chemap 0.2.1

Library for computing molecular fingerprint based similarities as well as dimensionality reduction based chemical space visualizations.

chemap - Mapping chemical space

Library for computing molecular fingerprint based similarities as well as dimensionality reduction based chemical space visualizations.

Fingerprint computations

Fingerprints can be computed using generators from RDKit or scikit-fingerprints. Here a code example:

import numpy as np
import scipy.sparse as sp
from rdkit.Chem import rdFingerprintGenerator
from skfp.fingerprints import MAPFingerprint, AtomPairFingerprint

from chemap import compute_fingerprints, DatasetLoader, FingerprintConfig


ds_loader = DatasetLoader()
smiles = ds_loader.load("tests/data/smiles.csv")

# ----------------------------
# RDKit: Morgan (folded, dense)
# ----------------------------
morgan = rdFingerprintGenerator.GetMorganGenerator(radius=3, fpSize=4096)
X_morgan = compute_fingerprints(
    smiles,
    morgan,
    config=FingerprintConfig(
        count=False,
        folded=True,
        return_csr=False,   # dense numpy
        invalid_policy="raise",
    ),
)
print("RDKit Morgan:", X_morgan.shape, X_morgan.dtype)

# -----------------------------------
# RDKit: RDKitFP (folded, CSR sparse)
# -----------------------------------
rdkitfp = rdFingerprintGenerator.GetRDKitFPGenerator(fpSize=4096)
X_rdkitfp_csr = compute_fingerprints(
    smiles,
    rdkitfp,
    config=FingerprintConfig(
        count=False,
        folded=True,
        return_csr=True,    # SciPy CSR
        invalid_policy="raise",
    ),
)
assert sp.issparse(X_rdkitfp_csr)
print("RDKit RDKitFP (CSR):", X_rdkitfp_csr.shape, X_rdkitfp_csr.dtype, "nnz=", X_rdkitfp_csr.nnz)

# --------------------------------------------------
# scikit-fingerprints: MAPFingerprint (folded, dense)
# --------------------------------------------------
# MAPFingerprint is a MinHash-like fingerprint (different from MAP4 lib).
map_fp = MAPFingerprint(fp_size=4096, count=False, sparse=False)
X_map = compute_fingerprints(
    smiles,
    map_fp,
    config=FingerprintConfig(
        count=False,
        folded=True,
        return_csr=False,
        invalid_policy="raise",
    ),
)
print("skfp MAPFingerprint:", X_map.shape, X_map.dtype)

# ----------------------------------------------------
# scikit-fingerprints: AtomPairFingerprint (folded, CSR)
# ----------------------------------------------------
atom_pair = AtomPairFingerprint(fp_size=4096, count=False, sparse=False, use_3D=False)
X_ap_csr = compute_fingerprints(
    smiles,
    atom_pair,
    config=FingerprintConfig(
        count=False,
        folded=True,
        return_csr=True,
        invalid_policy="raise",
    ),
)
assert sp.issparse(X_ap_csr)
print("skfp AtomPair (CSR):", X_ap_csr.shape, X_ap_csr.dtype, "nnz=", X_ap_csr.nnz)

# (Optional) convert CSR -> dense if you need a NumPy array downstream:
X_ap = X_ap_csr.toarray().astype(np.float32, copy=False)