Further Development of an Application Framework for Computational Chemistry (AFCC) Applied to New Drug Discovery

Further Development of an Application Framework for Computational Chemistry (AFCC) Applied to New Drug Discovery

J. Tindle (University of Sunderland – St. Peter's Campus, UK), M. Gray (University of Sunderland – City Campus, UK), R.L. Warrender (University of Sunderland – St. Peter's Campus, UK), K. Ginty (University of Sunderland – St. Peter's Campus, UK) and P.K.D. Dawson (University of Sunderland – City Campus, UK)
DOI: 10.4018/978-1-4666-6252-0.ch006
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Abstract

This chapter describes the performance of a compute cluster applied to solve Three Dimensional (3D) molecular modelling problems. The primary goal of this work is to identify new potential drugs. The chapter focuses upon the following issues: computational chemistry, computational efficiency, task scheduling, and the analysis of system performance. The philosophy of design for an Application Framework for Computational Chemistry (AFCC) is described. Eighteen months after the release of the original chapter, the authors have examined a series of changes adopted which have led to improved system performance. Various experiments have been carried out to optimise the performance of a cluster computer, the results analysed, and the statistics produced are discussed in the chapter.
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2. Molecular Modelling

Molecular modelling software allows the user to select atoms from the periodic table and to place them in a three dimensional workspace. In most modelling systems it is possible to build a three dimensional molecular structure by using a colour graphics user interface (GUI), refer to Figure 1. The initial position of the atoms is normally determined by the user calling upon common sense and experience. In all cases the actual position that the atoms assume in the real world is determined by the Laws of Physics. Computational chemistry is a general name for computer based algorithms that may be used to solve this type of problem. There are numerous algorithms that may be deployed and normally this involves computing the minimum value of an energy function to find the optimum solution.

Figure 1.

Molecular modelling workspace

For complex structures in many cases the rate of convergence is relatively slow and it is therefore often necessary to employ high performance computing methods to produce solutions in a reasonable period of time.

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