Challenges for large scale simulation: general discussion

Brandenburg, Jan Gerit; Burke, Kieron; Civalleri, Bartolomeo; Cole, D. J. A.; Csänyi, Gábor; David, Grégoire; Gidopoulos, Nikitas I.; Gowland, Duncan; Helgaker, Trygve; Herbst, Michael F.; Hourahine, B.; Irons, Tom J. P.; Jacob, Christoph R.; Loos, Pierre‐François; Mehta, Nisha; Mulay, Manasi R.; Neugebauer, Johannes; Pernal, Katarzyna; Pribram‐Jones, Aurora; Ryder, Matthew R.; Savin, Andreas; Sirbu, Dumitru; Skylaris, Chris Kriton; Truhlar, Donald G.; Wetherell, Jack; Yang, Weitao

doi:10.1039/d0fd90024a

Cited by 5 publications

(5 citation statements)

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“…However, given the success of NNPs [ 28 , 33 ] one could use them in a MM/PBSA- or MM/GBSA-style approach [ 82 ] to directly compute the free energy of binding on more physical grounds. In fact, approaches to combine NNP with molecular mechanics for drug discovery applications are already starting to appear [ 83 – 85 ].…”

Section: Discussionmentioning

confidence: 99%

Learning protein-ligand binding affinity with atomic environment vectors

et al. 2021

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Scoring functions for the prediction of protein-ligand binding affinity have seen renewed interest in recent years when novel machine learning and deep learning methods started to consistently outperform classical scoring functions. Here we explore the use of atomic environment vectors (AEVs) and feed-forward neural networks, the building blocks of several neural network potentials, for the prediction of protein-ligand binding affinity. The AEV-based scoring function, which we term AEScore, is shown to perform as well or better than other state-of-the-art scoring functions on binding affinity prediction, with an RMSE of 1.22 pK units and a Pearson’s correlation coefficient of 0.83 for the CASF-2016 benchmark. However, AEScore does not perform as well in docking and virtual screening tasks, for which it has not been explicitly trained. Therefore, we show that the model can be combined with the classical scoring function AutoDock Vina in the context of $$\Delta$$ Δ -learning, where corrections to the AutoDock Vina scoring function are learned instead of the protein-ligand binding affinity itself. Combined with AutoDock Vina, $$\Delta$$ Δ -AEScore has an RMSE of 1.32 pK units and a Pearson’s correlation coefficient of 0.80 on the CASF-2016 benchmark, while retaining the docking and screening power of the underlying classical scoring function.

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Section: Discussionmentioning

confidence: 99%

Learning protein-ligand binding affinity with atomic environment vectors

et al. 2021

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“…When combined with a nearly complete basis set, high-level wave function theory methods can predict various thermochemical and structural properties with an accuracy comparable to, or even better than, experiments. However, such approaches have limited applicability because their computational cost increases steeply with the size of the system. − This precludes high-level wave function methods from being applied to study chemical and biological processes involving large molecular systems, such as enzymatic catalysis, protein folding, supramolecular host–guest complexation, and many others. − …”

Section: Introductionmentioning

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

J. Chem. Theory Comput.

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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 bioorganic 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 (73 832 data points) of noncovalent properties (interaction and conformational energies) and validated additionally on a set of 32 048 data points. All reference data are 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 noncovalent 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 density functional theory (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 noncovalent 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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“…100 atoms. This is unfortunate because there are many interesting problems involving systems in this molecular size range: supra-molecular and (bio)chemical complexes, nanostructured materials and surfaces, enzyme active sites, and many more. − The reason for this limitation is the unfavorable increase in computational cost as approximately the third power in the number of basis functions for common DFT methods. Consequently, the development of computationally inexpensive DFT-based methods that allow efficient and accurate modeling of large systems is an important area of research. − …”

Section: Introductionmentioning

confidence: 99%

Small-Basis Set Density-Functional Theory Methods Corrected with Atom-Centered Potentials

Prasad

Otero-de-la-Roza

DiLabio

2022

J. Chem. Theory Comput.

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Density functional theory (DFT) is currently the most popular method for modeling noncovalent interactions and thermochemistry. The accurate calculation of noncovalent 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 noncovalent and thermochemical properties in molecular systems. In this article, we present three new DFT methods based on the BLYP, M06-2X, 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 118655 data points, mostly of complete basis set coupled-cluster level quality. The target molecular properties for the ACP-corrected methods include noncovalent 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 42567 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, M06-2X/6-31G*-ACP, and CAM-B3LYP/ 6-31G*-ACP methods uniquely suited to the calculation of noncovalent, thermochemical, and kinetic properties in large molecular systems.

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Challenges for large scale simulation: general discussion

Cited by 5 publications

References 0 publications

Learning protein-ligand binding affinity with atomic environment vectors

Learning protein-ligand binding affinity with atomic environment vectors

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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