Analytic Space Management for Drug Design Application

Abstract

Demands on efficient drug design have been increasing with the advancement of computing technology and bioinformatics. A variety of information technologies pertaining to drug design have been proposed recently and such technology mostly contributes to drug design research. Molecular docking simulation is a promising application for drug design, and can be realized with current information technology. However although docking simulation and the related information technology have advanced in recent years, scientists still have difficulty finding a suitable parameter set of docking simulations for accuracy of simulation. The parametertuning step takes a long time, and existing computing technology can hardly assist in this step. This is because the parameter-tuning step involves factors that are difficult to automate with computers. In this paper, we propose a new architecture for assisting procedures that require the decisions of scientists, especially when they need to tune parameters in a docking simulation.

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“…Moreover, results of the simulations are stored in a relational database automatically. The researcher analyzes a huge number of dataset on the system [13]. In the fluid dynamics domain, researchers employ not only simulation system such as FLUENT [14] but systems [15][16] that help their experiments.…”

Construction of PSE System for Developing Reinforcement Learning Algorithms

“…Moreover, results of the simulations are stored in a relational database automatically. The researcher analyzes a huge number of dataset on the system [14]. In the fluid dynamics domain, researchers employ not only simulation system such as FLUENT [16] but systems [17][18] that help their experiments.…”