Evaluating the accuracy of density functional theory for calculating 1H and 13C NMR chemical shifts in drug molecules

Hill, David E.; Vasdev, Neil; Holland, Jason P.

doi:10.1016/j.comptc.2014.11.007

Cited by 19 publications

(16 citation statements)

References 83 publications

(101 reference statements)

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“…Performance and ap- [30,37,38] 6 -211 3.9 ±0.28 5.8 ±0. 30 36 ±8.0 0.988 ±0.001 20 ±3.4 3 1 H δ/ppm 0.11 [38][39][40] 0 -10 0.28 ±0.01 0.42 ±0.02 3.2 ±1.1 0.954 ±0.005 0.53 ±1.2 3.5 1s C δ/mE h 7.5 [41][42][43] -165 --2 4.9 ±0.12 6.5 ±0. 27 [22], mean absolute error (MAE), root mean squared error (RMSE), maximum absolute error (maxAE), squared correlation (R 2 ) and hyperparameters (kernel length scale σ, cut-off radius τ ).…”

Section: Interpolation Between Training Examples Then Provides Prementioning

confidence: 99%

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

Rupp

Ramakrishnan

Lilienfeld

2015

J. Phys. Chem. Lett.

223

233

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We introduce machine learning models of quantum mechanical observables of atoms in molecules. Instant out-of-sample predictions for proton and carbon nuclear chemical shifts, atomic core level excitations, and forces on atoms reach accuracies on par with density functional theory reference. Locality is exploited within non-linear regression via local atom-centered coordinate systems. The approach is validated on a diverse set of 9 k small organic molecules. Linear scaling of computational cost in system size is demonstrated for saturated polymers with up to sub-mesoscale lengths.

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Section: Interpolation Between Training Examples Then Provides Prementioning

confidence: 99%

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

Rupp

Ramakrishnan

Lilienfeld

2015

J. Phys. Chem. Lett.

223

233

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“…For an initial demonstration of ISiCLE, we have compiled a molecule set of 312 compounds from previous studies: Alver [63], Asiri et al [64], Bally and Rablen [65], Bagno et al [66], Borkowski et al [67], Coruh et al [68], Fulmer et al [69], Hill et al [70], Izgi et al [71], Karabacak et al [72], Krishnakumar et al [73–75], Kwan and Liu [45], Li et al [76], Lomas [77], Osmialowski et al [78], Parlak et al [79], Perez et al [80], Rablen et al [81], Sarotti and Pellegrinet [82, 83], Sebastian et al [84], Seca et al [52], Senyel et al [85, 86], Sridevi et al [87], Tormena and da Silva [88], Vijaya and Sankaran [89], Watts et al [53], Wiitala et al [90, 91], Willoughby et al [92], and Yang et al [93]. We aimed to cover a broad chemical space and distribution of sizes.…”

Section: Methodsmentioning

confidence: 99%

“…H per moleculeAve. C per moleculeTypes of moleculesAlver [63]124118Boron-based compoundAsiri et al [64]1331216Organic photochromic compoundBagno et al [66]4442118Small organic molecules with constrained conformationsBarkowski et al [67]15835031Pentacyclic terpenoids (fernenes)Coruh et al [68]125813Heterocyclic aromatic compoundFulmer et al [69]331494Commonly used NMR solventsHill et al [70]1703330Complex drug with multiple chemical groups and one stereocenterIzgi et al [71]124158Molecule with cyclohexene (C 6 H 10 ) attached to ethylamine (C 2 H 7 N)Karabacak et al [72]11787Planar benzene ring with attached B(OH) 2 and two F groupsKrishnakumar et al [74]21646Agrochemical intermediate compounds with planar ringsKrishnakumar et al [73]2167…”

Section: Methodsmentioning

confidence: 99%

An automated framework for NMR chemical shift calculations of small organic molecules

Yesiltepe¹,

Nuñez²,

Colby³

et al. 2018

J Cheminform

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When using nuclear magnetic resonance (NMR) to assist in chemical identification in complex samples, researchers commonly rely on databases for chemical shift spectra. However, authentic standards are typically depended upon to build libraries experimentally. Considering complex biological samples, such as blood and soil, the entirety of NMR spectra required for all possible compounds would be infeasible to ascertain due to limitations of available standards and experimental processing time. As an alternative, we introduce the in silico Chemical Library Engine (ISiCLE) NMR chemical shift module to accurately and automatically calculate NMR chemical shifts of small organic molecules through use of quantum chemical calculations. ISiCLE performs density functional theory (DFT)-based calculations for predicting chemical properties—specifically NMR chemical shifts in this manuscript—via the open source, high-performance computational chemistry software, NWChem. ISiCLE calculates the NMR chemical shifts of sets of molecules using any available combination of DFT method, solvent, and NMR-active nuclei, using both user-selected reference compounds and/or linear regression methods. Calculated NMR chemical shifts are provided to the user for each molecule, along with comparisons with respect to a number of metrics commonly used in the literature. Here, we demonstrate ISiCLE using a set of 312 molecules, ranging in size up to 90 carbon atoms. For each, calculation of NMR chemical shifts have been performed with 8 different levels of DFT theory, and with solvation effects using the implicit solvent Conductor-like Screening Model. The DFT method dependence of the calculated chemical shifts have been systematically investigated through benchmarking and subsequently compared to experimental data available in the literature. Furthermore, ISiCLE has been applied to a set of 80 methylcyclohexane conformers, combined via Boltzmann weighting and compared to experimental values. We demonstrate that our protocol shows promise in the automation of chemical shift calculations and, ultimately, the expansion of chemical shift libraries. Electronic supplementary materialThe online version of this article (10.1186/s13321-018-0305-8) contains supplementary material, which is available to authorized users.

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“…Non-relativistic DFT calculations of shielding for light atoms, usually with PCM standing in for the solvent, provide quick resolution of spectral assignments and answers to structural questions for experimentalists. Some recent examples are the following: DFT calculation of 15 N NMR chemical shifts of the nitrogencontaining heterocycles (best results with KT3 functional) (Samultsev et al, 2014), using the KT3 functional and PCM for 15 N NMR chemical shifts of 27 azoles and azines in 10 different solvents (Semenov et al, 2014), using mPW1PW91 functional for calculations of 1 H chemical shifts in lactones (Aimola et al, 2015), using B3LYP for 1 H and 13 C in tautomeric 1-[(pyridin-2-yl amino) methyl] pyrrolidine-2,5-dione (Boobalan et al, 2014), using mPW1PW91 and PCM for 1 H and 13 C in lignin amino acid adducts (Diehl et al, 2014), using 16 different functionals for 1 H and 13 C in (R)-ispinesib, a drug molecule (Hill et al, 2015), using B3LYP in COSMO solvent model for for 1 H and 13 C shielding in bis(4trimethylammoniumbenzoate) hydroiodide hydrate in DMSO (Komasa et al, 2015), using B3LYP for 13 C shielding in tautomers of vitamin B-13 in PCM solvent model (Kubica and Gryff-Keller, 2015), using 7 different functionals for 13 C shielding in 2hydroxylamino-4,6-dinitrotoluene (Liu et al, 2015), using BP86 functional for 13 C shielding tensors in C60 and C60 10+ (Munoz-Castro, 2015), using BHandH functional for 13 C and 17 O in 1,3-dioxolane and 19 F in perfluoro-1,3dioxolane (Nozirov et al, 2014), using B3LYP for 1 H and 13 C in in 2-bromo-1H-benzimidazol (Sas et al, 2015), using B3LYP for 1 H and 13 C shielding in 1:2 complex of dimethylphenyl betaine with 2,6-dichloro-4nitro-phenol in DMSO (Szafran et al, 2015), and using the MPW1K (Lynch et al, 2000) hybrid functional with PCM solvent model for 29 Si in 24 organosilanes (Zhang et al, 2014 parameters, nuclear shielding in particular, has appeared (Bagno and Saielli, 2015).…”

Section: General Theorymentioning

confidence: 99%

Recent Advances in Theoretical and Physical Aspects of NMR Chemical Shifts

Dios

Jameson

2015

KIMIKA

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In the first part of this review, theoretical aspects of nuclear magnetic shielding include (a) general theory, for example, newly developed approaches in relativistic theory of nuclear shielding, the relation between the spin-rotation tensor and shielding in relativistic theory, ab initio methods for treating open shell systems and a complete theory of chemical shifts in paramagnetic systems, the link between the definitions of the elusive concepts aromaticity and anti-aromaticity and the magnetic properties: the magnetizability tensor and the nuclear magnetic shielding tensor via delocalized electron currents and electron current maps, (b) ab initio and DFT calculations, both relativistic and non-relativistic, for various nuclei in various molecular systems using various levels of theoretical treatment. Physical aspects include (a) anisotropy of the shielding tensor, usually from solid state measurements, and calculations to support these, (b) shielding surfaces and rovibrational averaging, paying special attention to the sensitive relationship between shielding and bond angles or torsion angles that makes shielding such a powerful tool for structural/conformational determination in macromolecules, (c) chemical shifts that arise from isotopic substitution of NMR nucleus or neighboring nuclei, (d) intermolecular effects on nuclear shielding, and (e) absolute shielding scales.

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Evaluating the accuracy of density functional theory for calculating 1H and 13C NMR chemical shifts in drug molecules

Cited by 19 publications

References 83 publications

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

An automated framework for NMR chemical shift calculations of small organic molecules

Recent Advances in Theoretical and Physical Aspects of NMR Chemical Shifts

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