ChemDB update—full-text search and virtual chemical space

Chen, Jonathan; Linstead, Erik; Swamidass, S. Joshua; Wang, Dennis; Baldi, Pierre

doi:10.1093/bioinformatics/btm341

Cited by 122 publications

(79 citation statements)

References 12 publications

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“…ChemDB (Chen et al, 2005(Chen et al, , 2007 Annotated commercially available molecules ;5,000,000 WOMBAT Data base (World of Molecular BioAcTivity) (Ekins et al, 2007;Hristozov et al, 2007) Bioactivity data for compounds reported in medicinal chemistry journals 331,872…”

Section: B Ligand Databases For Computer-aided Drug Designmentioning

confidence: 99%

Computational Methods in Drug Discovery

et al. 2013

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Section: B Ligand Databases For Computer-aided Drug Designmentioning

confidence: 99%

Computational Methods in Drug Discovery

et al. 2013

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“…(2) Assuming that there is additional pruning resulting from the XOR bound, is it worth the additional computation cost? And if so, should the various pruning algorithms be combined, and how?…”

Section: A New Proximity Filter: the Xor Approach And Its Similarity mentioning

confidence: 99%

Speeding Up Chemical Database Searches Using a Proximity Filter Based on the Logical Exclusive OR

Baldi

Hirschberg

Nasr

2008

J. Chem. Inf. Model.

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In many large chemoinformatics database systems, molecules are represented by long binary fingerprint vectors whose components record the presence or absence in the molecular graphs of particular functional groups or combinatorial features, such as labeled paths or labeled trees. To speed up database searches, we propose to store with each fingerprint a small header vector containing primarily the result of applying the logical exclusive OR (XOR) operator to the fingerprint vector after modulo wrapping to a smaller number of bits, such as 128 bits. From the XOR headers of two molecules, tight bounds on the intersection and union of their fingerprint vectors can be rapidly obtained, yielding tight bounds on derived similarity measures, such as the Tanimoto measure. During a database search, every time these bounds are unfavorable, the corresponding molecule can be rapidly discarded with no need for further inspection. We derive probabilistic models that allow us to estimate precisely the behavior of the XOR headers and the level of pruning under different conditions in terms of similarity threshold and fingerprint density. These theoretical results are corroborated by experimental results on a large set of molecules. For a Tanimoto threshold of 0.5 (respectively 0.9), this approach requires searching less than 50% (respectively 10%) of the database, leading to typical search speedups of 2 to 3 times over the previous state-of-the-art.

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“…Development of molecular similarity, especially for drug design areas, has been discussed intensively in several publications [24][25][26][27]. ChemDB application, which is an online tool supported with large commercial chemical database, has been commonly used for molecular similarity analysis in drugs design area [28]. Here we have tried this tool in MIP area.…”

Section: Introductionmentioning

confidence: 99%