
+[162Ho]
+[142Sm]
+[186Ta]
+[104Tc]
+[184Ta]
+[185Ta]
+[170Er]
+[107Rh]
+[131La]
+[169Lu]
+[74BrH]
+[150Pm]
+[172Tm]
+[197Pt]
+[230Pu]
+[170Lu]
+[86Zr]
+[176W]
+[177W]
+[101Pd]
+[105Pd]
+[108Pd]
+[149Nd]
+[164Ho]
+[159Ho]
+[167Ho]
+[176Yb]
+[156Sm]
+[77BrH]
+[189Re]
+[99Rh]
+[100Rh]
+[151Pm]
+[232Pa]
+[228Pa]
+[230Pa]
+[66Ni]
+[194Os]
+[135La]
+[138La]
+[141La]
+[142La]
+[195Ir]
+[96Nb]
+[157Ho]
+[183Hf]
+[162Tm]
+[172Er]
+[148Eu]
+[150Eu]
+[15CH4]
+[89Kr]
+[143La]
+[58Ni]
+[61Co]
+[158Eu]
+[165Er]
+[167Yb]
+[173Tm]
+[175Tm]
+[172Hf]
+[172Lu]
+[93Tc]
+[177Yb]
+[124IH]
+[194Ir]
+[147Eu]
+[101Mo]
+[180Hf]
+[189Ir]
+[87Y]
+[43Sc]
+[195Au]
+[112Ag]
+[84BrH]
+[106Ag]
+[109Ag]
+[101Rh]
+[162Yb]
+[228Rn]
+[139Pr]
+[94Y]
+[201Au]
+[40PH3]
+[110Ag+]
+[104Cd]
+[133Ba+2]
+[226Ac]
+[145Gd]
+[186Ir]
+[184Ir]
+[224Rn]
+[185Ir]
+[182Ir]
+[184Hf]
+[200Pt]
+[227Pa]
+[178Yb]
+[72Br-]
+[72BrH]
+[248Am]
+[238Th]
+[161Gd]
+[35S-2]
+[107Ag]
+[FeH6-4]
+[89Sr]
+[SnH3-]
+[SeH3]
+[TeH3+]
+[SbH4+]
+[AsH4+]
+[4He]
+[AsH3-]
+[1HH]
+[3H+]
+[82Rb]
+[85Sr]
+[90Sr]
+[137Cs]
+[133Ba]
+[131Cs]
+[SbH5]
+[224Ra]
+[22Na]
+[210Bi]
+[214Bi]
+[228Ra]
+[127Sb]
+[136Cs]
+[125Sb]
+[134Cs]
+[140Ba]
+[45Ca]
+[206Pb]
+[207Pb]
+[24Na]
+[86Rb]
+[212Bi]
+[208Pb]
+[124Sb]
+[204Pb]
+[44K]
+[129Te]
+[113Sn]
+[204Tl]
+[87Sr]
+[208Tl]
+[87Rb]
+[47Ca]
+[135Cs]
+[216Po]
+[137Ba]
+[207Bi]
+[212Po]
+[79Se]
+[223Ra]
+[86Sr]
+[122Sb]
+[26Al]
+[32Si]
+[126Sn]
+[225Ra]
+[114In]
+[72Ga]
+[132Te]
+[10Be]
+[125Sn]
+[73As]
+[206Bi]
+[117Sn]
+[40Ca]
+[41Ca]
+[89Rb]
+[116In]
+[129Sb]
+[91Sr]
+[71Ge]
+[139Ba]
+[69Ga]
+[120Sb]
+[121Sn]
+[123Sn]
+[131Te]
+[77Ge]
+[135Ba]
+[82Sr]
+[43K]
+[131Ba]
+[92Sr]
+[88Rb]
+[129Cs]
+[144Cs]
+[127Cs]
+[200Tl]
+[202Tl]
+[141Ba]
+[117Sb]
+[116Sb]
+[78As]
+[131Sb]
+[126Sb]
+[128Sb]
+[130Sb]
+[67Ge]
+[68Ge]
+[78Ge]
+[66Ge]
+[223Fr]
+[132Cs]
+[125Cs]
+[138Cs]
+[133Te]
+[84Rb]
+[83Rb]
+[81Rb]
+[142Ba]
+[200Bi]
+[115Sb]
+[194Tl]
+[70Se]
+[112In]
+[118Sb]
+[70Ga]
+[27Mg]
+[202Bi]
+[83Se]
+[9Li]
+[69As]
+[79Rb]
+[81Sr]
+[83Sr]
+[78Se]
+[109In]
+[29Al]
+[118Sn]
+[117In]
+[119Sb]
+[114Sn]
+[138Ba]
+[69Ge]
+[73Ga]
+[74Ge]
+[206Tl]
+[199Tl]
+[130Cs]
+[28Mg]
+[116Te]
+[112Sn]
+[126Ba]
+[211Bi]
+[81Se]
+[127Sn]
+[143Cs]
+[134Te]
+[80Sr]
+[45K]
+[215Po]
+[207Po]
+[111Sn]
+[211Po]
+[128Ba]
+[198Tl]
+[227Ra]
+[213Po]
+[220Ra]
+[128Sn]
+[203Po]
+[205Po]
+[65Ga]
+[197Tl]
+[88Sr]
+[110In]
+[31Si]
+[201Bi]
+[121Te]
+[205Bi]
+[203Bi]
+[195Tl]
+[209Tl]
+[110Sn]
+[222Fr]
+[207At]
+[119In]
+[As@]
+[129IH]
+[157Dy]
+[111IH]
+[230Ra]
+[144Pr+3]
+[SiH3+]
+[3He]
+[AsH5]
+[72Se]
+[95Tc]
+[103Pd]
+[121Sn+2]
+[211Rn]
+[38SH2]
+[127IH]
+[74Br-]
+[133I-]
+[100Tc+4]
+[100Tc]
+[36Cl-]
+[89Y+3]
+[104Rh]
+[152Sm]
+[226Ra]
+[19FH]
+[104Pd]
+[148Gd]
+[157Lu]
+[33SH2]
+[121I-]
+[17FH]
+[71Se]
+[157Sm]
+[148Tb]
+[164Dy]
+[15OH2]
+[15O+]
+[39K]
+[40Ar]
+[50Cr+3]
+[50Cr]
+[52Ti]
+[103Pd+2]
+[130Ba]
+[142Pm]
+[153Gd+3]
+[151Eu]
+[103Rh]
+[124Xe]
+[152Tb]
+[17OH2]
+[20Ne]
+[52Fe]
+[94Zr+4]
+[94Zr]
+[149Pr]
+[16OH2]
+[53Cr+6]
+[53Cr]
+[81Br-]
+[112Pd]
+[125Xe]
+[155Gd]
+[157Gd]
+[168Yb]
+[184Os]
+[166Tb]
+[221Fr]
+[212Ra]
+[75Br-]
+[79Br-]
+[113Ag]
+[23Na]
+[34Cl-]
+[34ClH]
+[38Cl-]
+[56Fe]
+[68Cu]
+[77Br-]
+[90Zr+4]
+[90Zr]
+[102Pd]
+[154Eu+3]
+[57Mn]
+[165Tm]
+[152Dy]
+[217At]
+[77se]
+[13cH-]
+[122Te]
+[156Gd]
+[124Te]
+[53Ni]
+[131Xe]
+[174Hf+4]
+[174Hf]
+[76Se]
+[168Tm]
+[167Dy]
+[154Gd]
+[95Ru]
+[210At]
+[85Br]
+[59Co]
+[122Xe]
+[27Al]
+[54Cr]
+[198Hg]
+[85Rb+]
+[214Tl]
+[229Rn]
+[218Pb]
+[218Bi]
+[167Tm+3]
+[18o+]
+[P@@H+]
+[P@H+]
+[13N+]
+[212Pb+2]
+[217Bi]
+[249Cf+2]
+[18OH3+]
+[90Sr-]
+[Cf+3]
+[200Hg]
+[86Tc]
+[141Pr+3]
+[141Pr]
+[16nH]
+[14NH4+]
+[132Xe]
+[83Kr]
+[70Zn+2]
+[137Ba+2]
+[36Ar]
+[38Ar]
+[21Ne]
+[126Xe]
+[136Xe]
+[128Xe]
+[134Xe]
+[84Kr]
+[86Kr]
+[78Kr]
+[80Kr]
+[82Kr]
+[67Zn+2]
+[65Cu+2]
+[110Te]
+[58Fe+3]
+[142Nd]
+[38K]
+[198Au+3]
+[122IH]
+[38PH3]
+[130I-]
+[40K+]
+[38K+]
+[28Mg+2]
+[208Tl+]
+[13OH2]
+[198Bi]
+[192Bi]
+[194Bi]
+[196Bi]
+[132I-]
+[83Sr+2]
+[169Er+3]
+[122I-]
+[120I-]
+[92Sr+2]
+[126I-]
+[24Mg]
+[84Sr]
+[118Pd+2]
+[118Pd]
+[AsH4]
+[127I-]
+[9C-]
+[11CH3+]
+[17B]
+[7B]
+[4HH]
+[18C-]
+[22CH3-]
+[22CH4]
+[17C-]
+[15CH3]
+[16CH3]
+[11NH3]
+[21NH3]
+[11N-]
+[11NH]
+[16CH]
+[17CH2]
+[99Ru+2]
+[181Ta+2]
+[181Ta]
+[20CH]
+[32PH2]
+[55Fe+2]
+[SH3]
+[S@H]
+[Mn-]
+[IH4]
+[ThH]
+[GaH-]
+[BiH+]
+[EuH2]
+[FeH4-3]
+[FeH6]
+[IH5]
+[NiH+]
+[SrH2]
+[VH4]
+[YH3]
+[seH+]
+<unk>

models/smi_ssed/load.py

@@ -0,0 +1,553 @@
+PATTERN = "(\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|\(|\)|\.|=|#|-|\+|\\\\|\/|:|~|@|\?|>|\*|\$|\%[0-9]{2}|[0-9])"
+# Deep learning
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.backends.cudnn as cudnn
+
+# Tokenizer
+from transformers import BertTokenizer
+
+# Mamba
+
+
+
+#from mamba_ssm.models.mixer_seq_simple import MixerModel
+
+# Data
+import pandas as pd
+import numpy as np
+
+# Chemistry
+from rdkit import Chem
+from rdkit.Chem import PandasTools
+from rdkit.Chem import Descriptors
+PandasTools.RenderImagesInAllDataFrames(True)
+
+# Standard library
+import regex as re
+import random
+import os
+import gc
+from tqdm import tqdm
+from huggingface_hub import hf_hub_download
+tqdm.pandas()
+
+
+# function to canonicalize SMILES
+def normalize_smiles(smi, canonical=True, isomeric=False):
+    try:
+        normalized = Chem.MolToSmiles(
+            Chem.MolFromSmiles(smi), canonical=canonical, isomericSmiles=isomeric
+        )
+    except:
+        normalized = None
+    return normalized
+
+
+class MolTranBertTokenizer(BertTokenizer):
+    def __init__(self, vocab_file: str = '',
+                 do_lower_case=False,
+                 unk_token='<pad>',
+                 sep_token='<eos>',
+                 pad_token='<pad>',
+                 cls_token='<bos>',
+                 mask_token='<mask>',
+                 **kwargs):
+        super().__init__(vocab_file,
+                         unk_token=unk_token,
+                         sep_token=sep_token,
+                         pad_token=pad_token,
+                         cls_token=cls_token,
+                         mask_token=mask_token,
+                         **kwargs)
+
+        self.regex_tokenizer = re.compile(PATTERN)
+        self.wordpiece_tokenizer = None
+        self.basic_tokenizer = None
+        with open(vocab_file) as f:
+            self.padding_idx = f.readlines().index(pad_token+'\n')
+
+    def _tokenize(self, text):
+        split_tokens = self.regex_tokenizer.findall(text)
+        return split_tokens
+    
+    def convert_idx_to_tokens(self, idx_tensor):
+        tokens = [self.convert_ids_to_tokens(idx) for idx in idx_tensor.tolist()]
+        return tokens
+
+    def convert_tokens_to_string(self, tokens):
+        stopwords = ['<bos>', '<eos>']
+        clean_tokens = [word for word in tokens if word not in stopwords]
+        out_string = ''.join(clean_tokens)
+        return out_string
+    
+    def get_padding_idx(self):
+        return self.padding_idx
+    
+    def idx_to_smiles(self, torch_model, idx):
+        '''Convert tokens idx back to SMILES text'''
+        rev_tokens = torch_model.tokenizer.convert_idx_to_tokens(idx)
+        flat_list_tokens = [item for sublist in rev_tokens for item in sublist]
+        decoded_smiles = torch_model.tokenizer.convert_tokens_to_string(flat_list_tokens)
+        return decoded_smiles
+
+
+class AutoEncoderLayer(nn.Module):
+
+    def __init__(self, feature_size, latent_size):
+        super().__init__()
+        self.encoder = self.Encoder(feature_size, latent_size)
+        self.decoder = self.Decoder(feature_size, latent_size)
+    
+    class Encoder(nn.Module):
+
+        def __init__(self, feature_size, latent_size):
+            super().__init__()
+            self.is_cuda_available = torch.cuda.is_available()
+            self.fc1 = nn.Linear(feature_size, latent_size)
+            self.ln_f = nn.LayerNorm(latent_size)
+            self.lat = nn.Linear(latent_size, latent_size, bias=False)
+        
+        def forward(self, x):
+            if self.is_cuda_available:
+                self.fc1.cuda()
+                self.ln_f.cuda()
+                self.lat.cuda()
+                x = x.cuda()
+            x = F.gelu(self.fc1(x))
+            x = self.ln_f(x)
+            x = self.lat(x)
+            return x # -> (N, D)
+    
+    class Decoder(nn.Module):
+
+        def __init__(self, feature_size, latent_size):
+            super().__init__()
+            self.is_cuda_available = torch.cuda.is_available()
+            self.fc1 = nn.Linear(latent_size, latent_size)
+            self.ln_f = nn.LayerNorm(latent_size)
+            self.rec = nn.Linear(latent_size, feature_size, bias=False)
+        
+        def forward(self, x):
+            if self.is_cuda_available:
+                self.fc1.cuda()
+                self.ln_f.cuda()
+                self.rec.cuda()
+                x = x.cuda()
+            x = F.gelu(self.fc1(x))
+            x = self.ln_f(x)
+            x = self.rec(x)
+            return x # -> (N, L*D)
+
+
+class LangLayer(nn.Module):
+    def __init__(self, n_embd, n_vocab):
+        super().__init__()
+        self.is_cuda_available = torch.cuda.is_available()
+        self.embed = nn.Linear(n_embd, n_embd)
+        self.ln_f = nn.LayerNorm(n_embd)
+        self.head = nn.Linear(n_embd, n_vocab, bias=False)
+    def forward(self, tensor):
+        if self.is_cuda_available:
+            self.embed.cuda()
+            self.ln_f.cuda()
+            self.head.cuda()
+            tensor = tensor.cuda()
+        tensor = self.embed(tensor)
+        tensor = F.gelu(tensor)
+        tensor = self.ln_f(tensor)
+        tensor = self.head(tensor)
+        return tensor
+    
+
+class Net(nn.Module):
+    
+    def __init__(self, smiles_embed_dim, n_output=1, dropout=0.2):
+        super().__init__()
+        self.desc_skip_connection = True
+        self.fc1 = nn.Linear(smiles_embed_dim, smiles_embed_dim)
+        self.dropout1 = nn.Dropout(dropout)
+        self.relu1 = nn.GELU()
+        self.fc2 = nn.Linear(smiles_embed_dim, smiles_embed_dim)
+        self.dropout2 = nn.Dropout(dropout)
+        self.relu2 = nn.GELU()
+        self.final = nn.Linear(smiles_embed_dim, n_output)
+
+    def forward(self, smiles_emb, multitask=False):
+        x_out = self.fc1(smiles_emb)
+        x_out = self.dropout1(x_out)
+        x_out = self.relu1(x_out)
+
+        if self.desc_skip_connection is True:
+            x_out = x_out + smiles_emb
+
+        z = self.fc2(x_out)
+        z = self.dropout2(z)
+        z = self.relu2(z)
+        if self.desc_skip_connection is True:
+            z = self.final(z + x_out)
+        else:
+            z = self.final(z)
+
+        if multitask:
+            return F.sigmoid(z)
+        return z
+
+
+class MolEncoder(nn.Module):
+
+    def __init__(self, config, n_vocab):
+        super().__init__()
+        
+        self.config = config
+        self.mamba = MixerModel(
+            d_model=config['n_embd'], 
+            n_layer=config['n_layer'],
+            ssm_cfg=dict(
+                d_state=config['d_state'],
+                d_conv=config['d_conv'],
+                expand=config['expand_factor'],
+                dt_rank=config['dt_rank'],
+                dt_min=config['dt_min'],
+                dt_max=config['dt_max'],
+                dt_init=config['dt_init'],
+                dt_scale=config['dt_scale'],
+                dt_init_floor=config['dt_init_floor'],
+                conv_bias=bool(config['conv_bias']),
+                bias=bool(config['bias']),
+            ),
+            vocab_size=n_vocab,
+            rms_norm=False,
+            fused_add_norm=False,
+        )
+
+        # classification
+        self.lang_model = LangLayer(config['n_embd'], n_vocab)
+
+    def forward(self, idx, mask):
+        x = self.mamba(idx)
+
+        # add padding
+        token_embeddings = x
+        input_mask_expanded = mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+        mask_embeddings = (token_embeddings * input_mask_expanded)
+        token_embeddings = F.pad(mask_embeddings, pad=(0, 0, 0, self.config['max_len'] - mask_embeddings.shape[1]), value=0)
+
+        return token_embeddings
+        
+
+class MoLDecoder(nn.Module):
+
+    def __init__(self, n_vocab, max_len, n_embd, n_gpu=None):
+        super(MoLDecoder, self).__init__()
+
+        self.max_len = max_len
+        self.n_embd = n_embd
+        self.n_gpu = n_gpu
+        self.autoencoder = AutoEncoderLayer(n_embd*max_len, n_embd)
+        self.lm_head = LangLayer(n_embd, n_vocab)
+
+
+class Smi_ssed(nn.Module):
+    """granite.materials.smi-ssed 336M Parameters"""
+
+    def __init__(self, tokenizer, config=None):
+        super(Smi_ssed, self).__init__()
+        
+        # configuration
+        self.config = config
+        self.tokenizer = tokenizer
+        self.padding_idx = tokenizer.get_padding_idx()
+        self.n_vocab = len(self.tokenizer.vocab)
+        self.is_cuda_available = torch.cuda.is_available()
+
+        # instantiate modules
+        if self.config:
+            self.encoder = MolEncoder(self.config, self.n_vocab)
+            self.decoder = MoLDecoder(self.n_vocab, self.config['max_len'], self.config['n_embd'])
+            self.net = Net(self.config['n_embd'], n_output=self.config['n_output'], dropout=self.config['d_dropout'])
+
+    def load_checkpoint(self, ckpt_path):
+        # load checkpoint file
+        checkpoint = torch.load(ckpt_path, map_location=torch.device('cpu'))
+
+        # load hyparameters
+        self.config = checkpoint['hparams']
+        self.max_len = self.config['max_len']
+        self.n_embd = self.config['n_embd']
+        self._set_seed(self.config['seed'])
+
+        # instantiate modules
+        self.encoder = MolEncoder(self.config, self.n_vocab)
+        self.decoder = MoLDecoder(self.n_vocab, self.max_len, self.n_embd)
+        self.net = Net(self.n_embd, n_output=self.config['n_output'] if 'n_output' in self.config else 1, dropout=self.config['d_dropout'])
+
+        # load weights
+        self.load_state_dict(checkpoint['MODEL_STATE'], strict=False)
+
+        # load RNG states each time the model and states are loaded from checkpoint
+        if 'rng' in self.config:
+            rng = self.config['rng']
+            for key, value in rng.items():
+                if key =='torch_state':
+                    torch.set_rng_state(value.cpu())
+                elif key =='cuda_state':
+                    torch.cuda.set_rng_state(value.cpu())
+                elif key =='numpy_state':
+                    np.random.set_state(value)
+                elif key =='python_state':
+                    random.setstate(value)
+                else:
+                    print('unrecognized state')
+
+    def _init_weights(self, module):
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            module.weight.data.normal_(mean=0.0, std=0.02)
+            if isinstance(module, nn.Linear) and module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    def _set_seed(self, value):
+        print('Random Seed:', value)
+        random.seed(value)
+        torch.manual_seed(value)
+        torch.cuda.manual_seed(value)
+        torch.cuda.manual_seed_all(value)
+        np.random.seed(value)
+        cudnn.deterministic = True
+        cudnn.benchmark = False
+
+    def forward(self, smiles, batch_size=100):
+        return self.decode(self.encode(smiles, batch_size=batch_size, return_torch=True))
+            
+    def tokenize(self, smiles):
+        """Tokenize a string into tokens."""
+        if isinstance(smiles, str):
+            batch = [smiles]
+        else:
+            batch = smiles
+        
+        tokens = self.tokenizer(
+            batch, 
+            padding=True,
+            truncation=True, 
+            add_special_tokens=True, 
+            return_tensors="pt",
+            max_length=self.max_len,
+        )
+        
+        idx = tokens['input_ids'].clone().detach()
+        mask = tokens['attention_mask'].clone().detach()
+
+        if self.is_cuda_available:
+            return idx.cuda(), mask.cuda()
+        
+        return idx, mask
+    
+    def extract_all(self, smiles):
+        """Extract all elements from each part of smi-ssed. Be careful."""
+        # evaluation mode
+        self.encoder.eval()
+        self.decoder.eval()
+        if self.is_cuda_available:
+            self.encoder.cuda()
+            self.decoder.cuda()
+
+        # handle single str or a list of str
+        smiles = pd.Series(smiles) if isinstance(smiles, str) else pd.Series(list(smiles))
+
+        # SMILES normalization
+        smiles = smiles.apply(normalize_smiles)
+        null_idx = smiles[smiles.isnull()].index.to_list()  # keep track of SMILES that cannot normalize
+        smiles = smiles.dropna()
+        
+        # tokenizer
+        idx, mask = self.tokenize(smiles)
+        
+        ###########
+        # Encoder #
+        ###########
+        # encoder forward
+        x = self.encoder.mamba(idx)
+
+        # mean pooling
+        token_embeddings = x
+        input_mask_expanded = mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+        sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
+        sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+        true_set = sum_embeddings / sum_mask # DO NOT USE THIS FOR DOWNSTREAM TASKS, USE `pred_set` INSTEAD
+
+        # add padding
+        mask_embeddings = (token_embeddings * input_mask_expanded)
+        token_embeddings = F.pad(mask_embeddings, pad=(0, 0, 0, self.max_len - mask_embeddings.shape[1]), value=0)
+        idx = F.pad(idx, pad=(0, self.max_len - idx.shape[1], 0, 0), value=2)
+
+        true_ids = idx
+        true_cte = token_embeddings
+        true_cte = true_cte.view(-1, self.max_len*self.n_embd)
+
+        ###########
+        # Decoder #
+        ###########
+        # CTE autoencoder
+        pred_set = self.decoder.autoencoder.encoder(true_cte)
+        pred_cte = self.decoder.autoencoder.decoder(pred_set)
+
+        # reconstruct tokens
+        pred_ids = self.decoder.lm_head(pred_cte.view(-1, self.max_len, self.n_embd))
+        pred_ids = torch.argmax(pred_ids, axis=-1)
+
+        # replacing null SMILES with NaN values
+        for idx in null_idx:
+            true_ids = true_ids.tolist()
+            pred_ids = pred_ids.tolist()
+            true_cte = true_cte.tolist()
+            pred_cte = pred_cte.tolist()
+            true_set = true_set.tolist()
+            pred_set = pred_set.tolist()
+
+            true_ids.insert(idx, np.array([np.nan]*self.config['max_len']))
+            pred_ids.insert(idx, np.array([np.nan]*self.config['max_len']))
+            true_cte.insert(idx, np.array([np.nan] * (self.config['max_len']*self.config['n_embd'])))
+            pred_cte.insert(idx, np.array([np.nan] * (self.config['max_len']*self.config['n_embd'])))
+            true_set.insert(idx, np.array([np.nan]*self.config['n_embd']))
+            pred_set.insert(idx, np.array([np.nan]*self.config['n_embd']))
+        
+        if len(null_idx) > 0:
+            true_ids = torch.tensor(true_ids)
+            pred_ids = torch.tensor(pred_ids)
+            true_cte = torch.tensor(true_cte)
+            pred_cte = torch.tensor(pred_cte)
+            true_set = torch.tensor(true_set)
+            pred_set = torch.tensor(pred_set)
+        
+        return ((true_ids, pred_ids), # tokens
+               (true_cte, pred_cte),  # token embeddings
+               (true_set, pred_set))  # smiles embeddings
+
+    def extract_embeddings(self, smiles):
+        """Extract token and SMILES embeddings."""
+        # evaluation mode
+        self.encoder.eval()
+        if self.is_cuda_available:
+            self.encoder.cuda()
+        
+        # tokenizer
+        idx, mask = self.tokenize(smiles)
+        
+        # encoder forward
+        token_embeddings = self.encoder(idx, mask)
+
+        # aggregate token embeddings (similar to mean pooling)
+        # CAUTION: use the embeddings from the autoencoder.
+        smiles_embeddings = self.decoder.autoencoder.encoder(token_embeddings.view(-1, self.max_len*self.n_embd))
+
+        # add padding
+        idx = F.pad(idx, pad=(0, self.max_len - idx.shape[1], 0, 0), value=self.padding_idx)
+
+        return idx, token_embeddings, smiles_embeddings
+    
+    def encode(self, smiles, useCuda=False, batch_size=100, return_torch=False):
+        """Extract efficiently SMILES embeddings per batches."""
+        # TODO: remove useCuda argument
+
+        # handle single str or a list of str
+        smiles = pd.Series(smiles) if isinstance(smiles, str) else pd.Series(list(smiles))
+
+        # SMILES normalization
+        smiles = smiles.apply(normalize_smiles)
+        null_idx = smiles[smiles.isnull()].index.to_list()  # keep track of SMILES that cannot normalize
+        smiles = smiles.dropna()
+
+        # process in batches
+        n_split = smiles.shape[0] // batch_size if smiles.shape[0] >= batch_size else smiles.shape[0]
+        embeddings = [
+            self.extract_embeddings(list(batch))[2].cpu().detach().numpy() 
+                for batch in tqdm(np.array_split(smiles, n_split))
+        ]
+        flat_list = [item for sublist in embeddings for item in sublist]
+
+        # clear GPU memory
+        if self.is_cuda_available:
+            torch.cuda.empty_cache()
+            gc.collect()
+
+        # replacing null SMILES with NaN values
+        for idx in null_idx:
+            flat_list.insert(idx, np.array([np.nan]*self.config['n_embd']))
+        flat_list = np.asarray(flat_list)
+
+        if return_torch:
+            return torch.tensor(flat_list)
+        return pd.DataFrame(flat_list)
+
+    def embd_to_smiles(self, embds):
+        # evaluation mode
+        self.decoder.eval()
+        if self.is_cuda_available:
+            self.decoder.cuda()
+
+        # reconstruct token embeddings
+        pred_token_embds = self.decoder.autoencoder.decoder(embds)
+
+        # reconstruct tokens
+        pred_idx = self.decoder.lm_head(pred_token_embds.view(-1, self.max_len, self.n_embd))
+        pred_idx = torch.argmax(pred_idx, axis=-1).cpu().detach().numpy()
+
+        # convert idx to tokens
+        pred_smiles = []
+        for i in range(pred_idx.shape[0]):
+            idx = pred_idx[i]
+            smiles = self.tokenizer.idx_to_smiles(self, idx)
+            smiles = smiles.replace('<bos>', '') # begin token
+            smiles = smiles.replace('<eos>', '') # end token
+            smiles = smiles.replace('<pad>', '') # pad token
+            pred_smiles.append(smiles)
+        return pred_smiles
+    
+    def decode(self, smiles_embeddings, batch_size=100):
+        """Decode SMILES embeddings back to SMILES."""
+        # process in batches
+        n_split = smiles_embeddings.shape[0] // batch_size if smiles_embeddings.shape[0] >= batch_size else smiles_embeddings.shape[0]
+        embeddings = [
+            self.embd_to_smiles(batch) for batch in tqdm(np.array_split(smiles_embeddings, n_split))
+        ]
+        pred_smiles = [item for sublist in embeddings for item in sublist]
+
+        # clear GPU memory
+        if self.is_cuda_available:
+            torch.cuda.empty_cache()
+            gc.collect()
+
+        return pred_smiles
+
+    def __str__(self):
+        return 'smi-ssed'
+
+
+def load_smi_ssed(folder="./smi_ssed", 
+              ckpt_filename="smi-ssed_130.pt",
+              vocab_filename="bert_vocab_curated.txt"
+              ):
+    repo_id = "ibm/materials.smi_ssed"
+    filename = "bert_vocab_curated.txt"
+    vocab_filename = hf_hub_download(repo_id=repo_id, filename=filename)
+    tokenizer = MolTranBertTokenizer(vocab_filename)
+    model = Smi_ssed(tokenizer)
+
+    filename = "smi_ssed_130.pt"
+    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
+    model.load_checkpoint(file_path)
+    model.eval()
+
+
+    #tokenizer = MolTranBertTokenizer(os.path.join(folder, vocab_filename))
+    #model = Smi_ssed(tokenizer)
+    #model.load_checkpoint(os.path.join(folder, ckpt_filename))
+    #model.eval()
+    print('Vocab size:', len(tokenizer.vocab))
+    print(f'[INFERENCE MODE - {str(model)}]')
+    return model

models/smi_ted/smi_ted_light/load.py

@@ -665,10 +665,12 @@ def load_smi_ted(folder="./smi_ted_light",
               ckpt_filename="smi-ted-Light_40.pt",
               vocab_filename="bert_vocab_curated.txt"
               ):
-    tokenizer = MolTranBertTokenizer(os.path.join(folder, vocab_filename))
+    repo_id = "ibm/materials.smi-ted"
+    filename = "bert_vocab_curated.txt"
+    vocab_filename = hf_hub_download(repo_id=repo_id, filename=filename)
+    tokenizer = MolTranBertTokenizer(vocab_filename)
     model = Smi_ted(tokenizer)
 
-    repo_id = "ibm/materials.smi-ted"
     filename = "smi-ted-Light_40.pt"
     file_path = hf_hub_download(repo_id=repo_id, filename=filename)
     model.load_checkpoint(file_path)

requirements.txt

@@ -1,29 +1,29 @@
 pip-tools
 huggingface_hub
 pydantic==2.10.6
-matplotlib
-numpy
-scipy
-torch
-torchinfo
-torch-geometric
-pandas
-pyarrow
-scikit-learn
-xgboost
-rdkit
-networkx
-datasets
-evaluate
-selfies
-gradio
-transformers
-requests
-urllib3
-aiohttp
-zipp
+matplotlib==3.9.2
+numpy>=1.26.1, <2.0.0
+scipy>=1.12.0
+torch>=2.1.0
+torchinfo>=1.8.0
+torch-geometric>=2.3.1
+pandas>=1.5.3
+pyarrow>=14.0.1
+scikit-learn>=1.5.0
+xgboost==1.6.2
+rdkit>=2024.3.5
+networkx>=2.8
+datasets>=2.13.1
+evaluate>=0.4.0
+selfies>=2.1.0
+gradio #==3.41.0
+transformers==4.41.2
+requests>=2.32.2
+urllib3>=2.2.2
+aiohttp>=3.10.2
+zipp>=3.19.1
 umap-learn
 torch-optimizer
-tqdm
-pandas
+tqdm>=4.66.4
+pandas==2.2.3
 mordred