Simple position and orientation preconditioning scheme for minimum energy path calculations

Diffusion-based generative AI for exploring transition states from 2D molecular graphs

The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics. Recently, machine learning (ML) models have shown remarkable performance for prediction of TS geometries. However, they require 3D conformations of reactants and products often with their appropriate orientations as input, which demands substantial efforts and computational cost. Here, we propose a generative approach based on the stochastic diffusion method, namely TSDiff, for prediction of TS geometries just from 2D molecular graphs. TSDiff outperforms the existing ML models with 3D geometries in terms of both accuracy and efficiency. Moreover, it enables to sample various TS conformations, because it learns the distribution of TS geometries for diverse reactions in training. Thus, TSDiff finds more favorable reaction pathways with lower barrier heights than those in the reference database. These results demonstrate that TSDiff shows promising potential for an efficient and reliable TS exploration.

Graph-Driven Reaction Discovery: Progress, Challenges, and Future Opportunities

Graph-based descriptors, such as bond-order matrices and adjacency matrices, offer a simple and compact way of categorizing molecular structures; furthermore, such descriptors can be readily used to catalog chemical reactions (i.e., bond-making and -breaking). As such, a number of graph-based methodologies have been developed with the goal of automating the process of generating chemical reaction network models describing the possible mechanistic chemistry in a given set of reactant species. Here, we outline the evolution of these graph-based reaction discovery schemes, with particular emphasis on more recent methods incorporating graph-based methods with semiempirical and ab initio electronic structure calculations, minimum-energy path refinements, and transition state searches. Using representative examples from homogeneous catalysis and interstellar chemistry, we highlight how these schemes increasingly act as "virtual reaction vessels" for interrogating mechanistic questions. Finally, we highlight where challenges remain, including issues of chemical accuracy and calculation speeds, as well as the inherent challenge of dealing with the vast size of accessible chemical reaction space.

Computational Insight into the Rope-Skipping Isomerization of Diarylether Cyclophanes

The restricted rotation of chemical bonds may lead to the formation of stable, conformationally chiral molecules. While the asymmetry in chiral molecules is generally observed in the presence of one or more stereocenters, asymmetry exhibited by conformational chirality in compounds lacking stereocenters, called atropisomerism, depends on structural and temperature factors that are still not fully understood. This atropisomerism is observed in natural diarylether heptanoids where the length of the intramolecular tether constrains the compounds to isolable enantiomers at room temperature. In this work, we examine the impact tether length has on the activation free energies to isomerization of a diarylether cyclophane substructure with a tether ranging from 6 to 14 carbons. Racemization activation energies are observed to decay from 48 kcal/mol for a 7-carbon tether to 9.2 kcal/mol for a 14-carbon tether. Synthetic efforts to experimentally test these constraints are also presented. This work will likely guide the design and synthesis of novel asymmetric cyclophanes that will be of interest in the catalysis community given the importance of atropisomeric ligands in the field of asymmetric catalysis.