RLEC — RNA-Ligand Extended Connectivity Fingerprint

RLEC adapts the PLEC fingerprint (Wójcikowski et al., Bioinformatics 2019) to RNA-ligand systems. For each RNA–ligand contact pair, it pairs the Morgan-style chemical environments of the RNA atom and the ligand atom across increasing depths and hashes the pairs into a count vector.

Installation

pip install rlec

Quick start

from rlec import RLECFingerprint

fp = RLECFingerprint(
    rna_depth=6,      # Morgan depth for RNA atoms
    ligand_depth=3,   # Morgan depth for ligand atoms
    fp_size=4096,     # folded vector size
    feat_set=1,       # 1 = include nucleotide type (A/U/G/C) in RNA hash
    cutoff=6.0,       # contact distance cutoff (Å)
)

vec = fp.transform("path/to/rna.pdb", "path/to/ligand.sdf")
# vec: np.ndarray shape (4096,), dtype float32

transform also accepts an RDKit Mol object as the second argument (must have a 3D conformer).

For a batch:

X = fp.transform_batch([
    ("rna1.pdb", "lig1.sdf"),
    ("rna2.pdb", "lig2.sdf"),
])
# X: np.ndarray shape (n, 4096)

Feature sets

Performance

Validated on 143 RNA–ligand complexes (LOOCV, LightGBM):

95% bootstrap CI: [0.616, 0.790]. RLEC vs ligand-only: Δr = +0.148 (p < 0.0005).

Requirements

• Python ≥ 3.9
• numpy, scipy, biopython, rdkit

Citation

If you use RLEC, please cite:

Stalin A. RLEC: RNA-Ligand Extended Connectivity Fingerprint for binding affinity prediction. (2026)

Wójcikowski M, Kukiełka M, Stepniak-Konieczna M, Antosiewicz JM, Siedlecki P. Development of a protein–ligand extended connectivity (PLEC) fingerprint and its application for virtual screening. Bioinformatics 2019;35(8):1334–1341.