Application of Q2MM to predictions in stereoselective synthesis

Abstract

Quantum-Guided Molecular Mechanics (Q2MM) can be used to derive transition state force fields (TSFFs) that allow the fast and accurate predictions of stereoselectivity for a wide range of catalytic enantioselective reactions. The basic ideas behind the derivation of TSFFs using Q2MM are discussed and the steps involved in obtaining a TSFF using the Q2MM code, publically available at github.com/q2mm, are shown. The applicability for a range of reactions, including several non-standard applications of Q2MM, is demonstrated. Future developments of the method are also discussed.

Microsecond timescale MD simulations at the transition state of PmHMGR predict remote allosteric residues

“…We developed the Quantum-Guided Molecular Mechanics (Q2MM) method 13,14 for the automated tting of TSFFs to highlevel electronic structure calculations and applied it to a variety of transition metal catalyzed reactions. 17,18 The details of adapting the Q2MM method to the parameterization of TSFFs for enzymes have also been described.…”

“…The use of transition state force elds (TSFFs) is a promising alternative to QM/MM methods because they treat the entire system at a consistent level of theory, provide an accurate description of the transition state, and allow long time-scale MD simulations. TSFFs have been shown to be highly accurate compared to high-level DFT calculations and experimental data for a wide range of small-molecule reactions, 13,14 but have only been used for the study of enzyme reactivity in an approximate fashion 15,16 and, to the best of our knowledge, never for the study of enzyme dynamics or mechanism. Conceptually, this approach has some similarities to the EVB method.…”

Automated Fitting of Transition State Force Fields for Biomolecular Simulations

“…It should be noted that this procedure is analogous to transfer learning in that parameters trained to a much larger dataset (standard parameters of the Amber force field) and extensively validated in the literature are used as a starting point for retraining a much smaller subset for which smaller training data sets are available. It is a key difference from the development of TSFFs for transition metal catalyzed reactions 14,15,25,26 where there are usually no parameters available for the transition metal environment. As a result, a much larger training set is needed in those cases to achieve a reliable TSFF.…”

Computational Mapping of Dirhodium(II) Catalysts

“…In addition, substrate chemical space has been explored further for reactions of α‐diazo β‐carbonyl esters, [10b] where individual steric and electronic parameters for substrates have been related to both calculated enthalpies of activation for insertion into C(sp 3 )−H bonds and a range of experimentally‐measured yields and selectivities. By necessity, the models arising from these studies are reaction‐specific and focussed on the substrate; we further note that selectivity prediction poses significant computational challenges [12b, 14] …”