Derivation of Highly Predictive 3D-QSAR Models for hERG Channel Blockers Based on the Quantum Artificial Neural Network Algorithm.

The hERG potassium channel serves as an annexed target for drug discovery because the associated off-target inhibitory activity may cause serious cardiotoxicity. Quantitative structure-activity relationship (QSAR) models were developed to predict inhibitory activities against the hERG potassium channel, utilizing the three-dimensional (3D) distribution of quantum mechanical electrostatic potential (ESP) as the molecular descriptor. To prepare the optimal atomic coordinates of dataset molecules, pairwise 3D structural alignments were carried out in order for the quantum mechanical cross correlation between the template and other molecules to be maximized. This alignment method stands out from the common atom-by-atom matching technique, as it can handle structurally diverse molecules as effectively as chemical derivatives that share an identical scaffold. The alignment problem prevalent in 3D-QSAR methods was ameliorated substantially by dividing the dataset molecules into seven subsets, each of which contained molecules with similar molecular weights. Using an artificial neural network algorithm to find the functional relationship between the quantum mechanical ESP descriptors and the experimental hERG inhibitory activities, highly predictive 3D-QSAR models were derived for all seven molecular subsets to the extent that the squared correlation coefficients exceeded 0.79. Given their simplicity in model development and strong predictability, the 3D-QSAR models developed in this study are expected to function as an effective virtual screening tool for assessing the potential cardiotoxicity of drug candidate molecules.

Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage.

A successful passage of the blood-brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood-brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional (3D) distribution of the molecular electrostatic potential (ESP) as the numerical descriptor, a quantitative structure-activity relationship (QSAR) model termed AlphaQ was derived to predict the molecular LogBB values. To obtain the optimal atomic coordinates of the molecules under investigation, the pairwise 3D structural alignments were conducted in such a way to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has the advantage over the conventional atom-by-atom matching protocol in that the structurally diverse molecules can be analyzed as rigorously as the chemical derivatives with the same scaffold. The inaccuracy problem in the 3D structural alignment was alleviated in a large part by categorizing the molecules into the eight subsets according to the molecular weight. By applying the artificial neural network algorithm to associate the fully quantum mechanical ESP descriptors with the extensive experimental LogBB data, a highly predictive 3D-QSAR model was derived for each molecular subset with a squared correlation coefficient larger than 0.8. Due to the simplicity in model building and the high predictability, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability.

Development and application of a comprehensive machine learning program for predicting molecular biochemical and pharmacological properties.

We establish a comprehensive quantitative structure-activity relationship (QSAR) model termed AlphaQ through the machine learning algorithm to associate the fully quantum mechanical molecular descriptors with various biochemical and pharmacological properties. Preliminarily, a novel method for molecular structural alignments was developed in such a way to maximize the quantum mechanical cross correlations among the molecules. Besides the improvement of structural alignments, three-dimensional (3D) distribution of the molecular electrostatic potential was introduced as the unique numerical descriptor for individual molecules. These dual modifications lead to a substantial accuracy enhancement in multifarious 3D-QSAR prediction models of AlphaQ. Most remarkably, AlphaQ has been proven to be applicable to structurally diverse molecules to the extent that it outperforms the conventional QSAR methods in estimating the inhibitory activity against thrombin, the water-cyclohexane distribution coefficient, the permeability across the membrane of the Caco-2 cell, and the metabolic stability in human liver microsomes. Due to the simplicity in model building and the high predictive capability for varying biochemical and pharmacological properties, AlphaQ is anticipated to serve as a valuable screening tool at both early and late stages of drug discovery.

Graphite as a Long-Life Ca2+-Intercalation Anode and its Implementation for Rocking-Chair Type Calcium-Ion Batteries.

Herein, graphite is proposed as a reliable Ca2+-intercalation anode in tetraglyme (G4). When charged (reduced), graphite accommodates solvated Ca2+-ions (Ca-G4) and delivers a reversible capacity of 62 mAh g-1 that signifies the formation of a ternary intercalation compound, Ca-G4·C72. Mass/volume changes during Ca-G4 intercalation and the evolution of in operando X-ray diffraction studies both suggest that Ca-G4 intercalation results in the formation of an intermediate phase between stage-III and stage-II with a gallery height of 11.41 Å. Density functional theory calculations also reveal that the most stable conformation of Ca-G4 has a planar structure with Ca2+ surrounded by G4, which eventually forms a double stack that aligns with graphene layers after intercalation. Despite large dimensional changes during charge/discharge (C/D), both rate performance and cyclic stability are excellent. Graphite retains a substantial capacity at high C/D rates (e.g., 47 mAh g-1 at 1.0 A g-1 s vs 62 mAh g-1 at 0.05 A g-1) and shows no capacity decay during as many as 2000 C/D cycles. As the first Ca2+-shuttling calcium-ion batteries with a graphite anode, a full-cell is constructed by coupling with an organic cathode and its electrochemical performance is presented.

Extended solvent-contact model approach to blind SAMPL5 prediction challenge for the distribution coefficients of drug-like molecules.

The performance of the extended solvent-contact model has been addressed in the SAMPL5 blind prediction challenge for distribution coefficient (LogD) of drug-like molecules with respect to the cyclohexane/water partitioning system. All the atomic parameters defined for 41 atom types in the solvation free energy function were optimized by operating a standard genetic algorithm with respect to water and cyclohexane solvents. In the parameterizations for cyclohexane, the experimental solvation free energy (ΔG sol ) data of 15 molecules for 1-octanol were combined with those of 77 molecules for cyclohexane to construct a training set because ΔG sol values of the former were unavailable for cyclohexane in publicly accessible databases. Using this hybrid training set, we established the LogD prediction model with the correlation coefficient (R), average error (AE), and root mean square error (RMSE) of 0.55, 1.53, and 3.03, respectively, for the comparison of experimental and computational results for 53 SAMPL5 molecules. The modest accuracy in LogD prediction could be attributed to the incomplete optimization of atomic solvation parameters for cyclohexane. With respect to 31 SAMPL5 molecules containing the atom types for which experimental reference data for ΔG sol were available for both water and cyclohexane, the accuracy in LogD prediction increased remarkably with the R, AE, and RMSE values of 0.82, 0.89, and 1.60, respectively. This significant enhancement in performance stemmed from the better optimization of atomic solvation parameters by limiting the element of training set to the molecules with experimental ΔG sol data for cyclohexane. Due to the simplicity in model building and to low computational cost for parameterizations, the extended solvent-contact model is anticipated to serve as a valuable computational tool for LogD prediction upon the enrichment of experimental ΔG sol data for organic solvents.

Accuracy enhancement in the estimation of molecular hydration free energies by implementing the intramolecular hydrogen bond effects.

BACKGROUND: The formation of intramolecular hydrogen bonds (IHBs) may induce the remarkable changes in molecular physicochemical properties. Within the framework of the extended solvent-contact model, we investigate the effect of implementing the IHB interactions on the accuracy in estimating the molecular hydration free energies. RESULTS: The performances of hydration free energy functions including and excluding the IHB parameters are compared using the molecules distributed for SAMPL4 blind prediction challenge and those in Free Solvation Database (FSD). The calculated hydration free energies with IHB effects are found to be in considerably better agreement with the experimental data than those without them. For example, the root mean square error of the estimation decreases from 2.56 to 1.66 and from 1.73 to 1.54 kcal/mol for SAMPL4 and FSD molecules, respectively, due to the extension of atomic parameter space to cope with IHBs. CONCLUSIONS: These improvements are made possible by reducing the overestimation of attractive interactions between water and the solute molecules involving IHBs. The modified hydration free energy function is thus anticipated to be useful for estimating the desolvation cost for various organic molecules.

Magnetic resonance microscopic imaging based on high-order intermolecular multiple-quantum coherences.

Most imaging studies using intermolecular multiple-quantum coherences (iMQCs) have focused on the two-spin dipolar interactions--zero and double quantum coherences. Here, we report the results of various experimental studies to assess the feasibility of magnetic resonance microscopy with high-order iMQCs in model systems at 7 and 14 T. Experimental results demonstrated that the iMQC microscopic images with high coherence orders are readily observable at high field and have unique contrast depending on the sample microstructure and coherence order.

Fluorogenic Hg2+-selective chemodosimeter derived from 8-hydroxyquinoline.

[reaction: see text] A new thioamide derivative of 8-hydroxyquinoline-benzothiazole was prepared, and its fluorogenic chemodosimetric behaviors toward transition-metal ions were investigated. The thioamide derivative showed highly Hg2+-selective fluorescence enhancing properties (167-fold) in 30% aqueous acetonitrile solution. The selective and sensitive signaling behaviors were found to originate from the Hg2+ ion induced transformation of the very weak fluorescent thioamide derivative into a highly fluorescent amide analogue.