Clustering of Large Databases of Compounds:  Using the MDL “Keys” as Structural Descriptors

McGregor, Malcolm J.; Pallai, Peter V.

doi:10.1021/ci960151e

Cited by 174 publications

(179 citation statements)

References 13 publications

Supporting

Mentioning

176

Contrasting

Order By: Relevance

“…The ISIS keys are small topological substructure fragments, whereas the MACCS keys consist of the ISIS keys plus algorithmically generated more abstract atom-pair descriptors. MDL keys are commonly used when optimizing diversity (McGregor and Pallai, 1997;Roberto Todeschini, 2010). For example, the PubChem data base uses a fingerprint that is 881 bits long to rank substances against a query compound.…”

Section: A Molecular Descriptors/featuresmentioning

confidence: 99%

See 1 more Smart Citation

Computational Methods in Drug Discovery

et al. 2013

View full text Add to dashboard Cite

Section: A Molecular Descriptors/featuresmentioning

confidence: 99%

“…Two structures cluster together if they are in each other's list of nearest neighbors, and they have at least K of their J nearest neighbors in common. The MDL keys also provide a way to eliminate compounds that are least likely to satisfy the drug-likeness criterion (McGregor and Pallai, 1997).…”

Section: B Molecular Fingerprint and Similarity Searchesmentioning

confidence: 99%

Computational Methods in Drug Discovery

et al. 2013

View full text Add to dashboard Cite

“…This molecular fingerprint uses a pre-defined set of definitions and creates molecular fingerprints based on pattern matching of the structure to the defined "key" set. 4) This key based approach relies on the definitions to encapsulate the molecular descriptions. The keys were originally developed for the purpose of database substructure searching.…”

Section: Methodsmentioning

confidence: 99%

A Simple Method to Improve the Odds in Finding 'Lead-Like' Compounds from Chemical Libraries

et al. 2007

View full text Add to dashboard Cite

“…14,21 It has been shown that this method performs well for chemical clustering and is computationally efficient for large databases. 17,22 rEsults…”

Section: Methodsmentioning

confidence: 99%

Assessing the Chemical and Biological Diversity of an Ion Channels Knowledge Database

Ijjaali¹,

Dubus²,

Bourinet³

et al. 2007

Channels

View full text Add to dashboard Cite

KEy worDsion channel, biological space, drug design, chemical diversity, Structure-activity relationships (SAR) AcKnowlEDgEmEntsThe authors express their appreciation to ChemAxon team for providing JChem tools, and their helpful support. We thank Sophie Ollivier and the knowledge management team as well as Dominique Neaud and the IT team for their valuable help during the preparation of this work. We gratefully thank Mary Donlan for her comments and for assisting us with the proofreading of the manuscript. Emmanuel Bourinet is supported by operating grants from the Agence Nationale de la Recherche (ANR-05-NEUR-031-01), the ARC-INCa-2006, the Institut UPSA de la Douleur, the Association Française contre les Myopathies (AFM), and the Fédération pour la Recherche sur le Cerveau (FRC, équipe-ment 2006). ABstrActThe aim of the present work is to assess the chemical and biological diversity of ligands reported in scientific articles or patents to be active against ion channels targets. A specific query of the AurSCOPE Ion Channel knowledge database was constructed to retrieve a set of the most active non-peptide ligands tested in binding or electrophysiology experiments against all ion channel families. A biological activity threshold cutoff expressed by K i , IC 50 , or EC 50 was set to 300 nM. This activity cutoff was selected such that we would retrieve a set of compounds, which contain the most active ligands for all target families, but is a reasonable number to analyze. To encode the chemical space for the entire active dataset (9897 molecules), ChemAxon's chemical fingerprints were computed and optimized and then employed to cluster the dataset at a variety of different similarity thresholds. Concurrently, the exploration of the biological space was performed by associating with each chemical cluster the corresponding target or target family. Tri-dimensional visualization of different voltage-and ligand-gated ion channel families projected into the active chemical space was obtained after a principal components analysis performed using selected molecular descriptors. The findings presented herein give a global picture of the realm of ion channels active ligands and link the knowledge on chemical structures with their respective biological activities.

show abstract

Clustering of Large Databases of Compounds: Using the MDL “Keys” as Structural Descriptors

Cited by 174 publications

References 13 publications

Computational Methods in Drug Discovery

Computational Methods in Drug Discovery

A Simple Method to Improve the Odds in Finding 'Lead-Like' Compounds from Chemical Libraries

Assessing the Chemical and Biological Diversity of an Ion Channels Knowledge Database

Contact Info

Product

Resources

About