Metadata-Version: 2.4
Name: chemap
Version: 0.2.0
Summary: Library for computing molecular fingerprint based similarities as well as dimensionality reduction based chemical space visualizations. 
License-Expression: MIT
License-File: LICENSE
Author: Florian Huber
Author-email: florian.huber@hs-duesseldorf.de
Requires-Python: >=3.11,<3.14
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Dist: joblib (>=1.3.2)
Requires-Dist: map4 (>=1.1.3)
Requires-Dist: matplotlib (>=3.10.1)
Requires-Dist: numba (>=0.61.2)
Requires-Dist: numpy (>=2.1.0)
Requires-Dist: pandas (>=2.2.1)
Requires-Dist: pooch (>=1.8.2)
Requires-Dist: rdkit (>=2024.9.6)
Requires-Dist: scikit-fingerprints (>=1.15.0)
Requires-Dist: scipy (>=1.14.2)
Requires-Dist: tqdm (>=4.67.1)
Description-Content-Type: text/markdown

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# chemap - Chemical Mapping
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:

```python
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)


```

