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Converted to Selfies & Parquet

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  1. .gitattributes +1 -0
  2. gdb/ChEMBL.FL.smi → Kaggle/SMILES_Big_Data_Set.parquet +2 -2
  3. Kaggle/{chembl_22_clean_1576904_sorted_std_final.smi → chembl_22_clean_1576904_sorted_std_final.parquet} +2 -2
  4. Tokenizer.md +107 -0
  5. emolecules/{orderbb/2024-10-01/parent.smi → free/2024-10-01/version.parquet} +2 -2
  6. emolecules/orderbb/2024-10-01/parent.parquet +3 -0
  7. emolecules/orderbb/2024-10-01/version.parquet +3 -0
  8. emolecules/orderbb/2024-10-01/version.smi +0 -3
  9. emolecules/ordersc/2024-10-01/parent.parquet +3 -0
  10. emolecules/ordersc/2024-10-01/parent.part01.smi +0 -3
  11. emolecules/ordersc/2024-10-01/version.parquet +3 -0
  12. gdb/ChEMBL.FL.parquet +3 -0
  13. gdb/FDB-17-fragmentset.parquet +3 -0
  14. gdb/FDB-17-fragmentset.smi +0 -3
  15. gdb/{TasteDB.smi → FragranceDB.FL.parquet} +2 -2
  16. gdb/{FragranceDB.smi → FragranceDB.parquet} +2 -2
  17. emolecules/free/2024-10-01/version.part01.smi → gdb/GDB-13s.parquet +2 -2
  18. gdb/GDB-13s.part01.smi +0 -3
  19. gdb/GDB-13s.part02.smi +0 -3
  20. gdb/GDB-13s.part03.smi +0 -3
  21. gdb/GDB13_Subset-AB.parquet +3 -0
  22. gdb/GDB13_Subset-AB.part01.smi +0 -3
  23. gdb/GDB13_Subset-AB.part02.smi +0 -3
  24. gdb/GDB13_Subset-AB.part03.smi +0 -3
  25. gdb/GDB13_Subset-AB.part05.smi +0 -3
  26. gdb/GDB13_Subset-AB.part06.smi +0 -3
  27. gdb/GDB13_Subset-AB.part07.smi +0 -3
  28. gdb/GDB13_Subset-AB.part08.smi +0 -3
  29. gdb/GDB13_Subset-AB.part09.smi +0 -3
  30. gdb/GDB13_Subset-AB.part10.smi +0 -3
  31. gdb/GDB13_Subset-AB.part11.smi +0 -3
  32. gdb/GDB13_Subset-AB.part12.smi +0 -3
  33. gdb/GDB13_Subset-AB.part13.smi +0 -3
  34. gdb/GDB13_Subset-AB.part14.smi +0 -3
  35. gdb/GDB13_Subset-AB.part15.smi +0 -3
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  37. gdb/GDB17.50000000.parquet +3 -0
  38. gdb/GDB17.50000000.part01.smi +0 -3
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  40. gdb/GDB4c3D.parquet +3 -0
  41. gdb/GDBChEMBL.parquet +3 -0
  42. gdb/GDBMedChem.parquet +3 -0
  43. gdb/GDBMedChem.smi +0 -3
  44. gdb/PubChem.FL.parquet +3 -0
  45. gdb/PubChem.FL.smi +0 -3
  46. gdb/TasteDB.parquet +3 -0
  47. gdb/ZINC.FL.parquet +3 -0
  48. gdb/ZINC.FL.smi +0 -3
  49. gdb/gdb11.parquet +3 -0
  50. gdb/gdb11/gdb11_size05.smi +0 -3
.gitattributes CHANGED
@@ -33,6 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.xz filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
 
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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  # Audio files - uncompressed
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  *.pcm filter=lfs diff=lfs merge=lfs -text
 
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  *.xz filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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+ *.zstd filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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  # Audio files - uncompressed
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  *.pcm filter=lfs diff=lfs merge=lfs -text
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Tokenizer.md ADDED
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+ # SELFIES Tokenizer Documentation
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+
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+ ## Overview
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+ This tokenizer is designed specifically for SELFIES (Self-Referencing Embedded Strings) chemical structure notation. It provides a simple mapping between SELFIES tokens and their corresponding IDs for machine learning applications.
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+
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+ ## File Format
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+ The tokenizer configuration is stored in a JSON file with the following structure:
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+ ```json
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+ {
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+ "vocab": {
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+ "[UNK]": 0,
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+ "[CLS]": 1,
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+ "[SEP]": 2,
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+ "[PAD]": 3,
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+ "[MASK]": 4,
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+ // ... chemical tokens ...
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+ },
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+ "special_tokens": {
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+ "unk_token": "[UNK]",
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+ "cls_token": "[CLS]",
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+ "sep_token": "[SEP]",
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+ "pad_token": "[PAD]",
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+ "mask_token": "[MASK]"
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+ },
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+ "model_max_length": 512
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+ }
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+ ```
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+
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+ ## Special Tokens
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+ - `[UNK]`: Used for unknown tokens (ID: 0)
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+ - `[CLS]`: Classification token, added at start of sequence (ID: 1)
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+ - `[SEP]`: Separator token, added at end of sequence (ID: 2)
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+ - `[PAD]`: Padding token (ID: 3)
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+ - `[MASK]`: Masking token for masked language modeling (ID: 4)
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+
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+ ## Tokenization Process
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+ Since SELFIES strings are already tokenized by design, the process is straightforward:
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+
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+ 1. Split the SELFIES string into tokens (they are naturally delimited by `[]`)
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+ 2. Convert each token to its corresponding ID using the vocab mapping
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+ 3. Add special tokens as needed ([CLS] at start, [SEP] at end)
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+ 4. Pad/truncate to model_max_length if necessary
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+
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+ Example implementation:
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+ ```python
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+ import re
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+
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+ class SelfiesTokenizer:
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+ def __init__(self, tokenizer_path):
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+ with open(tokenizer_path, 'r') as f:
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+ config = json.load(f)
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+ self.vocab = config['vocab']
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+ self.special_tokens = config['special_tokens']
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+ self.max_length = config['model_max_length']
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+ # Create reverse mapping (id -> token) for decoding
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+ self.id2token = {v: k for k, v in self.vocab.items()}
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+
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+ def tokenize(self, selfies_string):
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+ """Split SELFIES string into tokens"""
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+ return re.findall(r'\[.*?\]', selfies_string)
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+
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+ def convert_tokens_to_ids(self, tokens):
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+ """Convert tokens to their IDs"""
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+ return [self.vocab.get(token, self.vocab['[UNK]']) for token in tokens]
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+
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+ def encode(self, selfies_string, add_special_tokens=True):
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+ """Full encoding process"""
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+ tokens = self.tokenize(selfies_string)
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+ ids = self.convert_tokens_to_ids(tokens)
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+
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+ if add_special_tokens:
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+ ids = [self.vocab['[CLS]']] + ids + [self.vocab['[SEP]']]
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+
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+ # Truncate or pad as needed
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+ if len(ids) > self.max_length:
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+ ids = ids[:self.max_length]
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+ else:
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+ ids.extend([self.vocab['[PAD]']] * (self.max_length - len(ids)))
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+
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+ return ids
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+
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+ def decode(self, ids):
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+ """Convert IDs back to SELFIES string"""
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+ tokens = [self.id2token[id] for id in ids]
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+ # Remove special tokens
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+ tokens = [t for t in tokens if t not in self.special_tokens.values()]
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+ return ''.join(tokens)
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+ ```
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+
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+ ## Usage Example
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+ ```python
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+ # Initialize tokenizer
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+ tokenizer = SelfiesTokenizer('tokenizer.json')
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+
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+ # Encode a SELFIES string
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+ selfies = '[C][=C][C][=C][O][H]'
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+ ids = tokenizer.encode(selfies)
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+
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+ # Decode back to SELFIES
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+ decoded = tokenizer.decode(ids)
101
+ ```
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+
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+ ## Notes
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+ - The tokenizer assumes well-formed SELFIES strings as input
105
+ - No additional preprocessing is needed since SELFIES tokens are already well-defined
106
+ - The vocab mapping preserves IDs from the original tokenizer where possible
107
+ - Maximum sequence length is set to 512 tokens by default
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