PubChem3D: Similar conformers

By Evan E. Bolton, Sunghwan Kim, Stephen H. Bryant. 2011

30 citations recorded by the source corpus.

BackgroundPubChem is a free and open public resource for the biological activities of small molecules. With many tens of millions of both chemical structures and biological test results, PubChem is a sizeable system with an uneven degree of available information. Some chemical structures in PubChem include a great deal of biological annotation, while others have little to none. To help users, PubChem pre-computes "neighboring" relationships to relate similar chemical structures, which may have similar biological function. In this work, we introduce a "Similar Conformers" neighboring relationship to identify compounds with similar 3-D shape and similar 3-D orientation of functional groups typically used to define pharmacophore features.ResultsThe first two diverse 3-D conformers of 26.1 million PubChem Compound records were compared to each other, using a shape Tanimoto (ST) of 0.8 or greater and a color Tanimoto (CT) of 0.5 or greater, yielding 8.16 billion conformer neighbor pairs and 6.62 billion compound neighbor pairs, with an average of 253 "Similar Conformers" compound neighbors per compound. Comparing the 3-D neighboring relationship to the corresponding 2-D neighboring relationship ("Similar Compounds") for molecules such as caffeine, aspirin, and morphine, one finds unique sets of related chemical structures, providing additional significant biological annotation. The PubChem 3-D neighboring relationship is also shown to be able to group a set of non-steroidal anti-inflammatory drugs (NSAIDs), despite limited PubChem 2-D similarity.In a study of 4,218 chemical structures of biomedical interest, consisting of many known drugs, using more diverse conformers per compound results in more 3-D compound neighbors per compound; however, the overlap of the compound neighbor lists per conformer also increasingly resemble each other, being 38% identical at three conformers and 68% at ten conformers. Perhaps surprising is that the average count of conformer neighbors per conformer increases rather slowly as a function of diverse conformers considered, with only a 70% increase for a ten times growth in conformers per compound (a 68-fold increase in the conformer pairs considered).Neighboring 3-D conformers on the scale performed, if implemented naively, is an intractable problem using a modest sized compute cluster. Methodology developed in this work relies on a series of filters to prevent performing 3-D superposition optimization, when it can be determined that two conformers cannot possibly be a neighbor. Most filters are based on Tanimoto equation volume constraints, avoiding incompatible conformers; however, others consider preliminary superposition between conformers using reference shapes.ConclusionThe "Similar Conformers" 3-D neighboring relationship locates similar small molecules of biological interest that may go unnoticed when using traditional 2-D chemical structure graph-based methods, making it complementary to such methodologies. The computational cost of 3-D similarity methodology on a wide scale, such as PubChem contents, is a considerable issue to overcome. Using a series of efficient filters, an effective throughput rate of more than 150,000 conformers per second per processor core was achieved, more than two orders of magnitude faster than without filtering.

Subfields: Computational Theory and Mathematics, Molecular Biology, Spectroscopy

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Citations in the corpus

PubChem structure–activity relationship (SAR) clusters (2015, methodology): “In addition to the 2-D similarity measure using the PubChem fingerprint and Tanimoto equation, PubChem uses two 3-D similarity metrics: shape-Tanimoto (ST) [19, 21, 22, 25–28] and color-Tanimoto (CT) [19, 21, 22, 26].”
Effects of multiple conformers per compound upon 3-D similarity search and bioassay data analysis (2012, methodology): “This log/linear behavior is similar to that observed in our earlier work [8], where a corresponding increase in the logarithmic number of conformers resulted in a linear increase of 3-D similarity neighbors.”
PubChem3D: Shape compatibility filtering using molecular shape quadrupoles (2011, methodology): “As such, PubChem employs a series of filters, based on the concept of volume, to effectively ignore approximately 65% of all conformer neighbor pairs during 3-D neighboring, thus dramatically accelerating processing [7].”
PubChem3D: conformer ensemble accuracy (2013, methodology): “To assist in biological activity analysis of these molecules, a new layer called PubChem3D [8-15] was added to PubChem.”
PubChem3D: Biologically relevant 3-D similarity (2011, methodology): “In the first part of this study, the first diverse conformer [24] for each of the 734,486 CIDs were downloaded.”
PubChem3D: a new resource for scientists (2011, methodology): “A diverse ordering of conformers is provided for each compound conformer ensemble [8,39,40].”
PubChemRDF: towards the semantic annotation of PubChem compound and substance databases (2015, unknown): “D Color Tanimoto (CT) [44].”