rDock: A Fast, Versatile and Open Source Program for Docking Ligands to Proteins and Nucleic Acids

Identification of chemical compounds with specific biological activities is an important step in both chemical biology and drug discovery. When the structure of the intended target is available, one approach is to use molecular docking programs to assess the chemical complementarity of small molecules with the target; such calculations provide a qualitative measure of affinity that can be used in virtual screening (VS) to rank order a list of compounds according to their potential to be active. rDock is a molecular docking program developed at Vernalis for high-throughput VS (HTVS) applications. Evolved from RiboDock, the program can be used against proteins and nucleic acids, is designed to be computationally very efficient and allows the user to incorporate additional constraints and information as a bias to guide docking. This article provides an overview of the program structure and features and compares rDock to two reference programs, AutoDock Vina (open source) and Schrodinger's Glide (commercial). In terms of computational speed for VS, rDock is faster than Vina and comparable to Glide. For binding mode prediction, rDock and Vina are superior to Glide. The VS performance of rDock is significantly better than Vina, but inferior to Glide for most systems unless pharmacophore constraints are used; in that case rDock and Glide are of equal performance. The program is released under the Lesser General Public License and is freely available for download, together with the manuals, example files and the complete test sets, at http://rdock.sourceforge.net

Evolutionary algorithms for the design of stack filters

Non-linear digital filters have been demonstrated to be useful in a variety of situations. But most of them take a higher amount of time to filter an image than linear filters. It is important to obtain fast implementations of non-linear filters, and this became possible when stack filters were introduced. Stack filters provide a new way of representing a large subgroup of non-linear filters specially suitable for fast implementations in VLSI. The difficult problem is to find the stack filter corresponding to a given set of desired properties. The size of the search space makes it impractical to perform exhaustive search. This dissertation implements evolutionary algorithms that have been successful in solving problems for filters with large window sizes. This dissertation demonstrates that selection probabilities can be used to obtain performance measures to compare different filters. It is also shown that greater success is achieved in designing stack filters using tree representations rather than bit string representations in the evolutionary algorithms

A Functional Framework for the Implementation of Genetic Algorithms: Comparing Haskell and Standard ML

We present our experience of developing a generic functional framework for the implementation of genetic algorithms (GAs). We have implemented two versions of the framework, one in Haskell and the other in Standard ML