We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19.

Extensive photochemical and spectroscopic properties of the ${\mathrm{V}}_{\mathrm{B}}^{\ensuremath{-}}$ defect in hexagonal boron nitride are calculated, concluding that the observed photoemission associated with recently observed optically detected magnetic resonance is most likely of ${(1)}^{3}{E}^{\ensuremath{'}\ensuremath{'}}\ensuremath{\rightarrow}{(1)}^{3}{A}_{2}^{\ensuremath{'}}$ origin Rapid intersystem crossing from the defect's triplet to singlet manifolds explains the observed short excited-state lifetime and very low quantum yield New experimental results reveal smaller intrinsic spectral bandwidths than previously recognized, interpreted in terms of spectral narrowing and zero-phonon-line shifting induced by the Jahn-Teller effect Different types of computational methods are applied to map out the complex triplet and singlet defect manifolds, including the doubly ionized formulation of the equation-of-motion coupled-cluster theory that is designed to deal with the open-shell nature of defect states, and mixed quantum-mechanics/molecular-mechanics schemes enabling 5763-atom simulations Two other energetically feasible spectral assignments from among the singlet and triplet manifolds are considered, but ruled out based on inappropriate photochemical properties

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Photoluminescence, photophysics, and photochemistry of the V B − defect in hexagonal boron nitride

The openCARP simulation environment for cardiac electrophysiology.

The Molecular Sciences Software Institute's (MolSSI) Quantum Chemistry Archive (QCArchive) project is an umbrella name that covers both a central server hosted by MolSSI for community data and the Python-based software infrastructure that powers automated computation and storage of quantum chemistry results.The MolSSI-hosted central server provides the computational molecular sciences community a location to freely access tens of millions of quantum chemistry computations for machine learning, methodology assessment, force-field fitting, and more through a Python interface.Facile, user-friendly mining of the centrally archived quantum chemical data also can be achieved through web applications found at https://qcarchive.molssi.org.The software infrastructure can be used as a standalone platform to compute, structure, and distribute hundreds of millions of quantum chemistry computations for individuals or groups of researchers at any scale.The QCArchive Infrastructure is open-source (BSD-3C), code repositories can be found at https://github.com/MolSSI, and releases can be downloaded via PyPI and Conda.

The MolSSI QCArchive project: An open‐source platform to compute, organize, and share quantum chemistry data

Background and Objective Cardiac electrophysiology is a medical specialty with a long and rich tradition of computational modeling. Nevertheless, no community standard for cardiac electrophysiology simulation software has evolved yet. Here, we present the openCARP simulation environment as one solution that could foster the needs of large parts of this community. Methods and Results openCARP and the Python-based carputils framework allow developing and sharing simulation pipelines which automate in silico experiments including all modeling and simulation steps to increase reproducibility and productivity. The continuously expanding openCARP user community is supported by tailored infrastructure. Documentation and training material facilitate access to this complementary research tool for new users. After a brief historic review, this paper summarizes requirements for a high-usability electrophysiology simulator and describes how openCARP fulfills them. We introduce the openCARP modeling workflow in a multi-scale example of atrial fibrillation simulations on single cell, tissue, organ and body level and finally outline future development potential. Conclusion As an open simulator, openCARP can advance the computational cardiac electrophysiology field by making state-of-the-art simulations accessible. In combination with the carputils framework, it offers a tailored software solution for the scientific community and contributes towards increasing use, transparency, standardization and reproducibility of in silico experiments.

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The openCARP Simulation Environment for Cardiac Electrophysiology

A discussion of many of the recently implemented features of GAMESS (General Atomic and Molecular Electronic Structure System) and LibCChem (the C++ CPU/GPU library associated with GAMESS) is presented. These features include fragmentation methods such as the fragment molecular orbital, effective fragment potential and effective fragment molecular orbital methods, hybrid MPI/OpenMP approaches to Hartree-Fock, and resolution of the identity second order perturbation theory. Many new coupled cluster theory methods have been implemented in GAMESS, as have multiple levels of density functional/tight binding theory. The role of accelerators, especially graphical processing units, is discussed in the context of the new features of LibCChem, as it is the associated problem of power consumption as the power of computers increases dramatically. The process by which a complex program suite such as GAMESS is maintained and developed is considered. Future developments are briefly summarized.

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Recent developments in the general atomic and molecular electronic structure system.

The quasi-atomic orbital (QUAO) bonding analysis is used to study intramolecular hydrogen bonding (IMHB) in salicylic acid and an intermediate that is crucial to the synthesis of aspirin. The bonding analysis rigorously explores IMHB through directly accessing information that is intrinsic to the molecular wave function, thereby bypassing the need for intrinsically biased methods. The variables that affect the strength of IMHB are determined using kinetic bond orders, QUAO populations, and QUAO hybridizations. Important properties include both the interatomic distance between hydrogen and oxygen participating in the IMHB and the hybridization on the oxygen. The bonding analysis further shows that each intramolecular hydrogen bond is a four-electron three-center bond. The bonding analysis is used to understand how aromatic reactivity changes due to the effect of functional groups on the aromatic ring.

Intramolecular hydrogen bonding analysis.

The time to solution and parallel efficiency of several commonly used electronic structure methods (Hartree-Fock, density functional theory, second order perturbation theory, resolution of the identity second order perturbation theory, coupled cluster) are evaluated on both the Intel Xeon Haswell and the Intel Xeon Phi Knights Landing (KNL) architectures. The Haswell completes the benchmark calculations with a faster time to solution than the KNL for all molecules and methods tested. While the Haswell exhibits an average speedup of at least 3.5 relative to the KNL for all nonthreaded computations, the KNL has a better parallel efficiency than the Haswell with increasing core counts. The architectures are further tested using a more computationally costly coupled cluster method on a transition state reaction. The Haswell appears to be the best choice to minimize the time to solution, though for very large systems and high levels of theory that require memory intensive processes the superior memory hierarchy and larger on node memory of the KNL can make it a better choice. These results are used to showcase aspects of novel architectures that will increase efficiency for quantum chemistry applications.

Taylor Harville

Papers

Recent developments in the general atomic and molecular electronic structure system.

Intramolecular hydrogen bonding analysis.

Electronic Structure Theory Calculations Using Modern Architectures: KNL vs Haswell.