I contributed MolsMatrixToGridImage to the RDKit 2023.09.1 release because I found myself writing similar code over and over to draw row-and-column grids of molecules. For projects where each row represented something, such as a molecule and the fragments off a common core, my mental model corresponded to a two-dimensional (nested) data structure, whereas the pre-existing function MolsToGridImage supported only linear (flat) data structures.

Here’s how to display formatted molecular formulas in tables and graphs. In addition to formatted molecular formulas, these techniques should work for any Markdown or LaTeX. In the last blog post, we generated molecular formulas from SMILES strings or RDKit molecules. Once we have those molecular formulas, formatted as Markdown or LaTeX, we might want to display them in tables or graphs.

In cheminformatics, the typical way of representing a molecule is with a SMILES string such as CCO for ethanol. A SMILES string can be converted into a molecular graph, which can be used to determine molecular structure and related properties. However, there are still cases where the molecular formula such as C ₂ H ₆ O is useful.

In a previous post, I revisited Wiener’s paper predicting alkanes’ boiling points using modern cheminformatics tools. This follow-up post refits the data with modern mathematical tools to check how well the literature parameters, and the current parameters optimized here, fit the data. Wiener and Egloff’s works are impressive for using cheminformatics parameters that model physical data with simple relationships.

Harry Wiener was “a pioneer in cheminformatics and chemical graph theory”. In his 1947 Journal of the American Chemical Society article “Structural Determination of Paraffin Boiling Points”, he introduced the path number $\omega$ “as the sum of the distances between any two carbon atoms in the molecule, in terms of carbon-carbon bonds”, which is now known as the Wiener index.

This utility reports whether the starting materials are commercially available for a set of synthesis targets given reactions. You give it your synthesis targets and the reaction to create each, it determines the starting materials, checks whether they are commercially available, and tells you whether each target is accessible–whether all its starting materials are commercially available.

This utility plots a mass spectrometry fragmentation tree given the species’ SMILES and their hierarchy, that is which species fragments into which other species. Here’s an annotated example.

In drug discovery, the lead optimization step often involves creating analogues of a hit (a promising compound which produces a desired result in an assay) to optimize selectivity and minimize toxicity. Because it is typically easier to chemically modify the periphery of the molecule (for example the functional groups) than the scaffold, it is helpful to compare the groups off of the common scaffold.

This example lets you visualize the hierarchy of nonbinary trees. An example is the classification of chemical isomers, which are compounds that have the same chemical formula, but different arrangements of atoms in space.

This example uses machine learning to predict the lipophilicity of compounds. Lipophilicity measures how well a compound dissolves in non-polar media such as fats and lipids. So it’s important for drugs that are delivered orally (for example, via a pill) because the active ingredient needs to be absorbed into the lipids of biological membranes.

Retrosynthetic analysis involves decomposing a target molecule into a set of fragments that could be combined to make the parent molecule using common reactions. The Recap algorithm by X. Lewell, D. Judd, S. Watson, and M. Hann accomplishes that. Recap is implemented in the RDKit cheminformatics Python package.