Training a Scoring Function for the Alignment of Small Molecules

A comprehensive data set of aligned ligands with highly similar binding pockets from the Protein Data Bank has been built. Based on this data set, a scoring function for recognizing good alignment poses for small molecules has been developed. This function is based on atoms and hydrogen-bond projected features. The concept is simply that atoms and features of a similar type (hydrogen-bond acceptors/donors and hydrophobic) tend to occupy the same space in a binding pocket and atoms of incompatible types often tend to avoid the same space. Comparison with some recently published results of small molecule alignments shows that the current scoring function can lead to performance better than those of several existing methods

Chemoinformatics Research at the University of Sheffield: A History and Citation Analysis

This paper reviews the work of the Chemoinformatics Research Group in the Department of Information Studies at the University of Sheffield, focusing particularly on the work carried out in the period 1985-2002. Four major research areas are discussed, these involving the development of methods for: substructure searching in databases of three-dimensional structures, including both rigid and flexible molecules; the representation and searching of the Markush structures that occur in chemical patents; similarity searching in databases of both two-dimensional and three-dimensional structures; and compound selection and the design of combinatorial libraries. An analysis of citations to 321 publications from the Group shows that it attracted a total of 3725 residual citations during the period 1980-2002. These citations appeared in 411 different journals, and involved 910 different citing organizations from 54 different countries, thus demonstrating the widespread impact of the Group's work

A computational framework for structure-based drug discovery with GPU acceleration.

Li, Hongjian.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (p. 132-156).Abstracts in English and Chinese.Abstract --- p.iAbstract in Chinese --- p.iiiAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.2Chapter 1.2 --- Objective --- p.2Chapter 1.3 --- Method --- p.3Chapter 1.4 --- Outline --- p.4Chapter 2 --- Background --- p.7Chapter 2.1 --- Overview of the Pharmaceutical Industry --- p.7Chapter 2.2 --- The Process of Modern Drug Discovery --- p.10Chapter 2.2.1 --- Development of an Innovative Idea --- p.10Chapter 2.2.2 --- Establishment of a Project Team --- p.11Chapter 2.2.3 --- Target Identification --- p.11Chapter 2.2.4 --- Hit Identification --- p.12Chapter 2.2.5 --- Lead Identification --- p.13Chapter 2.2.6 --- Lead Optimization --- p.14Chapter 2.2.7 --- Clinical Trials --- p.14Chapter 2.3 --- Drug Discovery via Computational Means --- p.15Chapter 2.3.1 --- Structure-Based Virtual Screening --- p.16Chapter 2.3.2 --- Computational Synthesis of Potent Ligands --- p.20Chapter 2.3.3 --- General-Purpose Computing on GPU --- p.23Chapter 3 --- Approximate Matching of DNA Patterns --- p.26Chapter 3.1 --- Problem Definition --- p.27Chapter 3.2 --- Motivation --- p.28Chapter 3.3 --- Background --- p.30Chapter 3.4 --- Method --- p.32Chapter 3.4.1 --- Binary Representation --- p.32Chapter 3.4.2 --- Agrep Algorithm --- p.32Chapter 3.4.3 --- CUDA Implementation --- p.34Chapter 3.5 --- Experiments and Results --- p.39Chapter 3.6 --- Discussion --- p.44Chapter 3.7 --- Availability --- p.45Chapter 3.8 --- Conclusion --- p.47Chapter 4 --- Structure-Based Virtual Screening --- p.50Chapter 4.1 --- Problem Definition --- p.51Chapter 4.2 --- Motivation --- p.52Chapter 4.3 --- Medicinal Background --- p.52Chapter 4.4 --- Computational Background --- p.59Chapter 4.4.1 --- Scoring Function --- p.59Chapter 4.4.2 --- Optimization Algorithm --- p.65Chapter 4.5 --- Method --- p.68Chapter 4.5.1 --- Scoring Function --- p.69Chapter 4.5.2 --- Inactive Torsions --- p.72Chapter 4.5.3 --- Optimization Algorithm --- p.73Chapter 4.5.4 --- C++ Implementation Tricks --- p.74Chapter 4.6 --- Data --- p.75Chapter 4.6.1 --- Proteins --- p.75Chapter 4.6.2 --- Ligands --- p.76Chapter 4.7 --- Experiments and Results --- p.77Chapter 4.7.1 --- Program Validation --- p.77Chapter 4.7.2 --- Virtual Screening --- p.81Chapter 4.8 --- Discussion --- p.89Chapter 4.9 --- Availability --- p.90Chapter 4.10 --- Conclusion --- p.91Chapter 5 --- Computational Synthesis of Ligands --- p.92Chapter 5.1 --- Problem Definition --- p.93Chapter 5.2 --- Motivation --- p.93Chapter 5.3 --- Background --- p.94Chapter 5.4 --- Method --- p.97Chapter 5.4.1 --- Selection --- p.99Chapter 5.4.2 --- Mutation --- p.102Chapter 5.4.3 --- Crossover --- p.102Chapter 5.4.4 --- Split --- p.103Chapter 5.4.5 --- Merging --- p.104Chapter 5.4.6 --- Drug Likeness Testing --- p.104Chapter 5.5 --- Data --- p.105Chapter 5.5.1 --- Proteins --- p.105Chapter 5.5.2 --- Initial Ligands --- p.107Chapter 5.5.3 --- Fragments --- p.107Chapter 5.6 --- Experiments and Results --- p.109Chapter 5.6.1 --- Binding Conformation --- p.112Chapter 5.6.2 --- Free Energy and Molecule Weight --- p.115Chapter 5.6.3 --- Execution Time --- p.116Chapter 5.6.4 --- Support for Phosphorus --- p.116Chapter 5.7 --- Discussion --- p.120Chapter 5.8 --- Availability --- p.123Chapter 5.9 --- Conclusion --- p.123Chapter 5.10 --- Personal Contribution --- p.124Chapter 6 --- Conclusion --- p.125Chapter 6.1 --- Conclusion --- p.125Chapter 6.2 --- Future Work --- p.128Chapter A --- Publications --- p.130Chapter A.1 --- Conference Papers --- p.130Chapter A.2 --- Journal Papers --- p.131Bibliography --- p.13