PubChem3D: Similar conformers

National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, 8600 Rockville Pike, Bethesda, MD 20894, USA. .
Journal of Cheminformatics (Impact Factor: 4.55). 05/2011; 3(1):13. DOI: 10.1186/1758-2946-3-13
Source: PubMed


PubChem 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.
The 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.
The "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.


Available from: Evan Bolton
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    • "Because the 2-D molecular similarity computation is very fast (typically at a rate of one million pair-wise comparisons per second per CPU core), it is appropriate for searching a large database like PubChem. However, there are many diverse chemical structures with similar biological efficacies against targets available in PubChem that can be difficult to interrelate using traditional 2-D similarity methods [8-11]. To assist in biological activity analysis of these molecules, a new layer called PubChem3D [8-15] was added to PubChem. "
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    ABSTRACT: Background PubChem is a free and publicly available resource containing substance descriptions and their associated biological activity information. PubChem3D is an extension to PubChem containing computationally-derived three-dimensional (3-D) structures of small molecules. All the tools and services that are a part of PubChem3D rely upon the quality of the 3-D conformer models. Construction of the conformer models currently available in PubChem3D involves a clustering stage to sample the conformational space spanned by the molecule. While this stage allows one to downsize the conformer models to more manageable size, it may result in a loss of the ability to reproduce experimentally determined “bioactive” conformations, for example, found for PDB ligands. This study examines the extent of this accuracy loss and considers its effect on the 3-D similarity analysis of molecules. Results The conformer models consisting of up to 100,000 conformers per compound were generated for 47,123 small molecules whose structures were experimentally determined, and the conformers in each conformer model were clustered to reduce the size of the conformer model to a maximum of 500 conformers per molecule. The accuracy of the conformer models before and after clustering was evaluated using five different measures: root-mean-square distance (RMSD), shape-optimized shape-Tanimoto (STST-opt) and combo-Tanimoto (ComboTST-opt), and color-optimized color-Tanimoto (CTCT-opt) and combo-Tanimoto (ComboTCT-opt). On average, the effect of clustering decreased the conformer model accuracy, increasing the conformer ensemble’s RMSD to the bioactive conformer (by 0.18 ± 0.12 Å), and decreasing the STST-opt, ComboTST-opt, CTCT-opt, and ComboTCT-opt scores (by 0.04 ± 0.03, 0.16 ± 0.09, 0.09 ± 0.05, and 0.15 ± 0.09, respectively). Conclusion This study shows the RMSD accuracy performance of the PubChem3D conformer models is operating as designed. In addition, the effect of PubChem3D sampling on 3-D similarity measures shows that there is a linear degradation of average accuracy with respect to molecular size and flexibility. Generally speaking, one can likely expect the worst-case minimum accuracy of 90% or more of the PubChem3D ensembles to be 0.75, 1.09, 0.43, and 1.13, in terms of STST-opt, ComboTST-opt, CTCT-opt, and ComboTCT-opt, respectively. This expected accuracy improves linearly as the molecule becomes smaller or less flexible.
    Journal of Cheminformatics 01/2013; 5(1):1. DOI:10.1186/1758-2946-5-1 · 4.55 Impact Factor
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    • "To quantify 3-D similarity between molecules, two 3-D similarity measures are used: shape-Tanimoto (ST) [8,10,11,14,19-21] and color-Tanimoto (CT) [8,10,11,19,20]. The ST score is a measure of shape similarity, which is defined as the following: "
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    ABSTRACT: Background To improve the utility of PubChem, a public repository containing biological activities of small molecules, the PubChem3D project adds computationally-derived three-dimensional (3-D) descriptions to the small-molecule records contained in the PubChem Compound database and provides various search and analysis tools that exploit 3-D molecular similarity. Therefore, the efficient use of PubChem3D resources requires an understanding of the statistical and biological meaning of computed 3-D molecular similarity scores between molecules. Results The present study investigated effects of employing multiple conformers per compound upon the 3-D similarity scores between ten thousand randomly selected biologically-tested compounds (10-K set) and between non-inactive compounds in a given biological assay (156-K set). When the “best-conformer-pair” approach, in which a 3-D similarity score between two compounds is represented by the greatest similarity score among all possible conformer pairs arising from a compound pair, was employed with ten diverse conformers per compound, the average 3-D similarity scores for the 10-K set increased by 0.11, 0.09, 0.15, 0.16, 0.07, and 0.18 for STST-opt, CTST-opt, ComboTST-opt, STCT-opt, CTCT-opt, and ComboTCT-opt, respectively, relative to the corresponding averages computed using a single conformer per compound. Interestingly, the best-conformer-pair approach also increased the average 3-D similarity scores for the non-inactive–non-inactive (NN) pairs for a given assay, by comparable amounts to those for the random compound pairs, although some assays showed a pronounced increase in the per-assay NN-pair 3-D similarity scores, compared to the average increase for the random compound pairs. Conclusion These results suggest that the use of ten diverse conformers per compound in PubChem bioassay data analysis using 3-D molecular similarity is not expected to increase the separation of non-inactive from random and inactive spaces “on average”, although some assays show a noticeable separation between the non-inactive and random spaces when multiple conformers are used for each compound. The present study is a critical next step to understand effects of conformational diversity of the molecules upon the 3-D molecular similarity and its application to biological activity data analysis in PubChem. The results of this study may be helpful to build search and analysis tools that exploit 3-D molecular similarity between compounds archived in PubChem and other molecular libraries in a more efficient way.
    Journal of Cheminformatics 11/2012; 4(1):28. DOI:10.1186/1758-2946-4-28 · 4.55 Impact Factor
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    ABSTRACT: PubChem provides a 3-D neighboring relationship, which involves finding the maximal shape overlap between two static compound 3-D conformations, a computationally intensive step. It is highly desirable to avoid this overlap computation, especially if it can be determined with certainty that a conformer pair cannot meet the criteria to be a 3-D neighbor. As such, PubChem employs a series of pre-filters, based on the concept of volume, to remove approximately 65% of all conformer neighbor pairs prior to shape overlap optimization. Given that molecular volume, a somewhat vague concept, is rather effective, it leads one to wonder: can the existing PubChem 3-D neighboring relationship, which consists of billions of shape similar conformer pairs from tens of millions of unique small molecules, be used to identify additional shape descriptor relationships? Or, put more specifically, can one place an upper bound on shape similarity using other "fuzzy" shape-like concepts like length, width, and height? Using a basis set of 4.18 billion 3-D neighbor pairs identified from single conformer per compound neighboring of 17.1 million molecules, shape descriptors were computed for all conformers. These steric shape descriptors included several forms of molecular volume and shape quadrupoles, which essentially embody the length, width, and height of a conformer. For a given 3-D neighbor conformer pair, the volume and each quadrupole component (Qx, Qy, and Qz) were binned and their frequency of occurrence was examined. Per molecular volume type, this effectively produced three different maps, one per quadrupole component (Qx, Qy, and Qz), of allowed values for the similarity metric, shape Tanimoto (ST) ≥ 0.8.The efficiency of these relationships (in terms of true positive, true negative, false positive and false negative) as a function of ST threshold was determined in a test run of 13.2 billion conformer pairs not previously considered by the 3-D neighbor set. At an ST ≥ 0.8, a filtering efficiency of 40.4% of true negatives was achieved with only 32 false negatives out of 24 million true positives, when applying the separate Qx, Qy, and Qz maps in a series (Qxyz). This efficiency increased linearly as a function of ST threshold in the range 0.8-0.99. The Qx filter was consistently the most efficient followed by Qy and then by Qz. Use of a monopole volume showed the best overall performance, followed by the self-overlap volume and then by the analytic volume.Application of the monopole-based Qxyz filter in a "real world" test of 3-D neighboring of 4,218 chemicals of biomedical interest against 26.1 million molecules in PubChem reduced the total CPU cost of neighboring by between 24-38% and, if used as the initial filter, removed from consideration 48.3% of all conformer pairs at almost negligible computational overhead. Basic shape descriptors, such as those embodied by size, length, width, and height, can be highly effective in identifying shape incompatible compound conformer pairs. When performing a 3-D search using a shape similarity cut-off, computation can be avoided by identifying conformer pairs that cannot meet the result criteria. Applying this methodology as a filter for PubChem 3-D neighboring computation, an improvement of 31% was realized, increasing the average conformer pair throughput from 154,000 to 202,000 per second per CPU core.
    Journal of Cheminformatics 07/2011; 3(1):25. DOI:10.1186/1758-2946-3-25 · 4.55 Impact Factor
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