The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ParserError
Message: Error tokenizing data. C error: Expected 605 fields in line 287, saw 883
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 231, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3212, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2051, in _head
return _examples_to_batch(list(self.take(n)))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2226, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1677, in __iter__
for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 299, in __iter__
for key, pa_table in self.generate_tables_fn(**gen_kwags):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/csv/csv.py", line 190, in _generate_tables
for batch_idx, df in enumerate(csv_file_reader):
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
return self.get_chunk()
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
return self.read(nrows=size)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1923, in read
) = self._engine.read( # type: ignore[attr-defined]
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
chunks = self._reader.read_low_memory(nrows)
File "parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
File "parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
File "parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
File "parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
File "parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
pandas.errors.ParserError: Error tokenizing data. C error: Expected 605 fields in line 287, saw 883Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Details of AF3D_pLDDT_PChem3D_Shapes_Energy_Binding_2025_03_14 data:
De novo drug design: Energy minimization is used to generate new drug molecules from scratch based on the 3D structure of the target receptor Energy minimization, also known as geometry optimization.
9329 Rows of data provided out of total of 14109 rows, Columns filtered to remove null values.
Duplicate "PDB_protein_key" mapped to same duplicate "AF 3D AtomicData"(along with all data in rows) due to the many to many relationship between Drugs, accession pathways and Genes.
Duplicate "drug_name" mapped to same duplicate "PubChem_3D_Coordinates_With_Atoms"(along with all data in rows) to map with Alpha Fold output.
one_accession field indicated one or multiple ascension pathways as of Nov 1, 2023 Approx 600 proteins included with provided data with value "False" under one_accession field indicating multiple accession pathways per Gene The Next release will have current full data on ascension pathways creating more possibilities for bootstrapping due to large number of rows. Resampling data with replacement creating confidence intervals from the columns of significance. In Newer "thinking" models these bootstrapped confidence intervals could be used to enhance model output via "chain of thought" (EG. apply confidence intervals to "chain of thought" = "confidence intervals with chain of thought"). ?Similar to multiple sequence alignment with confidence intervals or energy minimization?
Data could also be used for Basic Training or RL.
More detail to follow as AI works the problem.
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