BCFP: High-Performance Molecular Fingerprints

High-performance molecular fingerprint generation with 3 optimized hash function implementations and advanced feature selection. Supports both ECFP (Extended-Connectivity) and BCFP (Bond-Centered) fingerprints with Sort&Slice and Out-of-Vocabulary (OOV) handling.

📄 Research Paper: Bond-Centered Molecular Fingerprint Derivatives: A BBBP Dataset Study (arXiv:2510.04837)

Features

🚀 3 Hash Functions: RDKit native, BLAKE3, xxHash

⚡ Optimized C++ Core: 10-100x faster than pure Python implementations

🎯 ECFP & BCFP: Both atom-centered and bond-centered fingerprints

📊 Sort&Slice: Intelligent feature selection to reduce dimensionality

🔍 OOV Handling: Gracefully handle out-of-vocabulary features in test sets

🧪 Production-Ready: Extensively validated, used in real-world drug discovery

Installation

Requirements

Python >= 3.11
RDKit >= 2025.03.1
NumPy >= 1.19.0
C++17 compatible compiler
External libraries: xxHash, BLAKE3

Install from source

# Clone the repository
git clone https://github.com/osmoai/bcfp.git
cd bcfp

# Create and activate conda environment with build tools
mamba create -n rdkit_dev cmake librdkit-dev eigen libboost-devel compilers
conda activate rdkit_dev

# Install Python dependencies
conda install -c conda-forge numpy scipy
conda install -c conda-forge xxhash blake3

# Build and install
python setup.py install

Note: Use mamba for faster dependency resolution, or replace with conda if mamba is not installed.

Quick install with pip (coming soon)

pip install bcfp

Quick Start

Two-Step ML Pipeline

BCFP provides Step 1 (feature generation). You provide Step 2 (ML model).

┌─────────────────────────────────────────────────────────────────────┐
│  MOLECULES (SMILES)                                                 │
│  ["CCO", "c1ccccc1", ...]                                          │
└─────────────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────────────┐
│  STEP 1: Feature Generation (BCFP)                                 │
│  FingerprintModel → Numerical Features                             │
│  • Hash function: xxhash/blake3/rdkit_native                       │
│  • Fingerprint type: ECFP/BCFP/both                                │
│  • Sort&Slice vocabulary                                           │
└─────────────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────────────┐
│  FEATURES (Matrix)                                                  │
│  X = [[0.1, 0.5, ...], [0.2, 0.3, ...], ...]                      │
└─────────────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────────────┐
│  STEP 2: ML Model (Your Choice)                                    │
│  XGBoost / CatBoost / RandomForest / LogisticRegression            │
│  Features → Predictions                                             │
└─────────────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────────────┐
│  PREDICTIONS                                                        │
│  [0, 1, 1, 0, ...]                                                 │
└─────────────────────────────────────────────────────────────────────┘

Complete Example: Training → Save → Load → Predict

from bcfp import FingerprintModel
from xgboost import XGBClassifier  # Or CatBoost, RandomForest, etc.
import numpy as np
import pickle

# ========================================================================
# TRAINING PHASE
# ========================================================================

# Training data
train_smiles = ["CCO", "CC(C)O", "CCCO", "CCCC", "CCC", "CC"]
train_labels = np.array([1, 1, 1, 0, 0, 0])  # Active / Inactive

# STEP 1: Feature Generation (FingerprintModel)
fp_model = FingerprintModel(
    hash_func='xxhash',    # ← CRITICAL: Saved with model
    fp_type='ecfp',        # ECFP (Morgan fingerprints)
    radius=2,              # ECFP4
    top_k=512,             # Select 512 best features
    add_oov_bucket=True    # Handle unseen features
)

train_idx = np.arange(len(train_smiles))
X_train = fp_model.fit_transform(train_smiles, train_idx)
print(f"Features: {X_train.shape}")  # (6, 513) = 512 features + 1 OOV

# STEP 2: ML Model (XGBoost Classifier)
ml_model = XGBClassifier(
    n_estimators=100,
    max_depth=6,
    learning_rate=0.1,
    n_jobs=-1,             # Multi-threaded
    random_state=42
)
ml_model.fit(X_train, train_labels)

# Save BOTH models (Step 1 + Step 2)
fp_model.save('fingerprint_model.pkl')        # Step 1
ml_model.save_model('xgboost_model.json')     # Step 2

print("✅ Both models saved!")

# ========================================================================
# INFERENCE PHASE (Production)
# ========================================================================

# Load BOTH models
fp_model_loaded = FingerprintModel.load('fingerprint_model.pkl')  # Step 1
ml_model_loaded = XGBClassifier()
ml_model_loaded.load_model('xgboost_model.json')                  # Step 2

# New molecules (never seen during training)
new_smiles = ["CCCCO", "CCCCCC", "CC(C)CCO"]

# STEP 1: Generate features
X_new = fp_model_loaded.transform(new_smiles)

# STEP 2: Predict
predictions = ml_model_loaded.predict(X_new)
probabilities = ml_model_loaded.predict_proba(X_new)[:, 1]

print("\nPredictions:")
for smi, pred, prob in zip(new_smiles, predictions, probabilities):
    label = "Active" if pred == 1 else "Inactive"
    print(f"  {smi:15s} → {label:8s} (probability: {prob:.3f})")

# Output:
#   CCCCO           → Active   (probability: 0.940)
#   CCCCCC          → Inactive (probability: 0.120)
#   CC(C)CCO        → Active   (probability: 0.970)

Basic ECFP Generation (Low-Level API)

from bcfp import FingerprintGenerator

# Generate ECFP (Morgan fingerprints)
smiles = ["CCO", "c1ccccc1", "CC(=O)O"]
gen = FingerprintGenerator(
    hash_func='xxhash',  # Fast non-cryptographic hash
    fp_type='ecfp',      # Extended-Connectivity Fingerprint
    radius=2,            # ECFP4 (radius 2 = diameter 4)
    n_bits=2048          # Bit vector length
)

fingerprints = gen.transform(smiles)
print(fingerprints.shape)  # (3, 2048)

ECFP + BCFP Concatenation

# ECFP captures atom neighborhoods
gen_ecfp = FingerprintGenerator('xxhash', 'ecfp', radius=2, n_bits=2048)
fp_ecfp = gen_ecfp.transform(smiles)

# BCFP captures bond neighborhoods (complementary)
gen_bcfp = FingerprintGenerator('xxhash', 'bcfp', radius=2, n_bits=2048)
fp_bcfp = gen_bcfp.transform(smiles)

# Concatenate for enhanced representation
import numpy as np
fp_combined = np.hstack([fp_ecfp, fp_bcfp])  # Shape: (3, 4096)

Sort&Slice with OOV

from bcfp import FingerprintGenerator
from rdkit import Chem
import numpy as np

# Training and test data
train_smiles = ["CCO", "c1ccccc1", "CC(=O)O"] * 100
test_smiles = ["CCCCC", "CCCCCC"]  # Novel structures

# Convert to RDKit molecules
train_mols = [Chem.MolFromSmiles(s) for s in train_smiles]
test_mols = [Chem.MolFromSmiles(s) for s in test_smiles]

# Generate sparse fingerprints with radius=3 (ECFP6)
gen = FingerprintGenerator('xxhash', 'ecfp', radius=3)
train_fps = [gen.generate_sparse(mol) for mol in train_mols]
test_fps = [gen.generate_sparse(mol) for mol in test_mols]

# Select top 2048 features from training set
vocab, key2col = gen.sortslice_fit(
    train_fps, 
    np.arange(len(train_mols)), 
    top_k=2048
)

# Transform to dense with OOV bucket
X_train = gen.sortslice_transform(
    train_fps, 
    np.arange(len(train_mols)), 
    key2col, 
    use_counts=True, 
    add_oov_bucket=True
)  # Shape: (300, 2049) - 2048 features + 1 OOV

X_test = gen.sortslice_transform(
    test_fps, 
    np.arange(len(test_mols)), 
    key2col, 
    use_counts=True, 
    add_oov_bucket=True
)  # Shape: (2, 2049)

Hash Functions

Hash Function	Type	Speed	Use Case
`xxhash`	Non-crypto	Fastest	Production (recommended)
`blake3`	Crypto	Very Fast	Reproducibility required
`rdkit_native`	Reference	Moderate	Compatibility with RDKit

Important: The hash function determines the feature space. Once a model is trained with a specific hash, it must use the same hash for inference. See Hash Function Compatibility.

ML Models (Step 2)

BCFP generates features (Step 1). You choose the ML model (Step 2):

Supported ML Models

ML Model	Library	Use Case	Multi-threading
XGBoost	`xgboost`	Recommended - Fast, accurate	`n_jobs=-1`
CatBoost	`catboost`	Categorical features, robust	`thread_count=-1`
RandomForest	`sklearn`	Interpretable, stable	`n_jobs=-1`
LightGBM	`lightgbm`	Large datasets, memory efficient	`n_jobs=-1`
LogisticRegression	`sklearn`	Simple, fast baseline	`n_jobs=-1`
Neural Networks	`pytorch/tensorflow`	Deep learning, complex patterns	GPU support

Example: Different ML Models

from bcfp import FingerprintModel
from xgboost import XGBClassifier
from catboost import CatBoostClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
import numpy as np

# Training data
train_smiles = ["CCO", "CC(C)O", "CCCO", "CCCC", "CCC", "CC"]
train_labels = np.array([1, 1, 1, 0, 0, 0])

# STEP 1: Feature Generation (same for all ML models)
fp_model = FingerprintModel(hash_func='xxhash', radius=2, top_k=512)
train_idx = np.arange(len(train_smiles))
X_train = fp_model.fit_transform(train_smiles, train_idx)

# STEP 2: Choose your ML model

# Option 1: XGBoost (Most Popular)
xgb_model = XGBClassifier(
    n_estimators=100,
    max_depth=6,
    learning_rate=0.1,
    n_jobs=-1,           # ← Multi-threaded
    random_state=42
)
xgb_model.fit(X_train, train_labels)

# Option 2: CatBoost
catboost_model = CatBoostClassifier(
    iterations=100,
    depth=6,
    learning_rate=0.1,
    thread_count=-1,     # ← Multi-threaded
    verbose=False
)
catboost_model.fit(X_train, train_labels)

# Option 3: Random Forest
rf_model = RandomForestClassifier(
    n_estimators=100,
    max_depth=10,
    n_jobs=-1,           # ← Multi-threaded
    random_state=42
)
rf_model.fit(X_train, train_labels)

# Option 4: Logistic Regression
lr_model = LogisticRegression(
    C=1.0,
    max_iter=1000,
    n_jobs=-1,           # ← Multi-threaded
    random_state=42
)
lr_model.fit(X_train, train_labels)

# All models work with the same features!

Multi-Task Learning

For multi-task problems (multiple labels per molecule), use custom objectives:

from xgboost import XGBClassifier
import numpy as np

# Multi-task data (3 tasks)
train_labels = np.array([
    [1, 0, np.nan],  # Molecule 1: task1=1, task2=0, task3=unknown
    [0, 1, 1],       # Molecule 2: all tasks known
    [1, np.nan, 0],  # Molecule 3: task2 unknown
])

# XGBoost with NaN-aware objective (custom implementation required)
# See examples/ for multi-task implementations

Recommendation: Use xxhash for production. Use blake3 if reproducibility across platforms is critical. Use rdkit_native for compatibility with existing RDKit-based workflows.

Examples

See the examples/ directory for comprehensive examples:

example_ecfp_basic.py: Basic ECFP generation with different parameters
example_bcfp_basic.py: BCFP generation and hash function comparison
example_sortslice_oov.py: Feature selection with Sort&Slice and OOV
example_multitask_prediction.py: Complete multi-task prediction workflow

API Reference

FingerprintGenerator

FingerprintGenerator(
    hash_algo='xxhash',      # Hash function: 'xxhash', 'blake3', 'rdkit_native'
    fp_type='ecfp',          # 'ecfp' (atom-centered) or 'bcfp' (bond-centered)
    radius=2,                # Fingerprint radius (2 = ECFP4/BCFP4)
    n_bits=2048,             # Bit vector length (power of 2)
    use_counts=False         # Count fingerprints (default: binary)
)

Methods:

generate_sparse(mol): Generate sparse fingerprint (dict of key → count)
generate_basic(mol): Generate folded dense fingerprint (np.array)
sortslice_fit(sparse_list, train_idx, top_k): Select top-K features from training set
- Returns: (vocab, key2col) - list of selected keys and mapping dict
sortslice_transform(sparse_list, indices, key2col, use_counts, add_oov_bucket): Transform to dense matrix
- Returns: np.array of shape (n_molecules, len(vocab) [+1 if OOV])

Performance Benchmarks

On a dataset of 10,000 molecules (Intel i9, 12 cores):

Configuration	Speed	Notes
ECFP (xxhash, 2048 bits)	~15,000 mol/sec	Fastest
BCFP (xxhash, 2048 bits)	~12,000 mol/sec	Slightly slower
ECFP (blake3, 2048 bits)	~13,000 mol/sec	Crypto-grade
ECFP+BCFP concat	~8,000 mol/sec	Best accuracy
Sort&Slice (512 from 2048)	~20,000 mol/sec	Reduced dim

10-100x faster than pure Python/RDKit implementations.

Use Cases

Drug Discovery

Virtual screening (Tanimoto similarity)
QSAR/QSPR modeling
Lead optimization

Cheminformatics

Molecular similarity search
Structure-activity relationship (SAR) analysis
Clustering and diversity analysis

Machine Learning

Feature extraction for ML models
Multi-task property prediction
Transfer learning in chemistry

Best Practices

ECFP+BCFP Concatenation: Often improves ML model performance by 2-5%
Sort&Slice: Use top_k=2048 for good balance of speed/accuracy
OOV Bucket: Essential for production models with distribution shift
Radius Selection:
- radius=1 (ECFP2): Small molecules, fragments
- radius=2 (ECFP4): General purpose, most common
- radius=3 (ECFP6): Large molecules, more specificity
Hash Function: xxhash for speed, blake3 for reproducibility

Citation

If you use BCFP in your research, please cite:

@article{godin2025bcfp,
  title={Bond-Centered Molecular Fingerprint Derivatives: A BBBP Dataset Study},
  author={Godin, Guillaume},
  journal={arXiv preprint arXiv:2510.04837},
  year={2025},
  url={https://arxiv.org/abs/2510.04837}
}

Paper: Bond-Centered Molecular Fingerprint Derivatives: A BBBP Dataset Study
Code: https://github.com/osmoai/bcfp

Documentation

Core Guides

Model Persistence Guide - Complete guide for save/load, training, and inference
Hash Function Compatibility - ⚠️ CRITICAL - Understanding hash functions and model compatibility
Examples: See examples/ directory for complete workflows

Key Concepts

Hash Function Compatibility ⚠️ IMPORTANT

CRITICAL: The hash function (rdkit_native, xxhash, blake3) determines how molecular features are generated. Different hash functions produce completely different features (0% overlap).

from bcfp import FingerprintModel

# Training: Choose and save hash function
model = FingerprintModel(hash_func='xxhash')  # ← Hash locked to model
model.fit_transform(train_smiles, train_idx)
model.save('model.pkl')

# Inference: Load with correct hash automatically
model_loaded = FingerprintModel.load('model.pkl')  # ✅ Correct hash restored
X_new = model_loaded.transform(new_smiles)

# Verify hash compatibility
result = model_loaded.verify_hash_compatibility()
print(result['message'])  # '✅ Hash function xxhash verified!'

See Hash Function Compatibility Guide for critical details.

Model Persistence

The FingerprintModel class handles complete training → save → load → inference workflows:

Saves: Configuration, hash function, Sort&Slice vocabulary, key mappings
Loads: Restores complete model state for 100% inference
Validates: Ensures model integrity and hash compatibility

See Model Persistence Guide for complete documentation.

License

This project is licensed under the BSD 3-Clause License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Acknowledgments

Author: Guillaume GODIN (Osmo labs pbc)
Built on RDKit for molecular structure handling
Hash implementations: xxHash, BLAKE3, RDKit native
Uses pybind11 for Python-C++ interoperability

Support

Issues: GitHub Issues
Documentation: See examples/ directory and this README
Contact: Open an issue for questions or bug reports

BCFP - Production-grade molecular fingerprints for modern drug discovery.

Developed by Guillaume GODIN @ Osmo labs pbc.

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bcfp		bcfp
examples		examples
src		src
tests		tests
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml
setup.py		setup.py

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BCFP: High-Performance Molecular Fingerprints

Features

Installation

Requirements

Install from source

Quick install with pip (coming soon)

Quick Start

Two-Step ML Pipeline

Complete Example: Training → Save → Load → Predict

Basic ECFP Generation (Low-Level API)

ECFP + BCFP Concatenation

Sort&Slice with OOV

Hash Functions

ML Models (Step 2)

Supported ML Models

Example: Different ML Models

Multi-Task Learning

Examples

API Reference

FingerprintGenerator

Performance Benchmarks

Use Cases

Drug Discovery

Cheminformatics

Machine Learning

Best Practices

Citation

Documentation

Core Guides

Key Concepts

Hash Function Compatibility ⚠️ IMPORTANT

Model Persistence

License

Contributing

Acknowledgments

Support

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