Decomposition of molecular properties

Ågren, Hans; Harczuk, Ignat; Vahtras, Olav

doi:10.1039/c8cp04340j

Cited by 4 publications

(3 citation statements)

References 104 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Description of a molecular environment is enabled by the self‐consistent PE approach, which uses distributed electric multipole moments to represent the permanent electrostatic effects of the environment on the QM subsystem and distributed localized electric‐dipole polarizability tensors to describe anisotropic polarization effects both internally in the environment and due to interactions with the QM subsystem. PE parameters can be obtained with use of an integrated implementation of the LoProp scheme and the corresponding potential is consistently integrated in the response theory module under the assumption that dispersion effects in the polarizabilities of the environment can be ignored . The PE functionality in VeloxChem is handled through the external CPPE library …”

Section: Capabilitiesmentioning

confidence: 99%

VeloxChem: A Python‐driven density‐functional theory program for spectroscopy simulations in high‐performance computing environments

Rinkevičius

Vahtras

et al. 2019

WIREs Comput Mol Sci

Self Cite

View full text Add to dashboard Cite

An open‐source program named VeloxChem has been developed for the calculation of electronic real and complex linear response functions at the levels of Hartree–Fock and Kohn–Sham density functional theories. With an object‐oriented program structure written in a Python/C++ layered fashion, VeloxChem enables time‐efficient prototyping of novel scientific approaches without sacrificing computational efficiency, so that molecular systems involving up to and beyond 500 second‐row atoms (or some 10,000 contracted and in part diffuse Gaussian basis functions) can be routinely addressed. In addition, VeloxChem is equipped with a polarizable embedding scheme for the treatment of the classical electrostatic interactions with an environment that in turn is modeled by atomic site charges and polarizabilities. The underlying hybrid message passing interface (MPI)/open multiprocessing (OpenMP) parallelization scheme makes VeloxChem suitable for execution in high‐performance computing cluster environments, showing even slightly beyond linear scaling for the Fock matrix construction with use of up to 16,384 central processing unit (CPU) cores. An efficient—with respect to convergence rate and overall computational cost—multifrequency/gradient complex linear response equation solver enables calculations not only of conventional spectra, such as visible/ultraviolet/X‐ray electronic absorption and circular dichroism spectra, but also time‐resolved linear response signals as due to ultra‐short weak laser pulses. VeloxChem distributed under the GNU Lesser General Public License version 2.1 (LGPLv2.1) license and made available for download from the homepage https://veloxchem.org. This article is categorized under: Software > Quantum Chemistry Electronic Structure Theory > Density Functional Theory Theoretical and Physical Chemistry > Spectroscopy

show abstract

Section: Capabilitiesmentioning

confidence: 99%

VeloxChem: A Python‐driven density‐functional theory program for spectroscopy simulations in high‐performance computing environments

Rinkevičius

Vahtras

et al. 2019

WIREs Comput Mol Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…The anisotropy of bonding in molecules involves concentrating electron density in the spatial directions given by the molecular structure. Approaches such as the electronegativity equalization method to compute atomic charges in molecules 59 or the Applequist/Thole models for treating polarization effects on molecular charge distributions 60 include anisotropy via atomic functions developed within dedicated frameworks. We considered this anisotropy as an inherent feature of the molecules' own electron density and therefore extended the former AMD isotropic model to include anisotropic corrections in the analytical representation of ρ(r).…”

Section: ■ Methodsmentioning

confidence: 99%

Analytical Model of Electron Density and Its Machine Learning Inference

Cuevas-Zuviría

Pacios

2020

J. Chem. Inf. Model.

View full text Add to dashboard Cite

We present an analytical model representation of the electron density ρ(r) in molecules in the form of expansions of a few functions (exponentials and a Gaussian) per atom. Based on a former analytical model of ρ(r) in atoms, we devised its molecular implementation by introducing the anisotropy inherent in the electron distribution of atoms in molecules by means of proper anisotropic functions. The resulting model named A2MD (anisotropic analytical model of density) takes an analytical form highly suitable for obtaining the electron density in large biomolecules as its computational cost scales linearly with the number of atoms. To obtain the parameters of the model, we first devised a fitting procedure to reference electron densities obtained in ab initio correlated quantum calculations. Second, in order to skip costly ab initio calculations, we also developed a machine learning (ML)based predictor that used neural networks trained on broad molecular datasets to determine the parameters of the model. The resulting ML methodology that we named A2MDnet (A2MD network-trained) was able to provide reliable electron densities as a basis to predict molecular features without requiring quantum calculations. The results presented together with the low computational scaling associated to the A2MD representation of ρ(r) suggest potential applications to obtain reliable electron densities and ρ(r)-based molecular properties in biomacromolecules.

show abstract

“…In the experimental situational world, physicochemical properties are the projection of chemical properties in the known chemical space; being only projections, these properties are often not unique (degenerate, i.e., different molecules with the same mass) thus different molecules can exhibit similar properties [ 31 ]. Material design and drug discovery both involve the exploration of chemical space.…”

Section: Introductionmentioning

confidence: 99%

Benzoquinoline Chemical Space: A Helpful Approach in Antibacterial and Anticancer Drug Design

Lungu

Mangalagiu

et al. 2023

Molecules

View full text Add to dashboard Cite

Benzoquinolines are used in many drug design projects as starting molecules subject to derivatization. This computational study aims to characterize e benzoquinone drug space to ease future drug design processes based on these molecules. The drug space is composed of all benzoquinones, which are active on topoisomerase II and ATP synthase. Topological, chemical, and bioactivity spaces are explored using computational methodologies based on virtual screening and scaffold hopping and molecular docking, respectively. Topological space is a geometrical space in which the elements composing it can be defined as a set of neighbors (which satisfy a particular axiom). In such space, a chemical space can be defined as the property space spanned by all possible molecules and chemical compounds adhering to a given set of construction principles and boundary conditions. In this chemical space, the potentially pharmacologically active molecules form the bioactivity space. Results show a poly-morphological chemical space that suggests distinct characteristics. The chemical space is correlated with properties such as steric energy, the number of hydrogen bonds, the presence of halogen atoms, and membrane permeability-related properties. Lastly, novel chemical compounds (such as oxadiazole methybenzamide and floro methylcyclohexane diene) with drug-like potential, active on TOPO II and ATP synthase have been identified.

show abstract

Decomposition of molecular properties

Abstract: We review recent work on property decomposition techniques using quantum chemical methods and discuss some topical applications in terms of quantum mechanics-molecular mechanics calculations and the constructing of properties of large molecules and clusters.

Cited by 4 publications

References 104 publications

VeloxChem: A Python‐driven density‐functional theory program for spectroscopy simulations in high‐performance computing environments

VeloxChem: A Python‐driven density‐functional theory program for spectroscopy simulations in high‐performance computing environments

Analytical Model of Electron Density and Its Machine Learning Inference

Benzoquinoline Chemical Space: A Helpful Approach in Antibacterial and Anticancer Drug Design

Contact Info

Product

Resources

About