Abstract. The CASTEP code for first principles electronic structure calculations will be described. A brief, nontechnical overview will be given and some of the features and capabilities highlighted. Some features which are unique to CASTEP will be described and near-future development plans outlined.

First-principles simulation, meaning density-functional theory calculations with plane waves and pseudopotentials, has become a prized technique in condensed-matter theory. Here I look at the basics of the suject, give a brief review of the theory, examining the strengths and weaknesses of its implementation, and illustrating some of the ways simulators approach problems through a small case study. I also discuss why and how modern software design methods have been used in writing a completely new modular version of the CASTEP code.

Ab initio plane-wave electronic structure calculations are widely used in the study of bulk materials. A technique for the projection of plane-wave states onto a localized basis set is used to calculate atomic charges and bond populations by means of Mulliken analysis. We analyze a number of simple bulk crystals and find correlations of overlap population with covalency of bonding and bond strength, and effective valence charge with ionicity of bonding. Thus, we show that the techniques described in this paper may be usefully applied in the field of solid state physics. ͓S0163-1829͑96͒07847-2͔

In this article, we discuss the application of the Gaussian Process method for the prediction of absorption, distribution, metabolism, and excretion (ADME) properties. On the basis of a Bayesian probabilistic approach, the method is widely used in the field of machine learning but has rarely been applied in quantitative structure-activity relationship and ADME modeling. The method is suitable for modeling nonlinear relationships, does not require subjective determination of the model parameters, works for a large number of descriptors, and is inherently resistant to overtraining. The performance of Gaussian Processes compares well with and often exceeds that of artificial neural networks. Due to these features, the Gaussian Processes technique is eminently suitable for automatic model generation-one of the demands of modern drug discovery. Here, we describe the basic concept of the method in the context of regression problems and illustrate its application to the modeling of several ADME properties: blood-brain barrier, hERG inhibition, and aqueous solubility at pH 7.4. We also compare Gaussian Processes with other modeling techniques.

No abstract

No abstract

No abstract

No abstract