Molecular descriptors are quantities associated with small molecules that specify physical or chemical properties of interest. They can be used to numerically describe many different aspects of a molecule such as:

• molecular graph structure,
• lipophilicity (logP),
• molecular refractivity,
• electrotopological state,
• druglikeness,
• fragment profile,
• molecular charge,
• molecular surface,
• …

Vectors whose components are molecular descriptors can be used (amongst other things) as high-level feature representations for molecular machine learning. In my experience, molecular descriptor vectors tend to fall slightly short of more low-level molecular representation methods such as extended-connectivity fingerprints or graph neural networks when it comes to predictive performance on large and medium-sized molecular property prediction data sets. However, one advantage of molecular descriptor vectors is their interpretability; there is a reasonable chance that the meaning of a physicochemical descriptor can be intuitively understood by a chemical expert.

A wide variety of useful molecular descriptors can be automatically and easily computed via RDKit purely on the basis of the SMILES string of a molecule. Here is a code snippet to illustrate how this works:

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