John S. Delaney

Finding and filling protein cavities using cellular logic operations

A method for solid-filling protein cavities is presented. The method uses a pattern-recognition technique based on cellular logic operations to distinguish between convex and concave regions of a protein. In doing this it solid fills protein cavities and automatically defines a boundary between cavity and exterior free space. The operations used to fill the cavities also can be used to process the filler to filter out small-scale features. So far the main use of the method has been in visualizing protein active sites for docking. The method can be used to find cavities of a given size range and could be used to find novel protein binding sites.

Virtual screening using binary kernel discrimination : Analysis of pesticide data

This paper discusses the use of binary kernel discrimination (BKD) for identifying potential active compounds in lead-discovery programs. BKD was compared with established virtual screening methods in a series of experiments using pesticide data from the Syngenta corporate database. It was found to be superior to methods based on similarity searching and substructural analysis but inferior to a support vector machine. Similar conclusions resulted from application of the methods to a pesticide data set for which categorical activity data were available.

Targeting Chemical Inputs and Optimising HTS for Agrochemical Discovery

In vivo high throughput screening (HTS) has been adopted by most of the larger crop protection companies as an important tool for the discovery of new agrochemicals. There has been a paradigm shift in capabilities from screening a few thousand compounds a year to several hundred thousand and the quantity of screening sample required has fallen dramatically. The unifying goal now bringing together screens and inputs is the need to maximise the flow of useful information from HTS and thereby minimise the time taken to discover robust leads and new products. This review examines the positive changes that have occurred towards targeted design and selection of chemical inputs for agrochemical discovery over the last ten years and corresponding developments in HTS assays, data analysis and the logistics of compound storage and dispensing.

Modelling iterative compound optimisation using a self-avoiding walk

The optimisation phase is a crucial step in the process of drug development, yet the mechanics of the projects that make it up are poorly understood. Weak documentation of failed projects makes statistical analysis of the factors affecting project performance challenging, so a better approach may be the development of an underlying theory of how projects work. We present a model based on a modified random walk in a relevant chemical space and use it to produce simulations of projects and portfolios of projects. Simulation is used to explore parameters that might affect the performance of a project and shows that they fall into two groups--target and process--that affect the overall performance in distinct ways.