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.gitattributes

@@ -58,3 +58,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
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+pretraining_datasets/pretraining_dataset_meta.csv filter=lfs diff=lfs merge=lfs -text

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models/SELFormerMM/config.json

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+{
+  "_name_or_path": "HUBioDataLab/SELFormer",
+  "architectures": [
+    "RobertaForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
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+  "hidden_act": "gelu",
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+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
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+}

models/SELFormerMM/merges.txt

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+#version: 0.2 - Trained by `huggingface/tokenizers`
+B r
+a n
+c h
+Br an
+Bran ch
+Branch 1
+= C
+R i
+n g
+Ri ng
+Ring 1
+= Branch1
+Branch 2
+= O
+Ring 2
+H 1
+C @
+= N
+# Branch1
+C@ @
+= Branch2
+C@ H1
+C@@ H1
+# Branch2
+# C
+C l
+/ C
+N H1
+= Ring1
++ 1
+- 1
+O -1
+N +1
+\ C
+# N
+/ N
+= Ring2
+= S
+=N +1
+\ N
+N a
+Na +1
+/ O
+\ O
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+Branch 3
+\ S
+S +1
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+/ S
+=N -1
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+= P
+N -1
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+\ C@H1
+/ N+1
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++ 3
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+1 8
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+@ +1
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+1 2
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++ 2
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+- /
+\ Cl
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+/ C@@
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+/ Cl
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+=S e
+S @@+1
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+1 4
+-\ Ring2
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+\ F
+/ C@
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+H 2
+H1 -1
+=O +1
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+=S +1
+Z n
+/ P
+a +2
+/ I
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+C a+2
+\ Br
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+A l
+/ F
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+12 3
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+1 3
+I +1
+/ O-1
+-\ Ring1
+B H2
+BH2 -1
+\ I
+/ NH1
+O +1
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+= 14C
+/ S+1
+= Ring3
+\ C@@
+H2 +1
+\ C@
+A g
+= As
+=Se +1
+N H2+1
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+-/ Ring1
+= Te
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+Na H1
+=Te +1
+NH1 +1
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+H1 +1
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+\ P
+14C H2
+13 C
+14C H1
+= 11C
+S @@
+=P @@
+Si H2
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+B H3-1
+S @
+=14C H1
+=P H1
+=P @
+=N H1+1
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+=S i
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+/ B
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+19 F
+/ 125I
+P @+1
+R b
+Cl +1
+Si H4
+Rb +1
+= Branch3
+N @@
+As +1
+/ Si
+B H1-1
+S H1
+/ 123I
+3 2
+= Mg
+H +1
+\ B
+Si H1
+P@@ +1
+- 2
+1 5
+1 7
+3 5
+= 13CH1
+C s
+=N H2+1
+=S H1
+Mg H2
+32 P
+17 F
+35 S
+Cs +1
+# 11C
+/ 131I
+B i
+\ 125I
+=S @@
+\S -1
+6 Br
+7 I
+7 6Br
+= B
+e H1
+\N -1
+18 O
+12 7I
+11C H2
+14 C@@H1
+Te H2
+15 NH1
+Bi +3
+/ P+1
+/ 13C
+/ 13CH1
+0 B
+1 0B
+= Al
+= 18O
+B H0
+F -1
+N H3
+S -2
+Br +2
+Cl +2
+\S i
+/S -1
+=P H2
+14 C@H1
+NH3 +1
+# 14C
+# O+1
+- 3
+2 2
+4 H
+5 Se
+5 Sr+2
+7 5Se
+8 5Sr+2
+= B-1
+= 13C
+@ -1
+B e
+B @@
+B @-1
+C a
+C H1
+I +3
+K H1
+O H1+1
+R a+2
+S H1-1
+\ PH1
+\ 123I
+=C a
+\C H1-1
+=S @
+\S eH1
+/S eH1
+Se -1
+Li H1
+18F -1
+125I H1
+11C H1
+Te H1
+Zn +1
+Zn -2
+Al -3
+13C H3
+15 N
+Be +2
+B@@ -1
+# P
+# S
+- 4
+/ PH1
+/ P@@
+/ As
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+/ 14CH1
+2 K+1
+2 Rb+1
+3 Se
+3 Ra+2
+4 5
+4 7
+4 2K+1
+5 I-1
+7 3Se
+8 9
+8 2Rb+1
+= 32
+= 32P
+C H0
+C H2
+I +2
+N H0
+N H4
+O H1
+P H2+1
+S H0
+S H2
+\ 3H
+\ 11CH3
+\C -1
+\S e
+Si @
+Si -1
+Si H1-1
+Si H3-1
+/S e
+Se -2
+\NH1 -1
+18F H1
+12 5I-1
+11 C@@H1
+11 C-1
+As H1
+As -1
+14 C@@
+Te -1
+Mg +1
+123 I-1
+123 Te
+123I H1
+13 5I
+131 I-1
+Ag -4
+124 I-1
+76Br H1
+18O H1
+22 Na+1
+22 3Ra+2
+Ca H2
+45 Ca+2
+47 Ca+2
+89 Sr+2
+=32 PH1
+NH4 +1

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models/SELFormerMM/special_tokens_map.json

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+}

models/SELFormerMM/tokenizer.json

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+    "strategy": "LongestFirst",
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+      "17": 271,
+      "35": 272,
+      "=13CH1": 273,
+      "Cs": 274,
+      "=NH2+1": 275,
+      "=SH1": 276,
+      "MgH2": 277,
+      "32P": 278,
+      "17F": 279,
+      "35S": 280,
+      "Cs+1": 281,
+      "#11C": 282,
+      "/131I": 283,
+      "Bi": 284,
+      "\\125I": 285,
+      "=S@@": 286,
+      "\\S-1": 287,
+      "6Br": 288,
+      "7I": 289,
+      "76Br": 290,
+      "=B": 291,
+      "eH1": 292,
+      "\\N-1": 293,
+      "18O": 294,
+      "127I": 295,
+      "11CH2": 296,
+      "14C@@H1": 297,
+      "TeH2": 298,
+      "15NH1": 299,
+      "Bi+3": 300,
+      "/P+1": 301,
+      "/13C": 302,
+      "/13CH1": 303,
+      "0B": 304,
+      "10B": 305,
+      "=Al": 306,
+      "=18O": 307,
+      "BH0": 308,
+      "F-1": 309,
+      "NH3": 310,
+      "S-2": 311,
+      "Br+2": 312,
+      "Cl+2": 313,
+      "\\Si": 314,
+      "/S-1": 315,
+      "=PH2": 316,
+      "14C@H1": 317,
+      "NH3+1": 318,
+      "#14C": 319,
+      "#O+1": 320,
+      "-3": 321,
+      "22": 322,
+      "4H": 323,
+      "5Se": 324,
+      "5Sr+2": 325,
+      "75Se": 326,
+      "85Sr+2": 327,
+      "=B-1": 328,
+      "=13C": 329,
+      "@-1": 330,
+      "Be": 331,
+      "B@@": 332,
+      "B@-1": 333,
+      "Ca": 334,
+      "CH1": 335,
+      "I+3": 336,
+      "KH1": 337,
+      "OH1+1": 338,
+      "Ra+2": 339,
+      "SH1-1": 340,
+      "\\PH1": 341,
+      "\\123I": 342,
+      "=Ca": 343,
+      "\\CH1-1": 344,
+      "=S@": 345,
+      "\\SeH1": 346,
+      "/SeH1": 347,
+      "Se-1": 348,
+      "LiH1": 349,
+      "18F-1": 350,
+      "125IH1": 351,
+      "11CH1": 352,
+      "TeH1": 353,
+      "Zn+1": 354,
+      "Zn-2": 355,
+      "Al-3": 356,
+      "13CH3": 357,
+      "15N": 358,
+      "Be+2": 359,
+      "B@@-1": 360,
+      "#P": 361,
+      "#S": 362,
+      "-4": 363,
+      "/PH1": 364,
+      "/P@@": 365,
+      "/As": 366,
+      "/14C": 367,
+      "/14CH1": 368,
+      "2K+1": 369,
+      "2Rb+1": 370,
+      "3Se": 371,
+      "3Ra+2": 372,
+      "45": 373,
+      "47": 374,
+      "42K+1": 375,
+      "5I-1": 376,
+      "73Se": 377,
+      "89": 378,
+      "82Rb+1": 379,
+      "=32": 380,
+      "=32P": 381,
+      "CH0": 382,
+      "CH2": 383,
+      "I+2": 384,
+      "NH0": 385,
+      "NH4": 386,
+      "OH1": 387,
+      "PH2+1": 388,
+      "SH0": 389,
+      "SH2": 390,
+      "\\3H": 391,
+      "\\11CH3": 392,
+      "\\C-1": 393,
+      "\\Se": 394,
+      "Si@": 395,
+      "Si-1": 396,
+      "SiH1-1": 397,
+      "SiH3-1": 398,
+      "/Se": 399,
+      "Se-2": 400,
+      "\\NH1-1": 401,
+      "18FH1": 402,
+      "125I-1": 403,
+      "11C@@H1": 404,
+      "11C-1": 405,
+      "AsH1": 406,
+      "As-1": 407,
+      "14C@@": 408,
+      "Te-1": 409,
+      "Mg+1": 410,
+      "123I-1": 411,
+      "123Te": 412,
+      "123IH1": 413,
+      "135I": 414,
+      "131I-1": 415,
+      "Ag-4": 416,
+      "124I-1": 417,
+      "76BrH1": 418,
+      "18OH1": 419,
+      "22Na+1": 420,
+      "223Ra+2": 421,
+      "CaH2": 422,
+      "45Ca+2": 423,
+      "47Ca+2": 424,
+      "89Sr+2": 425,
+      "=32PH1": 426,
+      "NH4+1": 427
+    },
+    "merges": [
+      "B r",
+      "a n",
+      "c h",
+      "Br an",
+      "Bran ch",
+      "Branch 1",
+      "= C",
+      "R i",
+      "n g",
+      "Ri ng",
+      "Ring 1",
+      "= Branch1",
+      "Branch 2",
+      "= O",
+      "Ring 2",
+      "H 1",
+      "C @",
+      "= N",
+      "# Branch1",
+      "C@ @",
+      "= Branch2",
+      "C@ H1",
+      "C@@ H1",
+      "# Branch2",
+      "# C",
+      "C l",
+      "/ C",
+      "N H1",
+      "= Ring1",
+      "+ 1",
+      "- 1",
+      "O -1",
+      "N +1",
+      "\\ C",
+      "# N",
+      "/ N",
+      "= Ring2",
+      "= S",
+      "=N +1",
+      "\\ N",
+      "N a",
+      "Na +1",
+      "/ O",
+      "\\ O",
+      "Br -1",
+      "Branch 3",
+      "\\ S",
+      "S +1",
+      "Cl -1",
+      "I -1",
+      "/ C@@H1",
+      "S i",
+      "/ C@H1",
+      "/ S",
+      "=N -1",
+      "S e",
+      "= P",
+      "N -1",
+      "Ring 3",
+      "2 H",
+      "P +1",
+      "K +1",
+      "\\ C@@H1",
+      "\\ C@H1",
+      "/ N+1",
+      "@ @",
+      "C -1",
+      "# N+1",
+      "B -1",
+      "+ 3",
+      "Cl +3",
+      "\\ NH1",
+      "L i",
+      "Li +1",
+      "P H1",
+      "1 8",
+      "18 F",
+      "@ +1",
+      "3 H",
+      "P @@",
+      "H 0",
+      "O H0",
+      "1 2",
+      "P @",
+      "+ 2",
+      "@@ +1",
+      "S -1",
+      "/ Br",
+      "- /",
+      "\\ Cl",
+      "-/ Ring2",
+      "\\ O-1",
+      "1 1",
+      "5 I",
+      "12 5I",
+      "11 C",
+      "H 3",
+      "\\ N+1",
+      "- \\",
+      "/ C@@",
+      "S @+1",
+      "A s",
+      "/ Cl",
+      "11C H3",
+      "=S e",
+      "S @@+1",
+      "N @+1",
+      "1 4",
+      "-\\ Ring2",
+      "14 C",
+      "\\ F",
+      "/ C@",
+      "T e",
+      "H 2",
+      "H1 -1",
+      "=O +1",
+      "N @@+1",
+      "C +1",
+      "=S +1",
+      "Z n",
+      "/ P",
+      "a +2",
+      "/ I",
+      "O H1-1",
+      "C a+2",
+      "\\ Br",
+      "M g",
+      "Zn +2",
+      "A l",
+      "/ F",
+      "Mg +2",
+      "12 3",
+      "123 I",
+      "1 3",
+      "I +1",
+      "/ O-1",
+      "-\\ Ring1",
+      "B H2",
+      "BH2 -1",
+      "\\ I",
+      "/ NH1",
+      "O +1",
+      "13 1",
+      "131 I",
+      "= 14C",
+      "/ S+1",
+      "= Ring3",
+      "\\ C@@",
+      "H2 +1",
+      "\\ C@",
+      "A g",
+      "= As",
+      "=Se +1",
+      "N H2+1",
+      "Se H1",
+      "-/ Ring1",
+      "= Te",
+      "Al +3",
+      "Na H1",
+      "=Te +1",
+      "NH1 +1",
+      "Ag +1",
+      "H1 +1",
+      "NH1 -1",
+      "\\ P",
+      "14C H2",
+      "13 C",
+      "14C H1",
+      "= 11C",
+      "S @@",
+      "=P @@",
+      "Si H2",
+      "H3 -1",
+      "14C H3",
+      "B H3-1",
+      "S @",
+      "=14C H1",
+      "=P H1",
+      "=P @",
+      "=N H1+1",
+      "\\S +1",
+      "12 4",
+      "C H1-1",
+      "S r",
+      "=S i",
+      "124 I",
+      "Sr +2",
+      "#C -1",
+      "/C -1",
+      "N @",
+      "/N -1",
+      "13C H1",
+      "/ B",
+      "1 9",
+      "B a+2",
+      "H 4",
+      "S H1+1",
+      "Se +1",
+      "19 F",
+      "/ 125I",
+      "P @+1",
+      "R b",
+      "Cl +1",
+      "Si H4",
+      "Rb +1",
+      "= Branch3",
+      "N @@",
+      "As +1",
+      "/ Si",
+      "B H1-1",
+      "S H1",
+      "/ 123I",
+      "3 2",
+      "= Mg",
+      "H +1",
+      "\\ B",
+      "Si H1",
+      "P@@ +1",
+      "- 2",
+      "1 5",
+      "1 7",
+      "3 5",
+      "= 13CH1",
+      "C s",
+      "=N H2+1",
+      "=S H1",
+      "Mg H2",
+      "32 P",
+      "17 F",
+      "35 S",
+      "Cs +1",
+      "# 11C",
+      "/ 131I",
+      "B i",
+      "\\ 125I",
+      "=S @@",
+      "\\S -1",
+      "6 Br",
+      "7 I",
+      "7 6Br",
+      "= B",
+      "e H1",
+      "\\N -1",
+      "18 O",
+      "12 7I",
+      "11C H2",
+      "14 C@@H1",
+      "Te H2",
+      "15 NH1",
+      "Bi +3",
+      "/ P+1",
+      "/ 13C",
+      "/ 13CH1",
+      "0 B",
+      "1 0B",
+      "= Al",
+      "= 18O",
+      "B H0",
+      "F -1",
+      "N H3",
+      "S -2",
+      "Br +2",
+      "Cl +2",
+      "\\S i",
+      "/S -1",
+      "=P H2",
+      "14 C@H1",
+      "NH3 +1",
+      "# 14C",
+      "# O+1",
+      "- 3",
+      "2 2",
+      "4 H",
+      "5 Se",
+      "5 Sr+2",
+      "7 5Se",
+      "8 5Sr+2",
+      "= B-1",
+      "= 13C",
+      "@ -1",
+      "B e",
+      "B @@",
+      "B @-1",
+      "C a",
+      "C H1",
+      "I +3",
+      "K H1",
+      "O H1+1",
+      "R a+2",
+      "S H1-1",
+      "\\ PH1",
+      "\\ 123I",
+      "=C a",
+      "\\C H1-1",
+      "=S @",
+      "\\S eH1",
+      "/S eH1",
+      "Se -1",
+      "Li H1",
+      "18F -1",
+      "125I H1",
+      "11C H1",
+      "Te H1",
+      "Zn +1",
+      "Zn -2",
+      "Al -3",
+      "13C H3",
+      "15 N",
+      "Be +2",
+      "B@@ -1",
+      "# P",
+      "# S",
+      "- 4",
+      "/ PH1",
+      "/ P@@",
+      "/ As",
+      "/ 14C",
+      "/ 14CH1",
+      "2 K+1",
+      "2 Rb+1",
+      "3 Se",
+      "3 Ra+2",
+      "4 5",
+      "4 7",
+      "4 2K+1",
+      "5 I-1",
+      "7 3Se",
+      "8 9",
+      "8 2Rb+1",
+      "= 32",
+      "= 32P",
+      "C H0",
+      "C H2",
+      "I +2",
+      "N H0",
+      "N H4",
+      "O H1",
+      "P H2+1",
+      "S H0",
+      "S H2",
+      "\\ 3H",
+      "\\ 11CH3",
+      "\\C -1",
+      "\\S e",
+      "Si @",
+      "Si -1",
+      "Si H1-1",
+      "Si H3-1",
+      "/S e",
+      "Se -2",
+      "\\NH1 -1",
+      "18F H1",
+      "12 5I-1",
+      "11 C@@H1",
+      "11 C-1",
+      "As H1",
+      "As -1",
+      "14 C@@",
+      "Te -1",
+      "Mg +1",
+      "123 I-1",
+      "123 Te",
+      "123I H1",
+      "13 5I",
+      "131 I-1",
+      "Ag -4",
+      "124 I-1",
+      "76Br H1",
+      "18O H1",
+      "22 Na+1",
+      "22 3Ra+2",
+      "Ca H2",
+      "45 Ca+2",
+      "47 Ca+2",
+      "89 Sr+2",
+      "=32 PH1",
+      "NH4 +1"
+    ]
+  }
+}

models/SELFormerMM/tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "errors": "replace",
+  "mask_token": {
+    "__type": "AddedToken",
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "model_max_length": 1000000000000000019884624838656,
+  "name_or_path": "HUBioDataLab/SELFormer",
+  "pad_token": {
+    "__type": "AddedToken",
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "special_tokens_map_file": null,
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

models/SELFormerMM/vocab.json

@@ -0,0 +1 @@
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pretraining_datasets/graph_embeddings.npy

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+size 5846661248

pretraining_datasets/kg_embeddings.npy

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+oid sha256:1a7316c20e3fd4beb0753b83da934f1b7538fe3258284c4d4dfea5872ea9c0e3
+size 1461665408

pretraining_datasets/pretraining_dataset_meta.csv

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+oid sha256:884157632e2124ba90d715756259ead61fcbf840a46edecfa5b7ab1422432cd5
+size 1816899178

pretraining_datasets/selformermm_kg_heterodata.pt

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+oid sha256:7523a29b6ce2abd6cc58f02cdeea9b7cb5c74e19c46ed108a6965bde19aac681
+size 2397688779

pretraining_datasets/text_embeddings.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:97ba31be9aee7e9f25fea2d57a28d3c657aeddaf42c834552a17e626e8441001
+size 8769991808

processing/dmgi_model.py

@@ -0,0 +1,103 @@
+import torch
+import torch.nn.functional as F
+
+
+import torch_geometric.transforms as T
+from torch_geometric.nn import GCNConv
+
+
+def load_heterodata(path):
+    
+    data = torch.load(path, map_location=torch.device('cpu'))
+
+    print("Available edge types in the dataset:", data.edge_types)    
+    # data['Compound'].train_mask = torch.zeros(data['Compound'].num_nodes, dtype=torch.bool)
+    # data['Compound'].val_mask = torch.zeros(data['Compound'].num_nodes, dtype=torch.bool)
+    # data['Compound'].test_mask = torch.zeros(data['Compound'].num_nodes, dtype=torch.bool)
+
+    
+    # train_indices = np.random.choice(data['Compound'].num_nodes, int(data['Compound'].num_nodes * 0.8), replace=False)
+    # val_indices = np.random.choice(np.setdiff1d(np.arange(data['Compound'].num_nodes), train_indices), int(data['Compound'].num_nodes * 0.1), replace=False)
+    # test_indices = np.setdiff1d(np.arange(data['Compound'].num_nodes), np.concatenate([train_indices, val_indices]))
+
+    # data['Compound'].train_mask[train_indices] = 1
+    # data['Compound'].val_mask[val_indices] = 1
+    # data['Compound'].test_mask[test_indices] = 1
+
+    
+    # print(f'Train node count: {data["Compound"].train_mask.sum()}')
+    # print(f'Val node count: {data["Compound"].val_mask.sum()}')
+    # print(f'Test node count: {data["Compound"].test_mask.sum()}')
+
+    metapaths = [
+        [('Compound', 'CTI', 'Protein'), ('Protein', 'rev_CTI', 'Compound')],
+        [('Drug', 'DTI', 'Protein'), ('Protein', 'rev_DTI', 'Drug')],
+        [('Protein', 'PPI', 'Protein'), ('Protein', 'rev_PPI', 'Protein')],
+        [('Gene', 'Orthology', 'Gene'), ('Gene', 'rev_Orthology', 'Gene')],
+    ]
+    print(metapaths)
+    
+    data = T.AddMetaPaths(metapaths, drop_orig_edge_types=True)(data)
+    print('Available edge types in the dataset after adding metapaths:', data.edge_types)
+
+    return data
+
+class DMGI(torch.nn.Module):
+    def __init__(self, num_nodes, in_channels, out_channels, num_relations):
+        super().__init__()
+        self.convs = torch.nn.ModuleList(
+            [GCNConv(in_channels, out_channels) for _ in range(num_relations)])
+        self.M = torch.nn.Bilinear(out_channels, out_channels, 1)
+        self.Z = torch.nn.Parameter(torch.empty(num_nodes, out_channels))
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        for conv in self.convs:
+            conv.reset_parameters()
+        torch.nn.init.xavier_uniform_(self.M.weight)
+        self.M.bias.data.zero_()
+        torch.nn.init.xavier_uniform_(self.Z)
+
+    def forward(self, x, edge_indices):
+        pos_hs, neg_hs, summaries = [], [], []
+        for conv, edge_index in zip(self.convs, edge_indices):
+            pos_h = F.dropout(x, p=0.5, training=self.training)
+            pos_h = conv(pos_h, edge_index).relu()
+            pos_hs.append(pos_h)
+
+            neg_h = F.dropout(x, p=0.5, training=self.training)
+            neg_h = neg_h[torch.randperm(neg_h.size(0), device=neg_h.device)]
+            neg_h = conv(neg_h, edge_index).relu()
+            neg_hs.append(neg_h)
+
+            summaries.append(pos_h.mean(dim=0, keepdim=True))
+
+        return pos_hs, neg_hs, summaries
+
+    def loss(self, pos_hs, neg_hs, summaries):
+        loss = 0.
+        for pos_h, neg_h, s in zip(pos_hs, neg_hs, summaries):
+            s = s.expand_as(pos_h)
+            loss += -torch.log(self.M(pos_h, s).sigmoid() + 1e-15).mean()
+            loss += -torch.log(1 - self.M(neg_h, s).sigmoid() + 1e-15).mean()
+
+        pos_mean = torch.stack(pos_hs, dim=0).mean(dim=0)
+        neg_mean = torch.stack(neg_hs, dim=0).mean(dim=0)
+
+        pos_reg_loss = (self.Z - pos_mean).pow(2).sum()
+        neg_reg_loss = (self.Z - neg_mean).pow(2).sum()
+        loss += 0.001 * (pos_reg_loss - neg_reg_loss)
+
+        return loss
+    
+
+def load_dmgi_model(path, data):
+        
+        model = DMGI(data['Compound'].num_nodes,
+                    data['Compound'].x.size(-1), 
+                    64,
+                    len(data.edge_types))
+        
+        model.load_state_dict(torch.load(path))
+        
+        return model

processing/graph_embedding.py

@@ -0,0 +1,71 @@
+import os
+import pandas as pd
+import numpy as np
+import torch
+import time
+from datetime import datetime
+from unimol_tools import UniMolRepr
+
+unimol_model = UniMolRepr(data_type='molecule', remove_hs=False, use_gpu=True)
+
+def get_unimol_embeddings_batch(smiles_list, model):
+    try:
+        batch_repr = model.get_repr(smiles_list, return_atomic_reprs=True)
+        cls_reprs = batch_repr['cls_repr']
+        return np.array(cls_reprs)
+    except Exception as e:
+        print(f"Error embedding batch: {e}")
+        return None
+
+def process_folder_unimol(folder_path, batch_size=2000):
+    """
+    Walk through the folder and process each CSV file ending with 'filtered'.
+    Embeddings are saved in the same folder with '_graph_embedding.npy' added to the filename.
+    """
+    for root, dirs, files in os.walk(folder_path):
+        for file in files:
+            if not file.endswith("filtered.csv") and not file.endswith("mock.csv"):
+                file_path = os.path.join(root, file)
+                print(f"Processing file: {file_path}")
+                try:
+                    df = pd.read_csv(file_path)
+                    column_name = 'smiles'
+                    if column_name not in df.columns:
+                        column_name = 'mol'
+
+                        if column_name not in df.columns:
+                            raise ValueError("'smiles' column not found in the CSV file.")
+
+                    df = df.dropna(subset=[column_name])
+                    smiles_list = df[column_name].tolist()
+                    print(f"Found {len(smiles_list)} valid SMILES to process.")
+
+                    all_embeddings = []
+                    for i in range(0, len(smiles_list), batch_size):
+                        batch = smiles_list[i:i+batch_size]
+                        embeddings = get_unimol_embeddings_batch(batch, unimol_model)
+                        if embeddings is not None:
+                            all_embeddings.append(embeddings)
+                        else:
+                            print(f"Warning: Batch {i//batch_size} failed.")
+
+                    if all_embeddings:
+                        final_embeddings = np.concatenate(all_embeddings)
+                        output_file = os.path.join(root, f"{os.path.splitext(file)[0]}_graph_embedding.npy")
+                        np.save(output_file, final_embeddings)
+                        print(f"Saved embeddings with shape {final_embeddings.shape} to {output_file}\n")
+                    else:
+                        print(f"No embeddings generated for {file_path}.")
+
+                except Exception as e:
+                    print(f"Failed to process {file_path}: {e}\n")
+
+folder_path = "/home/g3-bbm-project/main_folder/FineTune/finetune_data_multi/finetuning_datasets/classification"  # Set your top-level folder here
+print(f"Starting UniMol embedding processing at {datetime.now().strftime('%H:%M:%S')}")
+start_time = time.time()
+
+process_folder_unimol(folder_path)
+
+total_time = time.time() - start_time
+print(f"\nTotal execution time: {total_time:.2f} seconds")
+print(f"Finished at {datetime.now().strftime('%H:%M:%S')}")

processing/npy_to_h5.py

@@ -0,0 +1,28 @@
+import numpy as np
+import h5py
+import argparse
+import os
+
+"""
+Example usage:
+python convert_npy_to_h5.py /path/to/input_file.npy /path/to/output_file.h5
+"""
+
+def convert_npy_to_h5(npy_file_path, h5_output_path):
+    if not os.path.isfile(npy_file_path):
+        print(f"Error: Input file '{npy_file_path}' does not exist.")
+        return
+
+    data = np.load(npy_file_path)
+
+    with h5py.File(h5_output_path, 'w') as h5_file:
+        h5_file.create_dataset('data', data=data)
+        print(f"Data from '{npy_file_path}' has been successfully saved to '{h5_output_path}'.")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Convert a .npy file to a .h5 file.")
+    parser.add_argument("npy_file", type=str, help="Path to the input .npy file.")
+    parser.add_argument("h5_file", type=str, help="Path to the output .h5 file.")
+
+    args = parser.parse_args()
+    convert_npy_to_h5(args.npy_file, args.h5_file)

processing/pretrain_dmgi.py

@@ -0,0 +1,77 @@
+import os
+
+import numpy as np
+
+import torch
+from torch.optim import Adam
+from torch_geometric import seed_everything
+
+from dmgi_model import load_heterodata, DMGI
+
+from datetime import datetime
+
+# set random seeds
+seed_everything(42)
+np.random.seed(42)
+
+torch.set_num_threads(5)
+
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--data', default='/home/g3bbmproject/main_folder/KG/kg.pt/selformerv2_kg_heterodata_1224.pt')
+
+args = parser.parse_args()
+
+
+def train(data, model, optimizer):
+    model.train()
+    optimizer.zero_grad()
+    x = data['Compound'].x
+    edge_indices = data.edge_index_dict.values()
+    pos_hs, neg_hs, summaries = model(x, edge_indices)
+    loss = model.loss(pos_hs, neg_hs, summaries)
+    loss.backward()
+    optimizer.step()
+    return float(loss)
+
+
+def pretrain_dmgi(hps, data, device):
+    model = DMGI(data['Compound'].num_nodes,
+                data['Compound'].x.size(-1), 
+                hps[0], 
+                len(data.edge_types))
+    
+    data, model = data.to(device), model.to(device)
+    print(data.node_types)
+    # Print available edge types in the dataset
+    print("Available edge types in the dataset:", data.edge_types)
+
+    
+    optimizer = Adam(model.parameters(), lr=hps[1], weight_decay=hps[2])
+
+    for epoch in range(1, 101):
+        epoch_start = datetime.now()
+        train_loss = train(data, model, optimizer)
+        
+        if epoch == 1 or epoch % 25 == 0:
+            print(f'\tEpoch: {epoch:03d}, Loss: {train_loss:.4f}, Time: {datetime.now() - epoch_start}')
+
+    return train_loss, model
+
+
+if __name__ == '__main__':
+    data = load_heterodata(args.data)
+    print(f'Loaded data: {args.data}')
+    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+    print(f'\nUsing device: {device}\n')
+
+    print('Starting training...\n')
+    train_start = datetime.now()
+    loss, model = pretrain_dmgi([32, 0.01, 0.001], data, device)
+    print(f'\nDone. Total training time: {datetime.now() - train_start}')
+    
+    # save model
+    os.makedirs('models', exist_ok=True)
+    torch.save(model.state_dict(), 'data/pretrained_models/kg_dmgi_model.pt')
+    print(f'Model saved: data/pretrained_models/kg_dmgi_model.pt\n')

processing/selfies_embedding.py

@@ -0,0 +1,39 @@
+import os
+import pandas as pd
+from pandarallel import pandarallel
+from transformers import RobertaTokenizer, RobertaModel, RobertaConfig
+import torch
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+os.environ["WANDB_DISABLED"] = "true"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+
+
+SELFIES_DATASET_PATH = "data/temp_selfies.csv"  			# path to the SELFIES dataset
+MODEL_FILE_PATH = "data/pretrained_models/SELFormer"       	# path to the pre-trained SELFormer model
+OUTPUT_EMBEDDINGS_PATH = "data/embeddings.csv"             	# path to save the generated embeddings
+
+
+df = pd.read_csv(SELFIES_DATASET_PATH)						# load the dataset
+print(f"Loaded dataset with {len(df)} molecules.")
+
+
+config = RobertaConfig.from_pretrained(MODEL_FILE_PATH)		# load the pre-trained model and tokenizer
+config.output_hidden_states = True
+tokenizer = RobertaTokenizer.from_pretrained("data/RobertaFastTokenizer")
+model = RobertaModel.from_pretrained(MODEL_FILE_PATH, config=config)
+
+
+def get_sequence_embeddings(selfies):
+    token = torch.tensor([tokenizer.encode(selfies, add_special_tokens=True, max_length=512, padding=True, truncation=True)])	# tokenize the SELFIES string
+    output = model(token)																										# forward pass through the model
+    sequence_out = output[0]																									# extract the sequence output and compute the mean pooling
+    return torch.mean(sequence_out[0], dim=0).tolist()
+
+print("Generating embeddings...")
+pandarallel.initialize(nb_workers=5, progress_bar=True)
+df["sequence_embeddings"] = df.selfies.parallel_apply(get_sequence_embeddings)
+
+df.drop(columns=["selfies"], inplace=True)
+df.to_csv(OUTPUT_EMBEDDINGS_PATH, index=False)
+print(f"Embeddings saved to {OUTPUT_EMBEDDINGS_PATH}")

processing/smiles_to_selfies.py

@@ -0,0 +1,52 @@
+import pandas as pd
+from pandarallel import pandarallel
+import selfies as sf
+
+def to_selfies(smiles):
+    """
+    Converts SMILES to SELFIES representation.
+    If an error occurs, returns the original SMILES unchanged.
+    """
+    try:
+        return sf.encoder(smiles)
+    except sf.EncoderError:
+        print(f"EncoderError for SMILES: {smiles}")
+        return smiles
+
+def prepare_data(path, save_to):
+    """
+    Reads a dataset with SMILES, converts SMILES to SELFIES, and saves the result.
+    """
+    chembl_df = pd.read_csv(path, sep="\t")
+    chembl_df["selfies"] = chembl_df["canonical_smiles"]  # Copy the SMILES column
+
+    pandarallel.initialize()
+    chembl_df["selfies"] = chembl_df["selfies"].parallel_apply(to_selfies)
+    chembl_df.drop(chembl_df[chembl_df["canonical_smiles"] == chembl_df["selfies"]].index, inplace=True)
+    chembl_df.drop(columns=["canonical_smiles"], inplace=True)
+    chembl_df.to_csv(save_to, index=False)
+
+input_csv_path = "/home/g3bbmproject/main_folder/KG/kg.pt/our_10k_matched_data_with_embeddings.csv"
+output_csv_path = "data_with_selfies.csv"
+temp_smiles_path = "temp_smiles.csv"
+temp_selfies_path = "temp_selfies.csv"
+
+data = pd.read_csv(input_csv_path)
+
+# Save the SMILES column to a temporary file for conversion
+data[['smiles']].rename(columns={"smiles": "canonical_smiles"}).to_csv(temp_smiles_path, index=False, sep="\t")
+
+# Convert SMILES to SELFIES using the prepare_data function
+prepare_data(path=temp_smiles_path, save_to=temp_selfies_path)
+
+# Load the resulting SELFIES data
+selfies_data = pd.read_csv(temp_selfies_path)
+
+# Add the SELFIES column back to the original data
+data['selfies'] = selfies_data['selfies']  # Assumes the converted file has a 'selfies' column
+
+# Save the updated data to a new CSV file
+data.to_csv(output_csv_path, index=False)
+
+print(f'Total length of data: {len(data)}')
+print(f"Updated dataset with SELFIES saved to: {output_csv_path}")

processing/text_embedding.py

@@ -0,0 +1,96 @@
+import os
+import pandas as pd
+import torch
+import numpy as np
+from transformers import AutoTokenizer, AutoModel
+from tqdm import tqdm
+
+# Check for GPU availability
+device = torch.device("cuda:2" if torch.cuda.is_available() else "cpu")
+print(f"Using device: {device}")
+
+# Load SciBERT tokenizer and model
+scibert_tokenizer = AutoTokenizer.from_pretrained("allenai/scibert_scivocab_uncased")
+scibert_model = AutoModel.from_pretrained("allenai/scibert_scivocab_uncased").to(device)
+scibert_model.eval()  # Set the model to evaluation mode
+
+# Function to generate text embeddings for a single text
+def get_text_embeddings(text, tokenizer, model, device):
+    if isinstance(text, str) and text.strip() != "":
+        tokens = tokenizer.encode(
+            text,
+            add_special_tokens=True,
+            max_length=512,
+            padding=True,
+            truncation=True,
+            return_tensors="pt"
+        ).to(device)
+        with torch.no_grad():
+            output = model(tokens)
+        text_out = output[0][0].mean(dim=0)
+    else:
+        text_out = torch.zeros(768).to(device)
+    return text_out.cpu().numpy()
+
+# New function: Process all CSV files ending with 'filtered' in a folder and its subfolders
+def process_folder(folder_path):
+    """
+    Walk through the folder and process each CSV file ending with 'filtered'.
+    Embeddings are saved in the same folder with '_text_embedding.npy' added to the base filename.
+    """
+    for root, dirs, files in os.walk(folder_path):
+        for file in files:
+            if file.endswith("filtered.csv"):
+                filtered_path = os.path.join(root, file)
+                base_filename = file.replace("_filtered", "")
+                full_path = os.path.join(root, base_filename)
+
+                if not os.path.exists(full_path):
+                    print(f"Corresponding full CSV file not found for {filtered_path}, skipping.\n")
+                    continue
+
+                print(f"Processing file: {filtered_path}")
+
+                try:
+                    # Read both full and filtered CSV files
+                    df_full = pd.read_csv(full_path)
+                    df_filtered = pd.read_csv(filtered_path)
+
+                    '''if ("smiles" not in df_full.columns or "smiles" not in df_filtered.column):
+                        raise ValueError("'smiles' column not found in one of the CSV files.")
+
+                    if "Description" not in df_filtered.columns:
+                        raise ValueError("'Description' column not found in filtered CSV file.")'''
+
+                    # Map smiles to description
+
+                    column_name = 'smiles'
+                    if base_filename == 'bace':
+                        column_name = 'mol'
+
+                    smiles_to_description = dict(zip(df_filtered["smiles"], df_filtered["Description"]))
+
+                    # Prepare Description column for full df (may include Nones)
+                    df_full["Description"] = df_full[column_name].map(smiles_to_description)
+
+                    # Now generate embeddings
+                    tqdm.pandas(desc=f"Embedding {base_filename}")
+                    embeddings = df_full["Description"].progress_apply(
+                        lambda text: get_text_embeddings(text, scibert_tokenizer, scibert_model, device)
+                    ).tolist()
+
+                    embeddings_array = np.array(embeddings)
+
+                    output_file = os.path.join(root, f"{os.path.splitext(base_filename)[0]}_text_embedding.npy")
+                    np.save(output_file, embeddings_array)
+
+                    print(f"Saved embeddings to {output_file}\n")
+
+                except Exception as e:
+                    print(f"Failed to process {filtered_path}: {e}\n")
+
+# Example usage
+folder_path = "/home/g3-bbm-project/main_folder/FineTune/finetune_data_multi/finetuning_datasets/classification"  # Set your top-level folder here
+print(f"Starting to process folder: {folder_path}")
+process_folder(folder_path)
+print("Folder processing complete.")