Datasets:

MOSAIC-UCSD
/

MOSAIC_conditional_motif

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English

Tags:

molecular-generation

conditional-generation

graph-tokenization

chemistry

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Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
dataset: string
tokenizer_type: string
motif_name: string
motif_smiles_prompt: string
motif_smarts: string
checkpoint: string
prompt_length_tokens: int64
k_target_decodable: int64
max_attempts: int64
num_attempted: int64
num_decodable: int64
num_decode_failures: int64
num_rdkit_valid: int64
num_motif_match: int64
decode_rate: double
rdkit_validity_of_decodable: double
motif_retention_of_valid: double
elapsed_sec: double
sampling: struct<top_k: int64, temperature: double, max_length: int64, batch_size: int64, seed: int64>
  child 0, top_k: int64
  child 1, temperature: double
  child 2, max_length: int64
  child 3, batch_size: int64
  child 4, seed: int64
indole: struct<smarts: string, n_ref: int64>
  child 0, smarts: string
  child 1, n_ref: int64
pyridine: struct<smarts: string, n_ref: int64>
  child 0, smarts: string
  child 1, n_ref: int64
benzene: struct<smarts: string, n_ref: int64>
  child 0, smarts: string
  child 1, n_ref: int64
cyclohexane: struct<smarts: string, n_ref: int64>
  child 0, smarts: string
  child 1, n_ref: int64
cyclopentane: struct<smarts: string, n_ref: int64>
  child 0, smarts: string
  child 1, n_ref: int64
naphthalene: struct<smarts: string, n_ref: int64>
  child 0, smarts: string
  child 1, n_ref: int64
to
{'benzene': {'smarts': Value('string'), 'n_ref': Value('int64')}, 'pyridine': {'smarts': Value('string'), 'n_ref': Value('int64')}, 'naphthalene': {'smarts': Value('string'), 'n_ref': Value('int64')}, 'indole': {'smarts': Value('string'), 'n_ref': Value('int64')}, 'cyclohexane': {'smarts': Value('string'), 'n_ref': Value('int64')}, 'cyclopentane': {'smarts': Value('string'), 'n_ref': Value('int64')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              dataset: string
              tokenizer_type: string
              motif_name: string
              motif_smiles_prompt: string
              motif_smarts: string
              checkpoint: string
              prompt_length_tokens: int64
              k_target_decodable: int64
              max_attempts: int64
              num_attempted: int64
              num_decodable: int64
              num_decode_failures: int64
              num_rdkit_valid: int64
              num_motif_match: int64
              decode_rate: double
              rdkit_validity_of_decodable: double
              motif_retention_of_valid: double
              elapsed_sec: double
              sampling: struct<top_k: int64, temperature: double, max_length: int64, batch_size: int64, seed: int64>
                child 0, top_k: int64
                child 1, temperature: double
                child 2, max_length: int64
                child 3, batch_size: int64
                child 4, seed: int64
              indole: struct<smarts: string, n_ref: int64>
                child 0, smarts: string
                child 1, n_ref: int64
              pyridine: struct<smarts: string, n_ref: int64>
                child 0, smarts: string
                child 1, n_ref: int64
              benzene: struct<smarts: string, n_ref: int64>
                child 0, smarts: string
                child 1, n_ref: int64
              cyclohexane: struct<smarts: string, n_ref: int64>
                child 0, smarts: string
                child 1, n_ref: int64
              cyclopentane: struct<smarts: string, n_ref: int64>
                child 0, smarts: string
                child 1, n_ref: int64
              naphthalene: struct<smarts: string, n_ref: int64>
                child 0, smarts: string
                child 1, n_ref: int64
              to
              {'benzene': {'smarts': Value('string'), 'n_ref': Value('int64')}, 'pyridine': {'smarts': Value('string'), 'n_ref': Value('int64')}, 'naphthalene': {'smarts': Value('string'), 'n_ref': Value('int64')}, 'indole': {'smarts': Value('string'), 'n_ref': Value('int64')}, 'cyclohexane': {'smarts': Value('string'), 'n_ref': Value('int64')}, 'cyclopentane': {'smarts': Value('string'), 'n_ref': Value('int64')}}
              because column names don't match

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MOSAIC: Conditional Motif Generation

Conditional generation samples + metrics for the three MOSAIC tokenizers (SENT, HDT-MC, HDTC) prompted with each of 6 shared ring/aromatic motifs, across MOSES, GuacaMol, and COCONUT.

The conditioning prompt for each model is the standalone-motif tokenization, with closing tokens stripped so the model continues from "the molecule starts with this motif." For HDT-MC, the outer ENTER block is left open so the model can add additional communities; for HDTC, the trailing super-graph block is stripped so the model can add more typed communities.

Layout

{dataset}__AVG.png — per-dataset metric table averaged over the 6 motifs
{dataset}__AVG__grid.png — per-dataset 6×5 visual grid (one row per motif, one column per model, plus reference + motif primer)
{dataset}/_reference/{motif}.txt — k motif-containing reference SMILES drawn from training (used as the comparison set for SNN / Frag / motif-distribution MMDs)
{dataset}/{motif}/{dataset}__{motif}.png — per-cell metric table
{dataset}/{motif}/{dataset}__{motif}__grid.png — per-cell 5-column visual grid (Reference | Motif Primer | SENT | HDT-MC | HDTC)
{dataset}/{motif}/{model}/generated_smiles.txt — k=100 conditional generations
{dataset}/{motif}/{model}/generated_metadata.json — gen-time stats (prompt token sequence, decode rate, motif retention, sampling params, elapsed)
{dataset}/{motif}/{model}/metrics.json — full computed metrics

Motifs

Name	SMARTS / SMILES
`benzene`	`c1ccccc1`
`pyridine`	`c1ccncc1`
`naphthalene`	`c1ccc2ccccc2c1`
`indole`	`c1ccc2[nH]ccc2c1`
`cyclohexane`	`C1CCCCC1`
`cyclopentane`	`C1CCCC1`

Datasets

moses — MOSES drug-like (training set ~1.6M)
guacamol — GuacaMol drug-like (test split fallback, ~941 reference SMILES)
coconut — COCONUT natural products (training set ~10K)

Models

Listed in the column order used by the rendered tables (flat → unsupervised hierarchies → supervised hierarchy → typed hierarchy):

sent — flat random-walk tokenizer (SENT)
hdt_lou — HDT with Louvain community coarsening (HDT-Lou)
hdt_hac — HDT with HAC-avg community coarsening (HDT-HAC)
hdt — HDT with motif-community coarsening (HDT-MC)
hdtc — HDT-Compositional, typed two-level (RING / FUNC / SINGLETON)

All models are gpt2xs (~11M params) trained with next-token prediction. Per-cell sample budget: k=100 decodable molecules per (dataset, model, motif), obtained via rejection sampling capped at 800 attempts.

Pipeline

Generation/metric/render code lives in the MOSAIC repo at scripts/conditional_motif/.

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Total file size:

23.6 MB