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Abstract AQME, automated quantum mechanical environments, is a free and open‐source Python package for the rapid deployment of automated workflows using cheminformatics and quantum chemistry. AQME workflows integrate tasks performed across multiple computational chemistry packages and data formats, preserving all computational protocols, data, and metadata for machine and human users to access and reuse. AQME has a modular structure of independent modules that can be implemented in any sequence, allowing the users to use all or only the desired parts of the program. The code has been developed for researchers with basic familiarity with the Python programming language. The CSEARCH module interfaces to molecular mechanics and semi‐empirical QM (SQM) conformer generation tools (e.g., RDKit and Conformer–Rotamer Ensemble Sampling Tool, CREST) starting from various initial structure formats. The CMIN module enables geometry refinement with SQM and neural network potentials, such as ANI. The QPREP module interfaces with multiple QM programs, such as Gaussian, ORCA, and PySCF. The QCORR module processes QM results, storing structural, energetic, and property data while also enabling automated error handling (i.e., convergence errors, wrong number of imaginary frequencies, isomerization, etc.) and job resubmission. The QDESCP module provides easy access to QM ensemble‐averaged molecular descriptors and computed properties, such as NMR spectra. Overall, AQME provides automated, transparent, and reproducible workflows to produce, analyze and archive computational chemistry results. SMILES inputs can be used, and many aspects of tedious human manipulation can be avoided. Installation and execution on Windows, macOS, and Linux platforms have been tested, and the code has been developed to support access through Jupyter Notebooks, the command line, and job submission (e.g., Slurm) scripts. Examples of pre‐configured workflows are available in various formats, and hands‐on video tutorials illustrate their use. This article is categorized under:Data Science > ChemoinformaticsData Science > Computer Algorithms and ProgrammingSoftware > Quantum Chemistry
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molli: A General-Purpose Python Toolkit for Combinatorial Small Molecule Library Generation, Manipulation, and Feature Extraction.
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.
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- Award ID(s):
- 2154237
- PAR ID:
- 10522711
- Publisher / Repository:
- ChemRxiv
- Date Published:
- Format(s):
- Medium: X
- Institution:
- University of Illinois at Urbana-Champaign
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Posted Content:
- https://doi.org/10.26434/chemrxiv-2024-1cl1f
