PEPCONF, a diverse data set of peptide conformational energies

Prasad, Viki Kumar; Otero-de-la-Roza, Alberto; DiLabio, Gino A.

doi:10.1038/sdata.2018.310

Cited by 36 publications

(33 citation statements)

References 97 publications

(70 reference statements)

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“…Multiple efforts have evaluated the success of wavefunction and density functional first‐principles methods to compare the energetics of different conformers. [ 6–12 ] While experimental crystal structures and bioactive docked conformers are not always the lowest energy conformer, recent efforts have demonstrated only small energy differences when using quantum chemical methods instead of force fields. [ 13,14 ]…”

Section: Introductionmentioning

confidence: 99%

Assessing conformer energies using electronic structure and machine learning methods

Folmsbee

Hutchison

2020

Int J of Quantum Chemistry

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We have performed a large-scale evaluation of current computational methods, including conventional small-molecule force fields; semiempirical, density functional, ab initio electronic structure methods; and current machine learning (ML) techniques to evaluate relative single-point energies. Using up to 10 local minima geometries across 700 molecules, each optimized by B3LYP-D3BJ with single-point DLPNO-CCSD(T) triplezeta energies, we consider over 6500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find that the current ML methods have potential and recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies. The ANI family of ML methods shows promise, particularly the ANI-1ccx variant trained in part on coupled-cluster energies. Multiple methods suggest continued improvements should be expected in both performance and accuracy.

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Section: Introductionmentioning

confidence: 99%

Assessing conformer energies using electronic structure and machine learning methods

Folmsbee

Hutchison

2020

Int J of Quantum Chemistry

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“…f) only a subset from the PEPCONF 188 database for which reference data was recalculated at the DLPNO-CCSD(T)/CBS level of theory (see SI for more details). g) available reference data was calculated at LC-ωPBE-XDM/aug-cc-pVTZ level of theory.…”

Section: Training and Validation Data Setsmentioning

confidence: 99%

Fast and accurate quantum mechanical modeling of large molecular systems using small basis set Hartree–Fock methods corrected with atom-centered potentials

Prasad

Otero-de-la-Roza

DiLabio

2022

Preprint

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There has been significant interest in developing fast and accurate quantum mechanical methods for modeling large molecular systems. In this work, by utilizing a machine-learning regression technique, we have developed new low-cost quantum mechanical approaches to model large molecular systems. The developed approaches rely on using one-electron Gaussian-type functions called atom-centered potentials (ACPs) to correct for the basis set incompleteness and the lack of correlation effects in the underlying minimal or small basis set Hartree-Fock (HF) methods. In particular, ACPs are proposed for ten elements common in organic and bio-organic chemistry (H, B, C, N, O, F, Si, P, S, and Cl) and four different base methods: two minimal basis sets (MINIs and MINIX) plus a double-ζ basis set (6-31G*) in combination with dispersion-corrected HF (HF-D3/MINIs, HF-D3/MINIX, HF-D3/6-31G*), and the HF-3c method. The new ACPs are trained on a very large set (73832 data points) of non-covalent properties (interaction and conformational energies) and validated additionally on a set of 32048 data points. All reference data is of complete basis set coupled-cluster quality, mostly CCSD(T)/CBS. The proposed ACP-corrected methods are shown to give errors in the tenths of a kcal/mol range for non-covalent interaction energies and up to 2 kcal/mol for molecular conformational energies. More importantly, the average errors are similar in the training and validation sets, confirming the robustness and applicability of these methods outside the boundaries of the training set. In addition, the performance of the new ACP-corrected methods is similar to complete basis set DFT but at a cost that is orders of magnitude lower, and the proposed ACPs can be used in any computational chemistry program that supports effective-core potentials without modification. It is also shown that ACPs improve the description of covalent and non-covalent bond geometries of the underlying methods and that the improvement brought about by the application of the ACPs is directly related to the number of atoms to which they are applied, allowing the treatment of systems containing some atoms for which ACPs are not available. Overall, the ACP-corrected methods proposed in this work constitute an alternative accurate, economical, and reliable quantum mechanical approach to describe the geometries, interaction energies, and conformational energies of systems with hundreds to thousands of atoms.

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“…h) only a subset from the PEPCONF 140 database for which reference data was recalculated at the DLPNO-CCSD(T)/CBS level of theory (see Reference 58 for more details). i) comprises negatively charged conformers.…”

Section: Computational Detailsmentioning

confidence: 99%

Small basis set density-functional theory methods corrected with atom-centered potentials

Prasad

Otero-de-la-Roza

DiLabio

2022

Preprint

Self Cite

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Density-functional theory (DFT) is currently the most popular method for modeling non-covalent interactions and thermochemistry. The accurate calculation of non-covalent interaction energies, reaction energies, and barrier heights requires choosing an appropriate functional and, typically, a relatively large basis set. Deficiencies of the density-functional approximation and the use of a limited basis set are the leading sources of error in the calculation of non-covalent and thermochemical properties in molecular systems. In this article, we present three new DFT methods based on the BLYP, M062X and CAM-B3LYP functionals in combination with the 6-31G* basis set and corrected with atom-centered potentials (ACPs). ACPs are one-electron potentials that have the same form as effective-core potentials, except they do not replace any electrons. The ACPs developed in this work are used to generate energy corrections to the underlying DFT/basis-set method such that the errors in predicted chemical properties are minimized while maintaining the low computational cost of the parent methods. ACPs were developed for the elements H, B, C, N, O, F, Si, P, S, and Cl. The ACP parameters were determined using an extensive training set of 118,655 data points, mostly of complete basis set coupled-cluster level quality. The target molecular properties for the ACP-corrected methods include non-covalent interaction energies, molecular conformational energies, reaction energies, barrier heights, and bond separation energies. The ACPs were tested first on the training set and then on a validation set of 42,567 additional data points. We show that the ACP-corrected methods can predict the target molecular properties with accuracy close to complete basis set wavefunction theory methods, but at a computational cost of double-ζ DFT methods. This makes the new BLYP/6-31G*-ACP, M062X/6-31G*-ACP, and CAM-B3LYP/6-31G*-ACP methods uniquely suited to the calculation of non-covalent, thermochemical, and kinetic properties in large molecular systems.

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PEPCONF, a diverse data set of peptide conformational energies

Cited by 36 publications

References 97 publications

Assessing conformer energies using electronic structure and machine learning methods

Assessing conformer energies using electronic structure and machine learning methods

Fast and accurate quantum mechanical modeling of large molecular systems using small basis set Hartree–Fock methods corrected with atom-centered potentials

Small basis set density-functional theory methods corrected with atom-centered potentials

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