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Assessment of drug cardiatoxicity is crucial in the development of new compounds and is typically addressed by evaluating the blockade they cause in the potassium human ether-a-go-go related gene (hERG) channels. Our objective is to develop a classifier to determine the preference for binding to the different states of a drug. We created a set of 2600 virtual blockers with different affinities and kinetics to the conformational states of the channel divided into 13 classes. Simulations were carried out using three stimulation protocols that enhance the probabilities of the channel to occupy a certain state. Three measurements were taken for each of the simulations: $IC_{50}$ , the recovery constant of the $I_{Kr}$ potassium current and an estimation of the time required for the simulation to be stable. Therefore, we obtained 9 variables for each of the blockers studied. A two-step classifier was developed, trained and evaluated. First, $we$ used support vector machines on the $IC_{50}$ to separate the 13 classes into three groups with 4, 5 and 4 classes respectively. Secondly, we used neural networks on each group with all the variables to finally classify the blockers. The three classifiers obtained an overall accuracy on the test group of 90.83, 88.66 and 89.16% for each of the groups respectively.

This work provides a novel concept for a voltage-programmable quantum memory with capability to tune superconducting qubits. Electric field-induced ion transport in Superconductor-Ionic hetero-structures programs the tunneling current and Hamiltonian [1]. This work develops a model of the field-effect in the superconducting state at resonance. We use the model to simulate designs of memory devices programmable with sub-50 milli-Volt and sub-nanosecond pulses while maintaining 1–10 year storage time [2]. Quantum Master Equation (QME) simulations [3] support tuning of the time dependent quantum coherence properties in these devices with a programmed Hamiltonian determined by unique ion energy levels. To obtain further insights, we construct Niobium/AlO x /Ionic-HfO x /AlO x /Niobium devices and characterize the effects of key experimental parameters. The work provides device technology advances applicable to cryogenic computing and quantum information [4-6].

The inherent complexity in utilizing and programming high performance computing (HPC) systems is the main obstacle to widespread exploitation of HPC resources and technologies in the Department of Defense (DoD). Consequently, there is the persistent need to simplify the programming interface for the generic user. This need is particularly acute in the Signal/Image Processing (SIP), Integrated Modeling and Test Environments (IMT), and related DoD communities where typical users have heterogeneous unconsolidated needs. Mastering the complexity of traditional programming tools (C, MPI, etc.) is often seen as a diversion of energy that could be applied to the study of the given scientific domain. Many SIP users instead prefer high-level languages (HLLs) within integrated development environments, such as MATLAB. We report on our collaborative effort to use a HLL distribution for HPC systems called ParaM to optimize and parallelize a compute-intensive Superconducting Quantum Interference Filter (SQIF) application provided by the Navy SPAWAR Systems Center in San Diego, CA. ParaM is an open-source HLL distribution developed at the Ohio Supercomputer Center (OSC), and includes support for processor architectures not supported by MATLAB (e.g., Itanium and POWER5) as well as support for high-speed interconnects (e.g., InfiniBand and Myrinet). We make use of ParaM installations available at the Army Research Laboratory (ARL) DoD Supercomputing Resource Center (DSRC) and OSC to perform a successful optimization/parallelization of the SQIF application. This optimization/parallelization may be used to assess the feasibility of using SQIF devices as extremely sensitive detectors for electromagnetic radiation which is of great importance to the Navy and DoD in general.