There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, http://rcsborg), the US data center for the global PDB archive, makes PDB data freely available to all users, from structural biologists to computational biologists and beyond New tools and resources have been added to the RCSB PDB web portal in support of a 'Structural View of Biology' Recent developments have improved the User experience, including the high-speed NGL Viewer that provides 3D molecular visualization in any web browser, improved support for data file download and enhanced organization of website pages for query, reporting and individual structure exploration Structure validation information is now visible for all archival entries PDB data have been integrated with external biological resources, including chromosomal position within the human genome; protein modifications; and metabolic pathways PDB-101 educational materials have been reorganized into a searchable website and expanded to include new features such as the Geis Digital Archive

The RCSB protein data bank: integrative view of protein, gene and 3D structural information

In this Article we describe the OpenMolcas environment and invite the computational chemistry community to collaborate. The open-source project already includes a large number of new developments realized during the transition from the commercial MOLCAS product to the open-source platform. The paper initially describes the technical details of the new software development platform. This is followed by brief presentations of many new methods, implementations, and features of the OpenMolcas program suite. These developments include novel wave function methods such as stochastic complete active space self-consistent field, density matrix renormalization group (DMRG) methods, and hybrid multiconfigurational wave function and density functional theory models. Some of these implementations include an array of additional options and functionalities. The paper proceeds and describes developments related to explorations of potential energy surfaces. Here we present methods for the optimization of conical intersections, the simulation of adiabatic and nonadiabatic molecular dynamics, and interfaces to tools for semiclassical and quantum mechanical nuclear dynamics. Furthermore, the Article describes features unique to simulations of spectroscopic and magnetic phenomena such as the exact semiclassical description of the interaction between light and matter, various X-ray processes, magnetic circular dichroism, and properties. Finally, the paper describes a number of built-in and add-on features to support the OpenMolcas platform with postcalculation analysis and visualization, a multiscale simulation option using frozen-density embedding theory, and new electronic and muonic basis sets.

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OpenMolcas : From Source Code to Insight

Motivation The interactive visualization of very large macromolecular complexes on the web is becoming a challenging problem as experimental techniques advance at an unprecedented rate and deliver structures of increasing size. Results We have tackled this problem by developing highly memory-efficient and scalable extensions for the NGL WebGL-based molecular viewer and by using Macromolecular Transmission Format (MMTF), a binary and compressed MMTF. These enable NGL to download and render molecular complexes with millions of atoms interactively on desktop computers and smartphones alike, making it a tool of choice for web-based molecular visualization in research and education. Availability and implementation The source code is freely available under the MIT license at github.com/arose/ngl and distributed on NPM (npmjs.com/package/ngl). MMTF-JavaScript encoders and decoders are available at github.com/rcsb/mmtf-javascript.

/pdf/ngl-viewer-web-based-molecular-graphics-for-large-complexes-4z1s1rebow.pdf

NGL viewer: web-based molecular graphics for large complexes.

The success of molecular modeling and computational chemistry efforts are, by definition, dependent on quality software applications. Open source software development provides many advantages to users of modeling applications, not the least of which is that the software is free and completely extendable. In this review we categorize, enumerate, and describe available open source software packages for molecular modeling and computational chemistry. An updated online version of this catalog can be found at https://opensourcemolecularmodeling.github.io.

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Open source molecular modeling

The emphasis of this review is particularly on multivariate statistical methods currently used in quantitative structure–activity relationship (QSAR) studies. The mathematical methods for constructing QSAR include linear and non-linear methods that solve regression and classification problems in data structure. The most widely used methods for the classification or pattern recognition; are principal component analysis (PCA) and hierarchical cluster analysis (HCA) as the exploratory data analysis methods. The regression analysis tools are artificial neural network (ANN), principal component regression (PCR), partial least squares (PLS) and classification and regression tree (CART). Also some pattern recognition approaches of k nearest neighbor (kNN), the soft independent modelling of class analogy (SIMCA) and support vector machines (SVM) have been described. Furthermore, different applications were represented for further characterization of these techniques.

Multivariate statistical analysis methods in QSAR

3D-QSAR analysis of human immunodeficiency virus entry-1 inhibitors by CoMFA and CoMSIA.

A pharmacophore model does not describe a real molecule or a real association of functional groups but illustrates a molecular recognition of a biological target shared by a group of compounds. Pharmacophores also represent the spatial arrangement of essential interactions in a receptor-binding pocket. Structure based pharmacophores (SBPs) can work both with a free (apo) structure or a macromolecule-ligand complex (holo) structure. The SBP methods that derive pharmacophore from protein-ligand complexes use the potential interactions observed between ligand and protein, whereas, the SBP method that aims to derive pharmacophore from ligand free protein, uses only protein active site information. Therefore SBPs do not encounter to challenging problems such as ligand flexibility, molecular alignment as well as proper selection of training set compounds in ligand based pharmacophore modeling. The current review deals with Hot Spot' analysis of binding site to feature generation, several approaches to feature reduction, and considers shape and excluded volumes to SBP model building. This review continues to represent several applications of SBPs in virtual screening especially in parallel screening approach and multi-target drug design. Also it reports the applications of SBPs in QSAR. This review emphasizes that SBPs are valuable tools for hit to lead optimization, virtual screening, scaffold hopping, and multi-target drug design.

Methods and Applications of Structure Based Pharmacophores in Drug Discovery

Acetylcholinesterase is a critical enzyme that regulates neurotransmission by catalyzing the breakdown of neurotransmitter acetylcholine in synapses of the nervous system. It is an important target...

Somayeh Pirhadi

Papers

Open source molecular modeling

Multivariate statistical analysis methods in QSAR

3D-QSAR analysis of human immunodeficiency virus entry-1 inhibitors by CoMFA and CoMSIA.

Methods and Applications of Structure Based Pharmacophores in Drug Discovery

Dynamic structure based pharmacophore modeling of the Acetylcholinesterase reveals several potential inhibitors.