Library for computing molecular fingerprint based similarities as well as dimensionality reduction based chemical space visualizations. Test
chemap can be installed using pip.
pip install chemapOr, to include UMAP computation abilities on either CPU or GPU chose one of the following option:
- CPU version:
pip install "chemap[cpu]" - GPU version (CUDA 12):
pip install "chemap[gpu-cu12]" - GPU version (CUDA 13):
pip install "chemap[gpu-cu13]"
Fingerprints can be computed using generators from RDKit or scikit-fingerprints. This includes popular fingerprint types such as:
- RDKit fingerprints
- Morgan fingerprints
- MACCS fingerprints
- PubChem fingerprints
- Klekota-Roth fingerprints
- Atom pair fingerprints
- MAP4 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)