molli: A General-Purpose Python Toolkit for Combinatorial Small Molecule Library Generation, Manipulation, and Feature Extraction.

• Main Authors: Shved, Alexander S., Ocampo, Blake E., Burlova, Elena S., Olen, Casey L., Rinehart, N. Ian, Denmark, Scott E.
• Format: Other/Unknown Material
• Language: unknown
• Published: American Chemical Society (ACS) 2024
• Subjects: Orca
• Online Access: http://dx.doi.org/10.26434/chemrxiv-2024-1cl1f-v2; https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/66b687e8c9c6a5c07aabd1a2/original/molli-a-general-purpose-python-toolkit-for-combinatorial-small-molecule-library-generation-manipulation-and-feature-extraction.pdf

The construction, management and analysis of large in silico molecular libraries is critical in many areas of modern chemistry. Herein, we introduce the MOLecular LIibrary toolkit, “molli”, which is a Python 3 cheminformatics module that provides a streamlined interface for manipulating large in silico libraries. Three-dimensional, combinatorial molecule libraries can be expanded directly from two-dimensional chemical structure fragments stored in CDXML files with high stereochemical fidelity. Geometry optimization, property calculation, and conformer generation are executed by interfacing with widely used computational chemistry programs such as OpenBabel, RDKit, ORCA, NWChem, and xTB/CREST. Conformer-dependent grid-based feature calculators provide numerical representation, and interface to robust three-dimensional visualization tools that provide comprehensive images to enhance human understanding of libraries with thousands of members. The package includes a command-line interface in addition to Python classes to streamline frequently used workflows. Parallel performance is benchmarked on various hardware platforms, and common workflows are demonstrated for different tasks ranging from optimized grid-based descriptor calculation on catalyst libraries to an NMR chemical shift prediction workflow from CDXML files.