PubChem3D: Conformer generation

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

44 citations recorded by the source corpus.

BackgroundPubChem, an open archive for the biological activities of small molecules, provides search and analysis tools to assist users in locating desired information. Many of these tools focus on the notion of chemical structure similarity at some level. PubChem3D enables similarity of chemical structure 3-D conformers to augment the existing similarity of 2-D chemical structure graphs. It is also desirable to relate theoretical 3-D descriptions of chemical structures to experimental biological activity. As such, it is important to be assured that the theoretical conformer models can reproduce experimentally determined bioactive conformations. In the present study, we investigate the effects of three primary conformer generation parameters (the fragment sampling rate, the energy window size, and force field variant) upon the accuracy of theoretical conformer models, and determined optimal settings for PubChem3D conformer model generation and conformer sampling.ResultsUsing the software package OMEGA from OpenEye Scientific Software, Inc., theoretical 3-D conformer models were generated for 25,972 small-molecule ligands, whose 3-D structures were experimentally determined. Different values for primary conformer generation parameters were systematically tested to find optimal settings. Employing a greater fragment sampling rate than the default did not improve the accuracy of the theoretical conformer model ensembles. An ever increasing energy window did increase the overall average accuracy, with rapid convergence observed at 10 kcal/mol and 15 kcal/mol for model building and torsion search, respectively; however, subsequent study showed that an energy threshold of 25 kcal/mol for torsion search resulted in slightly improved results for larger and more flexible structures. Exclusion of coulomb terms from the 94s variant of the Merck molecular force field (MMFF94s) in the torsion search stage gave more accurate conformer models at lower energy windows. Overall average accuracy of reproduction of bioactive conformations was remarkably linear with respect to both non-hydrogen atom count ("size") and effective rotor count ("flexibility"). Using these as independent variables, a regression equation was developed to predict the RMSD accuracy of a theoretical ensemble to reproduce bioactive conformations. The equation was modified to give a minimum RMSD conformer sampling value to help ensure that 90% of the sampled theoretical models should contain at least one conformer within the RMSD sampling value to a "bioactive" conformation.ConclusionOptimal parameters for conformer generation using OMEGA were explored and determined. An equation was developed that provides an RMSD sampling value to use that is based on the relative accuracy to reproduce bioactive conformations. The optimal conformer generation parameters and RMSD sampling values determined are used by the PubChem3D project to generate theoretical conformer models.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Physical and Theoretical Chemistry

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Citations in the corpus

Torsional Diffusion for Molecular Conformer Generation (2022, background): “…from chemical heuristics, rules, and databases for significantly faster generation [Lagorce et al., 2009, Cole et al., 2018, Miteva et al., 2010, Bolton et al., 2011, Li et al., 2007]; while these can readily model highly constrained degrees of freedom, they fail to capture the full energy…”
QMugs, quantum mechanical properties of drug-like molecules (2022, background): “…that provide three-dimensional conformational data, for a wide variety of chemical space, at levels of theory higher than classical force fields 32,33 , could boost the performance of machine learning methods in predicting properties from ensembles as well as generative models of conformations.”
Effects of multiple conformers per compound upon 3-D similarity search and bioassay data analysis (2012, methodology): “In theory, one should have a reasonable chance (~90%) to find any biologically accessible conformation within the RMSD sampling distance of the conformer model [6,56].”
PubChem structure–activity relationship (SAR) clusters (2015, methodology): “PubChem3D generates 3-D conformer models for about 92% of chemical records in PubChem, averaging ~110 conformers per compound [17, 24].”
PubChem3D: Biologically relevant 3-D similarity (2011, methodology): “The PubChem3D project [22-25], launched, in part, to help users identify useful structure-activity relationships, generates a theoretical 3-D conformer model [22,23] for each molecule in the PubChem Compound database, whenever it is possible.”
PubChemRDF: towards the semantic annotation of PubChem compound and substance databases (2015, background): “D coordinates [4], physical properties (e.g., molecular weight, XLogP3 [5]), and descriptors (e.g., InChI [6], SMILES [7, 8], IUPAC names [9]), as-is possible.”
PubChem3D: a new resource for scientists (2011, methodology): “Equations 1 and 2 were developed [6] to help prevent using a conformer sampling RMSD that was less than the capability of the OMEGA software to reproduce bioactive ligand conformations.”
PubChem3D: Shape compatibility filtering using molecular shape quadrupoles (2011, background): “D layer, PubChem3D [5-7], which provides a precomputed 3-”
PubChem3D: Diversity of shape (2011, background): “4)] given by the following equation [20]:”
PubChem3D: conformer ensemble accuracy (2013, methodology): “Therefore, in the present study, we investigated the effect of the conformer model clustering upon the accuracy of the conformer models as a follow-up to our previous study [13] in order to address key questions as to the performance of PubChem 3D sampled conformers to reproduce “bioactive” ligand g”
PubChem3D: Similar conformers (2011, methodology): “D descriptions of the 26,157,365 chemical structures covered in this work and found in the Pub-Chem Compound database [1,2] are generated as described in our previous studies [15,28].”