USearch Molecules is a large Chem-Informatics dataset of small molecules. It includes 7'131'914'291 molecules with up to 50 "heavy" (non-hydrogen) atoms gathered from:
- 115'034'339 mecules from the PubChem dataset.
- 977'468'301 molules from the GDB13 dataset.
- 6'039'411'651 molules from thEnamine REAL dataset.
All molecules have been encoded using rdkit and cdk to produce binary fingerprints (structural embeddings) of four kinds:
- MACCS: Molecular ACCess System keys with 166 dimensions.
- PubChem: Structure Fingerprints with 881 dimensions.
- ECFP4: Extended Connectivity Fingerprint of diameter 4 with 2048 dimensions.
- FCFP4: Functional Class Fingerprint of diameter 4 with 2048 dimensions.
Those fingerprints were then indexed using Unum's USearch to enable real-time search and clustering of molecular structures for drug discovery and broader chemistry.
The dataset is included in AWS Open Data platform and is publicly available from the s3://usearch-molecules bucket, accessible even without AWS credentials, entirely anonymously:
aws s3 ls --no-sign-request s3://usearch-molecules.
├── data
│ ├── pubchem
│ │ ├── index-maccs.usearch # 18.6 GB
│ │ ├── index-maccs-ecfp4.usearch # 46.1 GB
│ │ └── parquet # 30 GB
│ │ ├── 0000000000-0001000000.parquet # 265 MB
│ │ ├── 0001000000-0002000000.parquet # 265 MB
│ │ ├── ...
│ │ └── 0115000000-0116000000.parquet # 177 MB
│ ├── gdb13
│ │ ├── index-maccs.usearch # 157.0 GB
│ │ ├── index-maccs-ecfp4.usearch # 390.1 GB
│ │ └── parquet # 189 GB
│ │ ├── 0000000000-0001000000.parquet # 198 MB
│ │ ├── 0001000000-0002000000.parquet # 198 MB
│ │ ├── ...
│ │ └── 0977000000-0978000000.parquet # 93 MB
│ └── real
│ └── parquet # 477 GB
│ ├── 0000000000-0001000000.parquet # 262 MB
│ ├── 0001000000-0002000000.parquet # 262 MB
│ ├── ...
│ └── 6039000000-6040000000.parquet # 108 MB
└── README.mdPre-constructed search and clustering indexes for the Enamine REAL dataset are much harder to distribute and deploy. Those are not yet available in the bucket but are available per request. To view the dataset structure, one can use Python:
$ pip install pyarrow
$ python
>>> import pyarrow.parquet as pq
>>> pq.read_table('data/real/parquet/0000000000-0001000000.parquet')
pyarrow.Table
smiles: string not null
maccs: fixed_size_binary[21] not null
pubchem: fixed_size_binary[111] not null
ecfp4: fixed_size_binary[256] not null
fcfp4: fixed_size_binary[256] not nullIn a tabular form that will look like:
smiles |
maccs |
pubchem |
ecfp4 |
fcfp4 |
|
|---|---|---|---|---|---|
| 0 | CNCC(C)NC(=O)C1(C(C)(C)OC)CC1 | 0x00000200000002002021227C488B9C02100615FFCC | 0x00733000000000000000000000001800000000000000000000000000000000000000001E00100000000E6CC18006020002C004000800011010000000000000000000810800000040160080001400000636008000000000000F80000000000000000000000000000000000000000000 | 0x40000000000000000000800000002400000000000000000000000000000000000000001000000200000000000000000000800000000000000000000000000000000000000002000000000002000000000020000000000100000000000000000000000000010000000040000000000000000000020000000800000000000000000000000048000000000000000000000280200000000000000000020000000000000000000000000000000100000000000000020000000000000000000400000001000000000000000000000000000000010004000000000000000000000800000000000000000000800000000000000400000000000000000010000020000000 | 0xE0001400000000000000000000000000000000000000000200000000000000000000000000000000000000000000000000000000000000000000001000401000000000000000000000000400000000000000000000001000000000000000000000100080000004000000000000000000000000000000000000000000000000000000000000000004000800000000000000000000000000001000000200000000000000000000000000000000000020000000000000000000000000000000000000000000000000000000000000000000004000000000000000001000000000000000000000080020000004000000000000000000000000000000000000000080 |
| 1 | CN(C(=O)C1=CC2=C(F)C=C(F)C=C2N1)C1CN(C(=O)CC2=CC=CN=C2O)C1 | 0x00900000002000004011172DAC534CE55EF3EB7FFC | 0x007BB1800000000000000000000000005801600000003C400000000000000001F000001F00100800000C28C19E0C3EC4F3C99200A8033577540082802037222008D921BC6CDC0866F2C295B394710864D611C8D987BE99809E00000000000200000000000000040000000000000000 | 0x00000000000001000000800000200100000100000000000000000000000000020000000000000000040000000008002000000000000000808000000000000000000200000000000000000001000000000020000000000014000000001000200100000000014040000000000000104000000000020100400000000000000040100000110040000000880000200000000000100000000000000400000000000000000000000000000104040000080000000000000000080000000100000000000000000000000000042000000000004000020000000000014000004200200000000000000000008000002040000000000400800000000000000000004001000000 | 0xBE800000000000000001000000000000000080000000080000000000000000000000000000000000000200000000000000000000000900000000000000010000000000010000000000020000000000000000000000000000000000200000000000000080080000000000000000000000040000008000000000002000000080000000000000400004000000000000000010000000000000000000000000000000000000400000000000000014000000000008000000000000000000000000000000000800000000000000000000000400080000000000001000400000000100000000000000000040004000000000002404000000000000000002020040003180 |
I've also added a tiny sample dataset under the data/example directory, with only 2 shards totaling 2 million entries, with pre-constructed indexes to simplify the entry.
Those come in handy if you want to test your application without downloading the whole dataset or visualize a few molecules using the StreamLit app.
.
└── data
└── example # 1.8 GB
├── index-maccs.usearch # 329 MB
├── index-maccs-ecfp4.usearch # 817 MB
├── parquet # 30 GB
│ ├── 0000000000-0001000000.parquet # 265 MB
│ └── 0001000000-0002000000.parquet # 265 MB
└── smiles # 30 GB
├── 0000000000-0001000000.smi # 58 MB
└── 0001000000-0002000000.smi # 58 MBFirst, install NumPy, RDKit, and USearch v2, and download the dataset:
pip3 install git+https://github.com/ashvardanian/usearch-molecules.git@main
mkdir -p data/pubchem data/gdb13 data/real data/example
aws s3 sync --no-sign-request s3://usearch-molecules/data/example data/example/If you need just one of the subsets:
aws s3 sync --no-sign-request s3://usearch-molecules/data/pubchem/ data/pubchem/
aws s3 sync --no-sign-request s3://usearch-molecules/data/gdb13/ data/gdb13/
aws s3 sync --no-sign-request s3://usearch-molecules/data/real/ data/real/You can immediately check if the indexes are readable:
$ python
>>> from usearch.index import Index
>>> Index.metadata("data/pubchem/index-maccs.usearch") # example of reading metadata
{'matrix_included': True,
'matrix_uses_64_bit_dimensions': False,
'version': '2.8.10',
'kind_metric': <MetricKind.Tanimoto: 116>,
'kind_scalar': <ScalarKind.B1: 1>,
'kind_key': <ScalarKind.U64: 8>,
'kind_compressed_slot': <ScalarKind.U32: 9>,
'count_present': 115627267,
'count_deleted': 0,
'dimensions': 192}
>>> Index.restore("data/pubchem/index-maccs-ecfp4.usearch") # example of parsing it
usearch.Index
- config
-- data type: ScalarKind.B1
-- dimensions: 2240
-- metric: MetricKind.Tanimoto
-- connectivity: 16
-- expansion on addition:128 candidates
-- expansion on search: 64 candidates
- binary
-- uses OpenMP: 1
-- uses SimSIMD: 1
-- uses hardware acceleration: avx512+popcnt
- state
-- size: 115,627,267 vectors
-- memory usage: 69,631,939,864 bytes
-- max level: 4
--- 0. 115,627,267 nodes
--- 1. 7,148,410 nodes
--- 2. 461,450 nodes
--- 3. 37,714 nodes
--- 4. 5,152 nodesWith those out of the way, you can now query the downloaded files:
from usearch_molecules.dataset import FingerprintedDataset, shape_mixed
data = FingerprintedDataset.open("data/example", shape=shape_mixed)
# No inspiration? Pick a random molecule with `data.random_smiles()`
results = data.search('CC(O)C(CN)=NNCC(C)(C)C', 100)
results_keys = [r[0] for r in results]
results_smiles = [r[1] for r in results]
results_scores = [r[2] for r in results]The dataset also comes with Graphical sandbox implemented with StreamLit and 3DMol.js, to help visualize similarities between molecules.
pip install streamlit stmol ipython_genutils
streamlit run streamlit_app.py
Original data came from:
- PubChem: CID-SMILES.gz
- GDB13: gdb13.tgz
- Enamine REAL, split by Heavy Atom Counts:
- HAC 6-21: CXSMILES.cxsmiles.bz2
- HAC 22-23: CXSMILES.cxsmiles.bz2
- HAC 24: CXSMILES.cxsmiles.bz2
- HAC 25: CXSMILES.cxsmiles.bz2
- HAC 26:
- CXSMILES Part 1.cxsmiles.bz2
- CXSMILES Part 2.cxsmiles.bz2
- HAC 27:
- CXSMILES Part 1.cxsmiles.bz2
- CXSMILES Part 2.cxsmiles.bz2
- HAC 28:
- CXSMILES Part 1.cxsmiles.bz2
- CXSMILES Part 2.cxsmiles.bz2
- HAC 29-38:
- CXSMILES Part 1.cxsmiles.bz2
- CXSMILES Part 2.cxsmiles.bz2
prep_schedule.py: convert and split datasets into standardized Parquet files.prep_encode.py: produce MACCS, PubChem, ECFP4, and FCFP4 fingerprints and index those.prep_smiles.py: export newline-delimited.smifiles to simplify navigation with StringZilla.
Every script is designed to work with bigger-than-memory data. In other words, processing 1 TB of molecules doesn't require 1 TB of RAM. Everything happens in a "gliding-window" fashion, with computationally intensive parts split between processes and threads.
python usearch_molecules/prep_schedule.py # Prepare Parquet files
python usearch_molecules/prep_encode.py # Build USearch indexes
python usearch_molecules/prep_smiles.py # Export SMILES new-line delimited files to simplify servingOnce completed, datasets have been uploaded to S3:
aws s3 sync data/pubchem/parquet/ s3://usearch-molecules/data/pubchem/parquet/
aws s3 sync data/gdb13/parquet/ s3://usearch-molecules/data/gdb13/parquet/
aws s3 sync data/real/parquet/ s3://usearch-molecules/data/real/parquet/