Inclusion Scenarios and Conformational Flexibility of the SSRI Paroxetine as Perceived from Polymorphism of β-Cyclodextrin–Paroxetine Complex

Depression, a global mental health problem, is prevalent during the coronavirus disease 2019 (COVID-19) pandemic and can be efficiently treated by selective serotonin reuptake inhibitors (SSRIs). Our study series aims at forwarding insights on the β-cyclodextrin (β-CD)–SSRI inclusion complexes by X-ray crystallography combined with density functional theory (DFT) calculation. Here, we report a new crystal form (II) of the 1:1 β-CD–paroxetine (PXT) complex, which is inspired by the reported 2:1 β-CD–PXT complex (crystal form I), reflecting an elusive phenomenon of the polymorphism in CD inclusion complexes. The β-CD–PXT polymorphism stems from the PXT conformational flexibility, which is defined by torsion angles κ, ε around the -CH2–O- group bridging the A- and C–D-rings, of which those of PXT in I and II are totally different. While PXT (II) in an open V-shaped conformation that has the B-ring shallowly inserted in the β-CD cavity, PXT (I) in a closed U-shaped structure is mostly entirely embedded in the β-CD dimeric cavity, of which the A-ring is deeply inserted in the main β-CD cavity. However, PXT molecules in both crystal forms are similarly maintained in the CD cavity via host–guest N–H···O5/O6 H-bonds and C/O–H···π(B/C) interactions and β-CDs have similar 3D arrangements, channel (II) vs. screw-channel (I). Further theoretical explorations on the β-CD–PXT thermodynamic stabilities and the PXT conformational stabilities based on their potential energy surfaces (PESs) have been completed by DFT calculations. The 2:1 β-CD–PXT complex with the greater presence of dispersion interactions is more energetically favorable than the unimolar complex. Conversely, whereas free PXT, PXT (II) and PXT in complex with serotonin transporter are more energetically stable, PXT (I) is least stable and stabilized in the β-CD cavity. As SSRIs could lessen the COVID-19 severity, the CD inclusion complexation not only helps to improve the drug bioavailability, but also promotes the use of antidepressants and COVID-19 medicines concurrently.

Between Harmonic Crystal and Glass: Solids with Dimpled Potential-Energy Surfaces Having Multiple Local Energy Minima

Solids with dimpled potential-energy surfaces are ubiquitous in nature and, typically, exhibit structural (elastic or phonon) instabilities. Dimpled potentials are not harmonic; thus, the conventional quasiharmonic approximation at finite temperatures fails to describe anharmonic vibrations in such solids. At sufficiently high temperatures, their crystal structure is stabilized by entropy; in this phase, a diffraction pattern of a periodic crystal is combined with vibrational properties of a phonon glass. As temperature is lowered, the solid undergoes a symmetry-breaking transition and transforms into a lower-symmetry phase with lower lattice entropy. Here, we identify specific features in the potential-energy surface that lead to such polymorphic behavior; we establish reliable estimates for the relative energies and temperatures associated with the anharmonic vibrations and the solid–solid symmetry-breaking phase transitions. We show that computational phonon methods can be applied to address anharmonic vibrations in a polymorphic solid at fixed temperature. To illustrate the ubiquity of this class of materials, we present a range of examples (elemental metals, a shape-memory alloy, and a layered charge-density-wave system); we show that our theoretical predictions compare well with known experimental data.

Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data

We show that Gaussian process regression (GPR) allows representing multivariate functions with low-dimensional terms via kernel design. When using a kernel built with HDMR (High-dimensional model representation), one obtains a similar type of representation as the previously proposed HDMR-GPR scheme while being faster and simpler to use. We tested the approach on cases where highly accurate machine learning is required from sparse data by fitting potential energy surfaces and kinetic energy densities.

Excited States Computation of Models of Phenylalanine Protein Chains: TD-DFT and Composite CC2/TD-DFT Protocols

The present benchmark calculations testify to the validity of time-dependent density functional theory (TD-DFT) when exploring the low-lying excited states potential energy surfaces of models of phenylalanine protein chains. Among three functionals suitable for systems exhibiting charge-transfer excited states, LC-ωPBE, CAM-B3LYP, and ωB97X-D, which were tested on a reference peptide system, we selected the ωB97X-D functional, which gave the best results compared to the approximate coupled-cluster singles and doubles (CC2) method. A quantitative agreement for both the geometrical parameters and the vibrational frequencies was obtained for the lowest singlet excited state (a ππ* state) of the series of capped peptides. In contrast, only a qualitative agreement was met for the corresponding adiabatic zero-point vibrational energy (ZPVE)-corrected excitation energies. Two composite protocols combining CC2 and DFT/TD-DFT methods were then developed to improve these calculations. Both protocols substantially reduced the error compared to CC2 and experiment, and the best of both even led to results of CC2 quality at a lower cost, thus providing a reliable alternative to this method for very large systems.

Electronic Properties of Aldehyde Complexes Using DFT for Electrooptical Activity

Characterization of materials infer for physical and chemical properties that depend on its molecular structure. Structure of molecule has its dependence on respective electrons of molecule under consideration occupying their positions that correspond to changes in density of electrons. Many theories of its kind were developed to study density of electrons with roots from wavefunction method and electron density method. Wavefunction method has its dependence with linear combination of atomic orbitals, Born approximation, variational principle ,potential energy surfaces for development of Huckel theory, Hartree fock self-consistent theory. Electron density method includes Ab-intio method and density functional theory is possible with Kohenberbg-Kohn existence theorem and Kohn Sham formalism. Density functional studies has diverted attention of researches for properties dependent on structure with use of quantum mechanical descriptors that influence chemical reactivity of molecule forming complexes with properties responsible for electrooptical activity. In the present work complexes with p-anisaldehyde were studied with set of anilines using Gaussian 16 package with B3LYP method. Studies in present work were analyzed from computed infrared spectra responsible for formation of complexes with shifts in wavenumbers; quantum mechanical descriptors for electronic properties. A feature of study is that complexes with p-nitroaniline have greater tendency influence on electronic properties responsible for electrooptical activity due to electrophilic nature.

Role of conical intersection seam topography in the chemiexcitation of 1,2-dioxetanes

Chemiexcitation, the generation of electronic excited states by a thermal reaction initiated on the ground state, is an essential step in chemiluminescence, and it is mediated by the presence of a conical intersection that allows a nonadiabatic transition from ground state to excited state. Conical intersections classified as sloped favor chemiexcitation over ground state relaxation. The chemiexcitation yield of 1,2-dioxetanes is known to increase upon methylation. In this work we explore to which extent this trend can be attributed to changes in the conical intersection topography or accessibility. Since conical intersections are not isolated points, but continuous seams, we locate regions of the conical intersection seams that are close to the configuration space traversed by the molecules as they react on the ground state. We find that conical intersections are energetically and geometrically accessible from the reaction trajectory, and that topographies favorable to chemiexcitation are found in all three molecules studied. Nevertheless, the results suggest that dynamic effects are more important for explaining the different yields than the static features of the potential energy surfaces.

Transferable Neural Network Potential Energy Surfaces for Closed-Shell Organic Molecules: Extension to Ions

Transferable high dimensional neural network potentials (HDNNP) have shown great promise as an avenue to increase the accuracy and domain of applicability of existing atomistic force fields for organic systems relevant to life science. We have previously reported such a potential (Schrödinger-ANI) that has broad coverage of druglike molecules. We extend that work here to cover ionic and zwitterionic druglike molecules expected to be relevant to drug discovery research activities. We report a novel HDNNP architechture, which we call QRNN, that predicts atomic charges and uses these charges as descriptors in an energy model which delivers conformational energies within chemical accuracy when measured against the reference theory it is trained to. Further, we find that delta learning based on a semi-empirical level of theory approximately halves the errors. We test the models on torsion energy profiles, relative conformational energies, geometric parameters and relative tautomer errors.