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USearch Molecules

USearch Molecules 7B dataset thumbnail

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 molecules from the PubChem dataset.
  • 977'468'301 molecules from the GDB13 dataset.
  • 6'039'411'651 molecules from the Enamine 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

Dataset Structure

.
├── 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.md

Pre-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 null

In 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 MB

Installation

The project supports multiple installation profiles for different use cases.

Option 1: UV (Pure Python, Recommended for CPU-only)

We recommend using uv for fast, reliable Python dependency management.

git clone https://github.com/ashvardanian/USearchMolecules.git
cd USearchMolecules

uv venv --python 3.12                   # or your preferred Python version
source .venv/bin/activate               # to activate the virtual environment
uv pip install setuptools wheel         # to pull the latest build tools
uv pip install -e . --force-reinstall   # to build locally from source
uv pip install -e ".[dev]"              # for fingerprinting & generation of indexes
uv pip install -e ".[gpu]"              # for GPU-accelerated processing with nvMolKit
uv pip install -e ".[viz]"              # for visualization with StreamLit
uv pip install -e ".[all]"              # for all features

Or install from PyPI:

uv pip install usearch-molecules
uv pip install "usearch-molecules[dev]" # for fingerprinting & generation of indexes
uv pip install "usearch-molecules[gpu]" # for GPU-accelerated processing with nvMolKit
uv pip install "usearch-molecules[viz]" # for visualization with StreamLit
uv pip install "usearch-molecules[all]" # for all features

Option 2: Pixi (Conda-based, Recommended for GPU acceleration)

For GPU acceleration with nvMolKit, we recommend using pixi which handles conda dependencies (RDKit, nvMolKit) seamlessly:

pixi install
pixi run python -m usearch_molecules.prep_conformers --datasets example --use-gpu --conformers 20 --batch-size 20

Usage

Exploring Dataset via Command Line Interface

Download the example dataset (2M molecules):

mkdir -p 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 nodes

With 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]

Exploring Dataset via Graphical Interface

The dataset also comes with a graphical sandbox implemented with StreamLit and 3DMol.js to help visualize similarities between molecules.

streamlit run streamlit_app.py

USearch Molecules StreamLit demo preview

Methodology

Dataset Sources

Original data came from:

Pipeline

The data processing pipeline consists of 5 steps, each implemented as a standalone script:

  1. prep_parquet.py: Convert raw datasets into standardized Parquet shards with SMILES strings.
  2. prep_encode.py: Add molecular fingerprints (MACCS, ECFP4, FCFP4, PubChem) to Parquet files.
  3. prep_index.py: Build USearch similarity indexes for fast nearest neighbor search.
  4. prep_conformers.py: Generate 3D conformers using ETKDG and optionally optimize with MMFF94.
  5. prep_smiles.py: Export SMILES strings to newline-delimited .smi files for 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.

uv run python -m usearch_molecules.prep_parquet --datasets example
uv run python -m usearch_molecules.prep_encode --datasets example
uv run python -m usearch_molecules.prep_index --datasets example
uv run python -m usearch_molecules.prep_smiles --datasets example
uv run python -m usearch_molecules.prep_conformers --datasets example

Once 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/

About

Searching for structural similarities across billions of molecules in milliseconds

ashvardanian.com/posts/usearch-molecules

Topics

bioinformatics chemistry cheminformatics biology aws-s3 opendata open-data compchem drug-discovery vector-search vector-search-engine

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