Biosynformatic Descriptor Computation

biosynformatic-descriptor-computation

Summary

Compute biosynformatic molecular fingerprints—fixed-length numerical vector representations tailored to natural product structure—from molecule objects using the biosynfoni package. This skill converts chemical structures into machine-readable descriptors suitable for biosynthetic class prediction and natural product cheminformatics research.

When to use

When you have a natural product structure (as a molecule object, SMILES string, or InChI string) and need to generate a biosynformatic descriptor for downstream machine learning tasks such as biosynthetic class prediction, structure-activity correlation, or natural product database annotation. Apply this skill when standard molecular fingerprints are insufficient for capturing biosynthetic logic relevant to secondary metabolites.

When NOT to use

• Input is already a pre-computed feature table or descriptor matrix; use this skill only on raw chemical structures.
• Goal is general-purpose molecular fingerprinting (e.g., Tanimoto similarity, scaffold diversity) unrelated to biosynthetic classification or natural product cheminformatics; consider standard RDKit fingerprints (ECFP, MACCS) instead.
• Molecule structures are invalid, ambiguous, or cannot be parsed by RDKit; validate input with Chem.MolFromSmiles() returning non-None before applying this skill.

Inputs

• SMILES string (canonical or non-canonical)
• InChI string
• SDF file (molecular structure supplier)
• RDKit molecule object (Mol)

Outputs

• biosynfoni count fingerprint (fixed-length numerical vector)
• fingerprint array (integer or float dtype)
• CSV file of fingerprints (from SDF batch processing)

How to apply

Install biosynfoni from PyPI (pip install biosynfoni) or from the development repository (pip install -e .[dev]) if contributing. Parse your structure into an RDKit molecule object using Chem.MolFromSmiles() or Chem.MolFromInChI(). Instantiate a Biosynfoni object with the molecule and call the .fingerprint property to retrieve a count fingerprint—a fixed-length numerical vector. Verify output dimensionality and data type match expected specifications (e.g., integer or float array of consistent length). Use pytest tests/ to validate fingerprint computation correctness if modifying the package, and format any code contributions with black before submission.

Related tools

• biosynfoni (Computes biosynformatic molecular fingerprints from molecule objects; core tool for this skill) — https://github.com/lucinamay/biosynfoni
• RDKit (Parses SMILES/InChI strings into molecule objects required as input to biosynfoni)
• pytest (Validates fingerprint computation correctness and implementation integrity before deployment)
• black (Code formatter for maintaining style consistency in biosynfoni contributions) — https://github.com/psf/black

Examples

from biosynfoni import Biosynfoni
from rdkit import Chem

mol = Chem.MolFromSmiles('CC(C)Cc1ccc(cc1)C(C)C(O)=O')
fp = Biosynfoni(mol).fingerprint

Evaluation signals

• Output fingerprint array has expected fixed dimensionality (compare to documented fingerprint size in biosynfoni documentation).
• Fingerprint data type is consistent with specification (integer counts or floats); no NaN or infinite values.
• All pytest tests in tests/ pass without errors or warnings, confirming fingerprint computation logic is correct.
• Fingerprint for the same molecule is deterministic and reproducible across repeated calls.
• Fingerprints from semantically equivalent structures (e.g., different SMILES representations of the same molecule) are identical or very similar in Tanimoto distance.

Limitations

• biosynfoni is specialized for natural products and biosynthetic classification; fingerprints may not generalize well to synthetic drug-like compounds or other chemical spaces.
• Requires valid, parseable chemical structures; malformed or ambiguous SMILES/InChI strings will fail silently or produce invalid fingerprints.
• Fingerprint interpretation is tied to the biosynthetic class predictor training data (COCONUT and ZINC databases); performance on novel chemical scaffolds not represented in training may be limited.
• Python 3.9+ and RDKit (installed as a dependency) are required; some computational environments may have constraints on dependency installation.

Evidence

• [other] biosynfoni provides a biosynformatic molecular fingerprint tailored to natural product chem- and bioinformatic research.: "biosynfoni provides a biosynformatic molecular fingerprint tailored to natural product chem- and bioinformatic research"
• [other] Fingerprint computation returns fixed-length numerical vector: "Execute the fingerprint function on a test molecule to produce a fixed-length numerical vector representation"
• [readme] Installation and RDKit dependency: "Biosynfoni requires Python 3.9 or later. RDKit is installed as a dependency when installing Biosynfoni."
• [readme] Python API usage via Biosynfoni class: "from biosynfoni import Biosynfoni from rdkit import Chem

smi = mol = Chem.MolFromSmiles(smi) fp = Biosynfoni(mol).fingerprint"

• [other] Validation via pytest: "Run pytest tests/ to validate that all fingerprint computation tests pass successfully"
• [other] Code formatting requirement: "Please use black to format your code before submitting a pull request"
• [readme] Training data sourcing: "We have used data from the COCONUT natural product database and ZINC compound database"