Geometry Optimization in Python

This is the second post in a series aiming at generating a range of candidate structures for evaluation in the context of molecular modeling in the field of ion mobility spectrometry. In a previous post, the use of rdkit to generate structures was introduced. However, closer inspection of the code highlights a few funciton calls aimed at optimizing the conformer structures. Given that the tetraalkylammonium ions were the focus of that effort, the optimization step was quite rapid. This brought into question as to whether any geometry optimization was being performed. In the following jupyter notebook, ibuprofen generated from SMILES input is optimized using the same function call as found in the previous post. This degree of optimization does not reach the level needed for more advanced calculations but can be a decent start when trying to group the different conformers into structural families.

Required python modules include: rdkit

Optional modules: pymol and an instance of this program running as a server.

Conformational Searching using Python

This is the first of a series examining the use of python to generate candidate structures of molecules. These conformations may serve a variety of functions, though our particular purpose is to identify candidates for additional optimization and ultimate use in ion mobility modeling experiments. After considering a range of tools (e.g. Avogadro or ChemDraw), it was apparent that a more automated, open-source work-flow was needed. In full disclosure, there are surely other mechanisms to make this happen but the following jupyter notebook is a reasonable approach. Visualization of the conformers can be accomplished using pymol if you that module is installed and a server instance running in the background (i.e. pymol -R).