GRRM is a program designed to perform local, semi-global, or global reaction path explorations. It is highly versatile and can be used for a variety of purposes, from individual reaction path calculations to exhaustive reaction path explorations and the construction of complex reaction path networks. GRRM has been applied to a range of reaction systems, such as gas-phase reactions, organic reactions, organometallic catalysis, surface catalysis, cluster catalysis, radical reactions, photoreactions involving electronically excited states, photocatalysis involving electron transfers, crystal phase transitions under periodic boundary conditions, and enzyme catalysis using the QM/MM-ONIOM method.
GRRM also features built-in interfaces with Gaussian03/09/16, molpro, GAMESS, ORCA, TURBOMOLE, and SIESTA. Additionally, it can be combined with any electronic structure calculation code by providing a simple code.
The latest version, GRRM23, comes with two major updates as listed below:
1. Quantum chemistry-aided retrosynthetic analysis (QCaRA)
In GRRM20 and later versions, a kinetic analysis method called the Rate Constant Matrix Contraction (RCMC) method is available for application to complex reaction path networks. In GRRM23, an inverse kinetic analysis has been implemented based on RCMC, which predicts the yields of the target product for all reactions starting from each of the other species on a reaction path network [1]. By using this inverse kinetic analysis as a kinetic navigation tool, QCaRA can be performed. QCaRA, which employs the product's structure as the sole input, has demonstrated its capability of identifying the correct reactants for various known reactions, including the synthesis of a small natural product [2].
[1] Y. Sumiya, Y. Harabuchi, Y. Nagata, S. Maeda, Quantum chemical calculations to trace back reaction paths for the prediction of reactants., JACS Au, 2022, 2, 1181-1188.
[2] T. Mita, H. Takano, H. Hayashi, W. Kanna, Y. Harabuchi, K. N. Houk, S. Maeda, Prediction of high-yielding single-step or cascade pericyclic reactions for the synthesis of complex synthetic targets., J. Am. Chem. Soc., 2022, 144, 22985-23000.
2. Utilities to easily implement user-developed tools
GRRM23 offers users the ability to control structural optimization and exploration using user-developed external modules. These modules can modify the search order around local minima, alter the paths searched from a local minimum, or apply an external bias potential to a system. These options have been used to accelerate the SC-AFIR search for specific purposes by combining algorithms such as the rapidly-exploring random tree algorithm [3] or the graph neural network-based path selection algorithm [4]. Additionally, they have been used to develop and apply virtual ligands for transition metal catalysis [5].
[3] A. Nakao, Y. Harabuchi, S. Maeda, K. Tsuda, Leveraging algorithmic search in quantum chemical reaction path finding., Phys. Chem. Chem. Phys., 2022, 24, 10305-10310.
[4] A. Nakao, Y. Harabuchi, S. Maeda, K. Tsuda, Exploring the quantum chemical energy landscape with GNN-guided artificial force., J. Chem. Theory Comput., 2023, 19, 713-717.
[5] W. Matsuoka, Y. Harabuchi, S. Maeda, Virtual-ligand-assisted screening strategy to discover enabling ligands for transition metal catalysis., ACS Catal., 2022, 12, 3752-3766.; W. Matsuoka, Y. Harabuchi, S. Maeda, Virtual ligand strategy in transition metal catalysis toward highly efficient elucidation of reaction mechanisms and computational catalyst design., ACS Catal., 2023, 13, 5697–5711.
About distribution, see: https://global.hpc.co.jp/