PubChem 2019 update: improved access to chemical data

Kim, Sunghwan; Jie, Chen; Cheng, Tiejun; Gindulytė, Asta; He, Jia; He, Siqian; Li, Qingliang; Shoemaker, Benjamin A.; Thiessen, Paul A.; Yu, Bo; Zaslavsky, Leonid; Zhang, Jian; Bolton, Evan

doi:10.1093/nar/gky1033

Cited by 2,647 publications

(2,136 citation statements)

References 27 publications

(37 reference statements)

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“…Additionally, due to the comparatively large size of the cocrystallized peptidomimetic inhibitor, compounds with a molecular weight above 500 g/mol were considered. As templates, a substructure of the cocrystallized ligand of SARS-CoV-2 protease (compound 4), a cocrystallized ligand (compound 9) and three substructures of the equivalent SARS-CoV-1 protease inhibitors (compounds 1, 3, and 7), as well as four known SARS-CoV-1 inhibitors with binding affinity below 10 µM (compounds 2, 5, 6, and 8) derived from the PubChem database39…”

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confidence: 99%

Inhibitors for Novel Coronavirus Protease Identified by Virtual Screening of 687 Million Compounds

Fischer

SmieÅ¡ko²

2020

Preprint

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The rapid outbreak of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China followed by its spread around the world poses a serious global concern for public health with almost 90000 people infected and thousands of fatalities. To conquer viral infections, the inhibition of proteases essential for proteolytic processing of viral polyproteins is a conventional therapeutic strategy. To this date, no specific drugs or vaccines are available to treat SARS-CoV-2 despite its close relation to the SARS-CoV-1 virus that caused a similar epidemic in 2003. Thus, there remains an urgent need for the development of specific antiviral therapeutics to conquer SARS-CoV-2. In order to find novel inhibitors, we computationally screened a compound library of over 687 million compounds for binding at the recently solved crystal structure of the main protease of SARS-CoV-2. A screening of such a vast chemical space for SARS-CoV-2 protease inhibitors has not been reported before. After shape screening, two docking protocols were applied followed by the determination of pharmacokinetically relevant molecular descriptors to narrow down the initial hits. Next, molecular 1 dynamics simulations were conducted to validate the stability of docked binding modes and comprehensively quantify ligand binding energies. After evaluation of off-target binding, we report a list of 11 drug-like compounds with improved binding free energy to the target protease in contrast to the cocrystallized peptidomimetic lead compound that suffers from poor pharmacokinetic properties. Furthermore, we identified one potent binder with comparable properties from the natural compound library.

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confidence: 99%

Inhibitors for Novel Coronavirus Protease Identified by Virtual Screening of 687 Million Compounds

Fischer

SmieÅ¡ko²

2020

Preprint

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“…[36] Data collection and curation:P ubChem:C Ra rtefact assays for Al-phaScreen, FRET and TR-FRET were collected from PubChem Bioassay. [37] Assay AIDs and compositions are shown in Ta ble 4. Active and inactive compounds were classified as CIATs and NCIATs ,r espectively and compounds with inconclusive activities were not considered further.C ompounds which were CIATs and NCIATs depending of the assay were also discarded.…”

Section: Methodsmentioning

confidence: 99%

Identification of Compounds That Interfere with High‐Throughput Screening Assay Technologies

et al. 2019

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A significant challenge in high‐throughput screening (HTS) campaigns is the identification of assay technology interference compounds. A Compound Interfering with an Assay Technology (CIAT) gives false readouts in many assays. CIATs are often considered viable hits and investigated in follow‐up studies, thus impeding research and wasting resources. In this study, we developed a machine‐learning (ML) model to predict CIATs for three assay technologies. The model was trained on known CIATs and non‐CIATs (NCIATs) identified in artefact assays and described by their 2D structural descriptors. Usual methods identifying CIATs are based on statistical analysis of historical primary screening data and do not consider experimental assays identifying CIATs. Our results show successful prediction of CIATs for existing and novel compounds and provide a complementary and wider set of predicted CIATs compared to BSF, a published structure‐independent model, and to the PAINS substructural filters. Our analysis is an example of how well‐curated datasets can provide powerful predictive models despite their relatively small size.

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“…In particular, we focused on a training set containing the Full Paper www.molinf.com fragment "C(F)(F)N," which was taken from PubChem. [2,42] Because of the extremely high diversity of chemical structures in PubChem, we used multiple steps to extract training molecules: (1) we screened molecules in PubChem continuously until 5,000 molecules having the desired fragment were found (screening a total of over 36,940,000 molecules); (2) we reduced the number of molecules to 3,860 that consisted only of C, O, N, F, and/or S atoms; (3) we finally extracted 2,485 molecules by filtering out those that had more than six F atoms or included more than one molecule in a single SMILES string (SMILES string with "."). The final training set was formed by the union of these selected PubChem molecules and the set of PG polymers.…”

Section: Training the Generatormentioning

confidence: 99%

iQSPR in XenonPy: A Bayesian Molecular Design Algorithm

Lambard

Liu

et al. 2019

Molecular Informatics

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iQSPR is an inverse molecular design algorithm based on Bayesian inference that was developed in our previous study. Here, the algorithm is integrated in Python as a new module called iQSPR-X in the all-in-one materials informatics platform XenonPy. Our new software provides a flexible, easy-to-use, and extensible platform for users to build customized molecular design algorithms using pre-set modules and a pre-trained model library in XenonPy. In this paper, we describe key features of iQSPR-X and provide guidance on its use, illustrated by an application to a polymer design that targets a specific range of bandgap and dielectric constant.

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PubChem 2019 update: improved access to chemical data

Cited by 2,647 publications

References 27 publications

Inhibitors for Novel Coronavirus Protease Identified by Virtual Screening of 687 Million Compounds

Inhibitors for Novel Coronavirus Protease Identified by Virtual Screening of 687 Million Compounds

Identification of Compounds That Interfere with High‐Throughput Screening Assay Technologies

iQSPR in XenonPy: A Bayesian Molecular Design Algorithm

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