Hatchergravesen4974

We present a new approach to estimate the binding affinity from given three-dimensional poses of protein-ligand complexes. In this scheme, every protein-ligand atom pair makes an additive free-energy contribution. The sum of these pairwise contributions then gives the total binding free energy or the logarithm of the dissociation constant. The pairwise contribution is calculated by a function implemented via a neural network that takes the properties of the two atoms and their distance as input. The pairwise function is trained using a portion of the PDBbind 2018 data set. The model achieves good accuracy for affinity predictions when evaluated with PDBbind 2018 and with the CASF-2016 benchmark, comparing favorably to many scoring functions such as that of AutoDock Vina. The framework here may be extended to incorporate other factors to further improve its accuracy and power.Localized orbital coupled cluster theory has recently emerged as a nonempirical alternative to DFT for large systems. Intuitively, one might expect such methods to perform less well for highly delocalized systems. In the present work, we apply both canonical CCSD(T) approximations and a variety of localized approximations to a set of flexible expanded porphyrins-macrocycles that can switch between Hückel, figure-eight, and Möbius topologies under external stimuli. Both minima and isomerization transition states are considered. We find that Möbius(-like) structures have much stronger static correlation character than the remaining structures, and that this causes significant errors in DLPNO-CCSD(T) and even DLPNO-CCSD(T1) approaches, unless TightPNO cutoffs are employed. If sub-kcal mol-1 accuracy with respect to canonical relative energies is required even for Möbius-type systems (or other systems plagued by strong static correlation), then Nagy and Kallay's LNO-CCSD(T) method with "tight" settings is the suitable localized approach. see more We propose the present POLYPYR21 data set as a benchmark for localized orbital methods or, more broadly, for the ability of lower-level methods to handle energetics with strongly varying degrees of static correlation.For the accurate quantitation of kokumi-enhancing and bitter-tasting octadecadien-12-ynoic and octadecadienoic acids in chanterelles (Cantharellus cibarius Fr.), a sensitive ultra-high-performance liquid chromatography-differential ion mobility spectrometry-tandem mass spectrometry method was developed. On the basis of these quantitative data and the taste thresholds, dose-over-threshold factors were calculated to determine the contribution of these sensometabolites to the kokumi and bitter taste of chanterelles; e.g., 14,15-dehydrocrepenynic acid (3) and (9Z,15E)-14-oxooctadeca-9,15-dien-12-ynoic acid (7) were identified as key kokumi substances in raw chanterelles. Quantitative profiling of these compounds in various mushroom species demonstrated a unique accumulation of octadecadien-12-ynoic acids in Cantharellus. Furthermore, storage experiments highlighted dynamic processes, including the biosynthesis of these substances as a result of lipid peroxidation mechanisms.The system-size dependence of computed mutual diffusion coefficients of multicomponent mixtures is investigated, and a generalized correction term is derived. The generalized finite-size correction term was validated for the ternary molecular mixture chloroform/acetone/methanol as well as 28 ternary LJ systems. It is shown that only the diagonal elements of the Fick matrix show system-size dependency. The finite-size effects of these elements can be corrected by adding the term derived by Yeh and Hummer (J. Phys. Chem. B 2004, 108, 15873-15879). By performing an eigenvalue analysis of the finite-size effects of the matrix of Fick diffusivities we show that the eigenvector matrix of Fick diffusivities does not depend on the size of the simulation box. Only eigenvalues, which describe the speed of diffusion, depend on the size of the system. An analytic relation for finite-size effects of the matrix of Maxwell-Stefan diffusivities was developed. All Maxwell-Stefan diffusivities depend on the system size, and the required correction depends on the matrix of thermodynamic factors.Fast and inexpensive characterization of materials properties is a key element to discover novel functional materials. In this work, we suggest an approach employing three classes of Bayesian machine learning (ML) models to correlate electronic absorption spectra of nanoaggregates with the strength of intermolecular electronic couplings in organic conducting and semiconducting materials. As a specific model system, we consider poly(3,4-ethylenedioxythiophene) (PEDOT) polystyrene sulfonate, a cornerstone material for organic electronic applications, and so analyze the couplings between charged dimers of closely packed PEDOT oligomers that are at the heart of the material's unrivaled conductivity. We demonstrate that ML algorithms can identify correlations between the coupling strengths and the electronic absorption spectra. We also show that ML models can be trained to be transferable across a broad range of spectral resolutions and that the electronic couplings can be predicted from the simulated spectra with an 88% accuracy when ML models are used as classifiers. Although the ML models employed in this study were trained on data generated by a multiscale computational workflow, they were able to leverage experimental data.Nuclear magnetic resonance (NMR) spectroscopy is a powerful analytical tool that enables one to study molecular properties and interactions. Homonuclear couplings provide valuable structural information but are often difficult to disentangle in crowded 1H NMR spectra where complex multiplets and signal overlap commonly exist. Multidimensional NMR experiments push the power of NMR to a new level by providing better signal dispersion. Among them, 2D J-resolved spectroscopy is widely used for multiplet analysis and the measurement of scalar coupling constants. Here, we present a new 2D J-resolved method, CASCADE, through which easier multiplet analysis and unambiguous measurement of specific coupling constants can be achieved at the same time, fully exploiting the power of 2D J-resolved spectroscopy. It is expected that this method may replace a conventional 2D J experiment in many cases, facilitating structural and configurational studies as well as chemical and biological analyses.