Q-ChASM is a collection of tools for automating computational quantum chemistry workflows.
These tools are described briefly below, and include powerful command-line molecular builders.
These tools are all free and open-source and available on GitHub.
AaronTools is a collection tools for:
Information on installing and using the Perl-based Aarontools is available on the AaronTools GitHub Wiki.
The Python implementation of AaronTools is also available on GitHub.
- Building, measuring, manipulating, and comparing molecular structures
- Constructing input files, parsing output files
- Submitting and monitoring jobs using queuing software commonly found on high-performance computing clusters
- Analyzing data from QM computations
ChimAARON is an AaronTools-based plugin for UCSF ChimeraX, a cross-platform 3D molecular graphics program.
ChimAARON extends ChimeraX by adding tools to build and modify complex molecular structures, map new catalysts and ligands onto previously-computed structures, and manage AaronTools libraries.
These tools enable users to rapidly modify several structures simultaneously, providing an intuitive interface to build libraries of the complex molecular structures frequently encountered in modern quantum chemistry applications.
AARON is a computational toolkit that automates the QM-based geometry optimizations of the 100s of transition state (TS) and intermediate structures needed to predict the stereoselectivities of asymmetric organic reactions.
It is built using the Perl version of AaronTools and enables the computational screening of virtual libraries of potential catalysts, which can help prioritize the synthesis and testing of only the most promising catalysts.
A detaled description of AARON is provided in publication 2 below.
Representative applications can be found in the other listed papers.
Briefly, AARON relies on a library of pre-computed TS and intermediate structures for a representative model catalyst and substrate (TS Template Library) which have been either computed 'by hand' or pulled from the literature.
It then performs analogous geometry optimizations on any similar catalyst and related substrate.
AARON automatically handles conformations of simple rotatable groups (OMe, iPr, etc.), obviating the need for tedious manual searches over low-lying conformations of TS structures.
- A. N. Bootsma, T. N. Nguyen, Y. Guan, J. A. May, and S. E. Wheeler, "Enantioselective Catalyst Design through Quantum Mechanical Virtual Screening" (submitted).
- Y. Guan, V. M. Ingman, B. J. Rooks, and S. E. Wheeler, "AARON: An Automated Reaction Optimizer for New Catalysts", J. Chem. Theory Comput. 14, 5249 (2018).
- Y. Guan and S. E. Wheeler, "Automated Quantum Mechanical Predictions of Enantioselectivity in a Rh-Catalyzed Asymmetric Hydrogenation", Angew. Chem. Int. Ed. 56, 9101 (2017).
- A. C. Doney, B. J. Rooks, T. Lu, and S. E. Wheeler, "Design of Organocatalysts for Asymmetric Propargylations through Computational Screening", ACS Catal. 6 7948 (2016).
- S. E. Wheeler, T. J. Seguin, Y. Guan, and A. C. Doney, "Non-covalent Interactions in Organocatalysis and the Prospect of Computational Catalyst Design", Acc. Chem. Res. 49, 1061 (2016).
- B. J. Rooks, M. R. Haas, D. Sepulveda, T. Lu, and S. E. Wheeler, "Prospects for the Computational Design of Bipyridine N,N'-Dioxide Catalysts for Asymmetric Propargylations", ACS Catalysis 5, 272 (2015).
- D. Sepulveda, T. Lu, and S. E. Wheeler, "Performance of DFT Methods and Origin of Stereoselectivity in Bipyridine N,N'-Dioxide Catalyzed Allylation and Propargylation Reactions", Org. Biomol. Chem. 12, 8346 (2014).
- T. Lu, M. A. Porterfield, and S. E. Wheeler, "Explaining the Disparate Stereoselectivities of N-Oxide Catalyzed Allylations and Propargylations of Aromatic Aldehydes", Org. Lett. 14, 5310 (2012).
- T. Lu, R. Zhu, Y. An, and S. E. Wheeler, "Origin of Enantioselectivity in the Propargylation of Aromatic Aldehydes Catalyzed by Helical N-Oxides", J. Am. Chem. Soc. 134, 3095 (2012).
©2017 Steven E. Wheeler. All rights reserved.
CatalystTrends.org is supported in part by NSF Grant CHE-1665407