Visualizing chemical space networks with RDKit and NetworkX.

This article demonstrates how to create Chemical Space Networks (CSNs) using a Python RDKit and NetworkX workflow. CSNs are a type of network visualization that depict compounds as nodes connected by edges, defined as a pairwise relationship such as a 2D fingerprint similarity value. A step by step approach is presented for creating two different CSNs in this manuscript, one based on RDKit 2D fingerprint Tanimoto similarity values, and another based on maximum common substructure similarity values.

Programmatic conversion of crystal structures into 3D printable files using Jmol.

Background: Three-dimensional (3D) printed crystal structures are useful for chemistry teaching and research. Current manual methods of converting crystal structures into 3D printable files are time-consuming and tedious. To overcome this limitation, we developed a programmatic method that allows for facile conversion of thousands of crystal structures directly into 3D printable files.

Network Formation in an Orthogonally Self-Assembling System.

Many supramolecular motifs self-assemble into nanorods, forming the basis of the mechanical properties of supramolecular polymers. When integrated as end-caps in a bifunctional telechelic polymer, the motifs can phase segregate into the same or into another nanorod. In the latter case, a functional cross-link is formed by the bridging chain that strengthens the polymer network.