MGOS: A library for molecular geometry and its operating system

The geometry of atomic arrangement underpins the structural understanding of molecules in many fields. However, no general framework of mathematical/computational theory for the geometry of atomic arrangement exists. Here we present "Molecular Geometry (MG)'' as a theoretical framework accompanied by "MG Operating System (MGOS)'' which consists of callable functions implementing the MG theory. MG allows researchers to model complicated molecular structure problems in terms of elementary yet standard notions of volume, area, etc. and MGOS frees them from the hard and tedious task of developing/implementing geometric algorithms so that they can focus more on their primary research issues. MG facilitates simpler modeling of molecular structure problems; MGOS functions can be conveniently embedded in application programs for the efficient and accurate solution of geometric queries involving atomic arrangements. The use of MGOS in problems involving spherical entities is akin to the use of math libraries in general purpose programming languages in science and engineering. (C) 2019 The Author(s). Published by Elsevier B.V

Optimal ligand descriptor for pocket recognition based on the Beta-shape.

Structure-based virtual screening is one of the most important and common computational methods for the identification of predicted hit at the beginning of drug discovery. Pocket recognition and definition is frequently a prerequisite of structure-based virtual screening, reducing the search space of the predicted protein-ligand complex. In this paper, we present an optimal ligand shape descriptor for a pocket recognition algorithm based on the beta-shape, which is a derivative structure of the Voronoi diagram of atoms. We investigate six candidates for a shape descriptor for a ligand using statistical analysis: the minimum enclosing sphere, three measures from the principal component analysis of atoms, the van der Waals volume, and the beta-shape volume. Among them, the van der Waals volume of a ligand is the optimal shape descriptor for pocket recognition and best tunes the pocket recognition algorithm based on the beta-shape for efficient virtual screening. The performance of the proposed algorithm is verified by a benchmark test

Ultrafast non-excitonic valley Hall effect in MoS2/WTe2 heterobilayers

AbstractThe valley Hall effect (VHE) in two-dimensional (2D) van der Waals (vdW) crystals is a promising approach to study the valley pseudospin. Most experiments so far have used bound electron-hole pairs (excitons) through local photoexcitation. However, the valley depolarization of such excitons is fast, so that several challenges remain to be resolved. We address this issue by exploiting a unipolar VHE using a heterobilayer made of monolayer MoS2/WTe2 to exhibit a long valley-polarized lifetime due to the absence of electron-hole exchange interaction. The unipolar VHE is manifested by reduced photoluminescence at the MoS2 A exciton energy. Furthermore, we provide quantitative information on the time-dependent valley Hall dynamics by performing the spatially-resolved ultrafast Kerr-rotation microscopy; we find that the valley-polarized electrons persist for more than 4 nanoseconds and the valley Hall mobility exceeds 4.49 × 103 cm2/Vs, which is orders of magnitude larger than previous reports.11Nsciescopu

Box plots by ROC-based metrics of the six shape descriptors.

<p>(a) Balanced accuracy, (b) geometric mean 2, (c) Euclidean distance and (d) Youden index.</p

The sensitivity graphs.

<p><b>The red circle corresponds to the proposed method. The black triangle and blue square correspond to the average value (of the 85 structures of the Astex Diverse Set) for the STP and Random methods for each threshold value, respectively. The horizontal and the vertical axes denote the thresholds and the computed values of sensitivity, respectively.</b> (a) Sensitivity for “Without (component)” and (b) one for “With (component).”</p

Primary metrics of the confusion matrix.

<p>Primary metrics of the confusion matrix.</p

The precision graphs.

<p><b>The red circle corresponds to the proposed method. The black triangle and blue square correspond to the average value (of the 85 structures of the Astex Diverse Set) for the STP and Random methods for each threshold value, respectively. The horizontal and the vertical axes denote the thresholds and the computed values of precision, respectively.</b> (a) Precision for “Without (component)” and (b) one for “With (component).”</p

Box plots by entropy-based metrics of the six types of L-descriptor.

<p>(a) normalized mutual information and (b) Likelihood ratio. *Note that the y-axis scale of the <i>LR</i> plot is different from the <i>NMI</i> plot’s.</p

L-descriptor curves with respect to the ligand size.

<p>R² (the coefficient of determination) is a statistical measure of how close the data are to the fitted regression line. The p-values of the six linear regressions are all less than 10⁻¹¹.</p