Polarizable continuum models provide an effective electrostatic embedding model for fragment‐based chemical shift prediction in challenging systems

Abstract

Ab initio nuclear magnetic resonance chemical shift prediction provides an important tool for interpreting and assigning experimental spectra, but it becomes computationally prohibitive in large systems. The computational costs can be reduced considerably by fragmentation of the large system into a series of contributions from many smaller subsystems. However, the presence of charged functional groups and the need to partition the system across covalent bonds create complications in biomolecules that typically require the use of large fragments and careful descriptions of the electrostatic environment. The present work shows how a model that combines chemical shielding contributions from non-overlapping monomer and dimer fragments embedded in a polarizable continuum model provides a simple, easy-to-implement, and computationally inexpensive approach for predicting chemical shifts in complex systems. The model's performance proves rather insensitive to the continuum dielectric constant, making the 1 This article is protected by copyright. All rights reserved. This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Predicting Density Functional Theory-Quality Nuclear Magnetic Resonance Chemical Shifts via Δ-Machine Learning

“…The 0.70 ppm testing set RMSE for the Δ-ML model based on PBE0/6-31G is particularly noteworthy since it represents only a fraction of the ∼1.2–1.5 ppm RMS errors expected for 13 C chemical shift predictions relative to experiment in the best case scenarios. ,, To our knowledge, the Δ-ML approach here is the first one to predict DFT chemical shieldings with precision that is considerably better than the accuracy of the target DFT approach relative to experiment. Earlier ML models exhibit RMSEs that are up to 2–3 times larger than the accuracy of DFT itself. − , The trade-off, of course, is that the Δ-ML models require a small-basis DFT chemical shielding calculation, which is considerably more expensive than simply evaluating a neural network (though it is still at least an order of magnitude faster than a first-principles PBE0/6-311+G­(2d,p) calculation).…”

“…The 0.70 ppm testing set RMSE for the Δ-ML model based on PBE0/6-31G is particularly noteworthy since it represents only a fraction of the ∼1.2−1.5 ppm RMS errors expected for 13 C chemical shift predictions relative to experiment in the best case scenarios. 51,52,83 To our knowledge, the Δ-ML approach here is the first one to predict DFT chemical shieldings with precision that is considerably better than the accuracy of the target DFT approach relative to experiment. Earlier ML models exhibit RMSEs that are up to 2−3 times larger than the accuracy of DFT itself.…”

Accurate and Cost-Effective NMR Chemical Shift Predictions for Nucleic Acids Using a Molecules-in-Molecules Fragmentation-Based Method

“…Several fragmentation methods employing a QM/molecular mechanics (MM) framework, , and a range of density functional theory (DFT)-based methods like adjustable density matrix assembler (ADMA), fragment molecular orbital (FMO) method, combined fragmentation method (CFM), generalized energy-based fragmentation (GEBF), and systematic molecular fragmentation analysis (SMFA) have been developed by different groups to compute the NMR chemical shifts of various macromolecular systems. − Almost all of these fragmentation methods are tested and benchmarked on either proteins, peptides, or molecular crystals. Only a few studies are on nucleic acids such as a recent study using electrostatically embedded generalized molecular fractionation with conjugate caps (EE-GMFCC) scheme for the excited-state properties of fluorophore RNA systems.…”

Nuclear Magnetic Shielding Constants with the Polarizable Density Embedding Model

“…We note that the use of PCM-embedded calculations in combination with a many-body expansion technique has been suggested as a practical and accurate approach for modeling chemical shieldings for more challenging systems such as proteins and molecular crystals. 20 The most well-known explicit model for large environments is probably the hybrid between QM and classical molecular mechanics (MM) force fields. 21 The common implementations of QM/MM methods describe all electrostatic interactions between QM and MM regions through simple point charges.…”

“…In cases where homogeneous and isotropic polarization effects represent the main constituents for the solvation, these effects may be efficiently accounted for by dielectric continuum models such as the polarizable continuum model (PCM). , However, continuum models neglect the discrete nature of the solvent molecules, making such models inaccurate when directional solvent–solute interactions are important. , An explicit description of the solvent is needed to account for these interactions. , Furthermore, explicit solvation models are more easily generalized to heterogeneous environments. We note that the use of PCM-embedded calculations in combination with a many-body expansion technique has been suggested as a practical and accurate approach for modeling chemical shieldings for more challenging systems such as proteins and molecular crystals …”