The module pysoftk.torsional automatically detects and reports the atoms involved in the torsional angles found within planar conjugated polymers. PySoftK also enables the creation of molecular sketches highlighting and reporting the atom numbers that form a torsional angle as shown in Figure . The topological fingerprint used in this module relies on the description of molecules as graphs where the atoms are nodes and the covalent bonds are the edges. Based on the idea to convert a molecular Graph (M-Graph) into a path Graph (P-Graph), we have been able to label connections between edges in the M-Graph providing a nomenclature to name the P-Graph. In order to identify the torsions between ring subunits within the backbone of a conjugated polymer, we have identified two conditions that the atoms (e.g. nodes) making up the central bond of the torsion must meet. First, an atom must have three neighbours which are from the inner structure (such as rings) and one provided from the next monomer (as displayed in Fig. ). However, this condition can be also be found in atoms that are embedded within aromatic rings. To avoid this, we have used the ring detection function provided by RDKit to remove potential bonds within a ring, and therefore identifty the bonds which connect the ring moieties in the polymer (Fig. ). After identifying all of these important bonds within the polymer, we then identify all of the torsions that include these bonds as the central bond and therefore can provide the relevant atomic labels involved in these torsional angles as shown in Figure . This module then reports all of these important torsional angles within the molecule, and generates 2D molecular sketches of each one. Figure shows an example of this output for a boroxine polymer. Data Provenance and Software Development One of our main objectives while creating PySoftK is to provide tools to the community that allow users to utilize high-performance computational facilities to automate the creation of databases for polymeric structures as easily as possible. To do so, we have greatly simplified the installation process of PySoftK by employing the pip command strategy. PySoftK has been tested in Linux and Mac operating systems based on python 3.6 or more recent versions.