chemistry-analysis

Chemistry Analysis

Overview

Cheminformatics and computational chemistry span molecular representation, property prediction, spectroscopy interpretation, quantum chemical calculations, and materials characterization. This skill covers the full workflow from parsing SMILES strings through DFT calculations and database queries.

When to Use This Skill

Use this skill when:

• Parsing, validating, or canonicalizing molecular structures (SMILES, InChI, SDF)
• Computing molecular descriptors (MW, logP, TPSA, fingerprints) for a set of compounds
• Filtering compounds by drug-likeness, ADMET, or structural criteria
• Interpreting spectroscopy data (IR, NMR, MS, UV-Vis)
• Setting up or analyzing DFT/computational chemistry workflows
• Characterizing materials with XRD, SAXS, or BET surface area
• Querying chemistry databases (PubChem, Materials Project, CSD)

For genomics or protein sequence analysis, see the bioinformatics-analysis skill instead.

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Cheminformatics with RDKit

Installation

conda install -c conda-forge rdkit
# or
pip install rdkit

SMILES/InChI Parsing and Validation

from rdkit import Chem
from rdkit.Chem.inchi import MolToInchi, InchiToInchiKey

# Parse SMILES — returns None if invalid
mol = Chem.MolFromSmiles('CC(=O)Oc1ccccc1C(=O)O')  # aspirin
if mol is None:
    raise ValueError("Invalid SMILES")

# Canonicalize SMILES (normalize representation)
canonical_smi = Chem.MolToSmiles(mol)

# Convert to InChI and InChIKey
inchi = MolToInchi(mol)
inchikey = Chem.inchi.InchiToInchiKey(inchi)

# Validate a list of SMILES
def validate_smiles(smi_list):
    valid, invalid = [], []
    for smi in smi_list:
        mol = Chem.MolFromSmiles(smi)
        if mol is not None:
            valid.append(Chem.MolToSmiles(mol))  # canonical form
        else:
            invalid.append(smi)
    return valid, invalid

Morgan Fingerprints

Morgan fingerprints (circular fingerprints) are the standard for similarity-based tasks.