Dissimilarity-Based Algorithms for Selecting Structurally Diverse Sets of Compounds
- 1 October 1999
- journal article
- research article
- Published by Mary Ann Liebert Inc in Journal of Computational Biology
- Vol. 6 (3-4) , 447-457
- https://doi.org/10.1089/106652799318382
Abstract
This paper commences with a brief introduction to modern techniques for the computational analysis of molecular diversity and the design of combinatorial libraries. It then reviews dissimilarity-based algorithms for the selection of structurally diverse sets of compounds in chemical databases. Procedures are described for selecting a diverse subset of an entire database, and for selecting diverse combinatorial libraries using both reagent-based and product-based selection.Keywords
This publication has 43 references indexed in Scilit:
- An Efficient Implementation of Distance-Based Diversity Measures Based on k−d TreesJournal of Chemical Information and Computer Sciences, 1998
- Balancing Representativeness Against Diversity using Optimizable K-Dissimilarity and Hierarchical ClusteringJournal of Chemical Information and Computer Sciences, 1998
- Virtual Compound Libraries: A New Approach to Decision Making in Molecular Discovery ResearchJournal of Chemical Information and Computer Sciences, 1998
- OptiSim: An Extended Dissimilarity Selection Method for Finding Diverse Representative SubsetsJournal of Chemical Information and Computer Sciences, 1997
- Stochastic Algorithms for Maximizing Molecular DiversityJournal of Chemical Information and Computer Sciences, 1997
- Exhaustive enumeration of molecular substructuresJournal of Computational Chemistry, 1997
- The Information Content of 2D and 3D Structural Descriptors Relevant to Ligand-Receptor BindingJournal of Chemical Information and Computer Sciences, 1997
- Descriptors for diversity analysisPerspectives in Drug Discovery and Design, 1996
- Use of Structure−Activity Data To Compare Structure-Based Clustering Methods and Descriptors for Use in Compound SelectionJournal of Chemical Information and Computer Sciences, 1996
- Finding maximum cliques in arbitrary and in special graphsComputing, 1991