Towards Hybrid Density Functional Calculations of Molecular Crystals via Fragment-Based Methods

We introduce and employ two QM:QM schemes (a quantum mechanical method embedded into another quantum mechanical method) and report their performance for the X23 set of molecular crystals. We furthermore present the theory to calculate the stress tensors necessary for the computation of optimized cell volumes of molecular crystals and compare all results to those obtained with various density functionals and more approximate methods. Our QM:QM calculations with PBE0:PBE+D3, PBE0:PBE+MBD, and B3LYP:BLYP+D3 yield at a reduced computational cost lattice energy errors close to the ones of the parent hybrid density functional method, whereas for cell volumes, the errors of the QM:QM scheme methods are in between the GGA and hybrid functionals.Comment: Revised Manuscript accepted in "The Journal of Chemical Physics" (AIP

Information-theoretic approaches to atoms-in-molecules : Hirshfeld family of partitioning schemes

Many population analysis methods are based on the precept that molecules should be built from fragments (typically atoms) that maximally resemble the isolated fragment. The resulting molecular building blocks are intuitive (because they maximally resemble well-understood systems) and transferable (because if two molecular fragments both resemble an isolated fragment, they necessarily resemble each other). Information theory is one way to measure the deviation between molecular fragments and their isolated counterparts, and it is a way that lends itself to interpretation. For example, one can analyze the relative importance of electron transfer and polarization of the fragments. We present key features, advantages, and disadvantages of the information-theoretic approach. We also codify existing information-theoretic partitioning methods in a way, that clarifies the enormous freedom one has within the information-theoretic ansatz

Supporting the identification of novel fragment-based positive allosteric modulators using a supervised molecular dynamics approach: A retrospective analysis considering the human A2A adenosine receptor as a key example

Structure-driven fragment-based (SDFB) approaches have provided efficient methods for the identification of novel drug candidates. This strategy has been largely applied in discovering several pharmacological ligand classes, including enzyme inhibitors, receptor antagonists and, more recently, also allosteric (positive and negative) modulators. Recently, Siegal and collaborators reported an interesting study, performed on a detergent-solubilized StaR adenosine A2A receptor, describing the existence of both fragment-like negative allosteric modulators (NAMs), and fragment-like positive allosteric modulators (PAMs). From this retrospective study, our results suggest that Supervised Molecular Dynamics (SuMD) simulations can support, on a reasonable time scale, the identification of fragment-like PAMs following their receptor recognition pathways and characterizing the possible allosteric binding sites

JigsawNet: Shredded Image Reassembly using Convolutional Neural Network and Loop-based Composition

This paper proposes a novel algorithm to reassemble an arbitrarily shredded image to its original status. Existing reassembly pipelines commonly consist of a local matching stage and a global compositions stage. In the local stage, a key challenge in fragment reassembly is to reliably compute and identify correct pairwise matching, for which most existing algorithms use handcrafted features, and hence, cannot reliably handle complicated puzzles. We build a deep convolutional neural network to detect the compatibility of a pairwise stitching, and use it to prune computed pairwise matches. To improve the network efficiency and accuracy, we transfer the calculation of CNN to the stitching region and apply a boost training strategy. In the global composition stage, we modify the commonly adopted greedy edge selection strategies to two new loop closure based searching algorithms. Extensive experiments show that our algorithm significantly outperforms existing methods on solving various puzzles, especially those challenging ones with many fragment pieces

Hydration of a B-DNA Fragment in the Method of Atom-atom Correlation Functions with the Reference Interaction Site Model Approximation

We propose an efficient numerical algorithm for solving integral equations of the theory of liquids in the Reference Interaction Site Model (RISM) approximation for infinitely dilute solution of macromolecules with a large number of atoms. The algorithm is based on applying the nonstationary iterative methods for solving systems of linear algebraic equations. We calculate the solvent-solute atom-atom correlation functions for a fragment of the B-DNA duplex d(GGGGG).d(CCCCC) in infinitely dilute aqueous solution. The obtained results are compared with available experimental data and results from computer simulations.Comment: 9 pages, RevTeX, 9 pages of ps figures, accepted for publications in JC