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; https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/65e65d9a9138d23161ab7a79/original/molli-a-general-purpose-python-toolkit-for-combinatorial-small-molecule-library-generation-manipulation-and-feature-extraction.pdf

The management and analysis of large in silico molecular libraries is pivotal in many areas of modern chemistry. The adoption and success of data-oriented approaches to chemical research is dependent on the ease of handling large collections of in silico molecular structures in a programmatic way. Herein, we introduce the MOLecular LIibrary toolkit, “molli”, which is a Python 3 chemoinformatics 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, and xTB/CREST. Conformer-dependent grid-based feature calculators provide numerical representation suitable for diversity analysis, and interface to robust three-dimensional visualization tools provide comprehensive images to enhance human understanding of libraries with thousands of members. The package includes command-line interface in addition to Python classes to streamline frequently used workflows. This work describes the development and implementation of molli 1.0 and highlights the available functionality. 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 NMR prediction workflow from CDXML files.