Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds

Salmina, Elena; Haider, N.; Tetko, Igor V.

doi:10.3390/molecules21010001

Cited by 54 publications

(39 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently an extended version of the program containing 583 manually curated functionalities encoded as SMARTS was published [7]. This list includes also numerous heterocyclic rings and general structural patterns (i.e.…”

Section: Introductionmentioning

confidence: 99%

An algorithm to identify functional groups in organic molecules

Ertl

2017

J Cheminform

109

View full text Add to dashboard Cite

BackgroundThe concept of functional groups forms a basis of organic chemistry, medicinal chemistry, toxicity assessment, spectroscopy and also chemical nomenclature. All current software systems to identify functional groups are based on a predefined list of substructures. We are not aware of any program that can identify all functional groups in a molecule automatically. The algorithm presented in this article is an attempt to solve this scientific challenge.ResultsAn algorithm to identify functional groups in a molecule based on iterative marching through its atoms is described. The procedure is illustrated by extracting functional groups from the bioactive portion of the ChEMBL database, resulting in identification of 3080 unique functional groups.ConclusionsA new algorithm to identify all functional groups in organic molecules is presented. The algorithm is relatively simple and full details with examples are provided, therefore implementation in any cheminformatics toolkit should be relatively easy. The new method allows the analysis of functional groups in large chemical databases in a way that was not possible using previous approaches.Graphical abstract.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0225-z) contains supplementary material, which is available to authorized users.

show abstract

Section: Introductionmentioning

confidence: 99%

An algorithm to identify functional groups in organic molecules

Ertl

2017

J Cheminform

109

View full text Add to dashboard Cite

show abstract

“…Additionally, we performed a functional group analysis using ToxAlerts . ToxAlerts is an analytical feature of OCHEM intended for the identification of potentially toxic functional groups, however it also contains an extended functional group (EFG) category . This category allows the easy identification of the (binary) presence of over 500 different functional groups, of which 472 were present in the dataset.…”

Section: Methodsmentioning

confidence: 99%

Matched Molecular Pair Analysis on Large Melting Point Datasets: A Big Data Perspective

2017

View full text Add to dashboard Cite

A matched molecular pair (MMP) analysis was used to examine the change in melting point (MP) between pairs of similar molecules in a set of ∼275k compounds. We found many cases in which the change in MP (ΔMP) of compounds correlates with changes in functional groups. In line with the results of a previous study, correlations between ΔMP and simple molecular descriptors, such as the number of hydrogen bond donors, were identified. In using a larger dataset, covering a wider chemical space and range of melting points, we observed that this method remains stable and scales well with larger datasets. This MMP‐based method could find use as a simple privacy‐preserving technique to analyze large proprietary databases and share findings between participating research groups.

show abstract

“…Structural analysis involved a common scaffold search, structural similarity grouping, as well as analysis of chemical activity based on the known reactive functional moieties. 15 To estimate the quality of the filters, we used the assessment technique 10 used in our previous publication. 12 Considering that we had a smaller number of assays compared with both previous studies, we calculated the enrichment value (EV GST ) with the following equation: , where HITS i j shows the number of compounds that were active in the i GST/GSH assays (i = 0, 1, 2) and j GST/GSHfree assays (j = 0, 1, 2, 3) simultaneously.…”

Section: Identification Of Additional Experimentalmentioning

confidence: 99%

Identification of Small-Molecule Frequent Hitters of Glutathione S-Transferase–Glutathione Interaction

et al. 2016

View full text Add to dashboard Cite

In high-throughput screening (HTS) campaigns, the binding of glutathione S-transferase (GST) to glutathione (GSH) is used for detection of GST-tagged proteins in protein-protein interactions or enzyme assays. However, many false-positives, so-called frequent hitters (FH), arise that either prevent GST/GSH interaction or interfere with assay signal generation or detection. To identify GST-FH compounds, we analyzed the data of five independent AlphaScreen-based screening campaigns to classify compounds that inhibit the GST/GSH interaction. We identified 53 compounds affecting GST/GSH binding but not influencing His-tag/Ni 2+ -NTA interaction and general AlphaScreen signals. The structures of these 53 experimentally identified GST-FHs were analyzed in chemoinformatic studies to categorize substructural features that promote interference with GST/GSH binding. Here, we confirmed several existing chemoinformatic filters and more importantly extended them as well as added novel filters that specify compounds with anti-GST/GSH activity. Selected compounds were also tested using different antibody-based GST detection technologies and exhibited no interference clearly demonstrating specificity toward their GST/GSH interaction. Thus, these newly described GST-FH will further contribute to the identification of FH compounds containing promiscuous substructures. The developed filters were uploaded to the OCHEM website (http://ochem.eu) and are publicly accessible for analysis of future HTS results.

show abstract

Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds

Cited by 54 publications

References 29 publications

An algorithm to identify functional groups in organic molecules

An algorithm to identify functional groups in organic molecules

Matched Molecular Pair Analysis on Large Melting Point Datasets: A Big Data Perspective

Identification of Small-Molecule Frequent Hitters of Glutathione S-Transferase–Glutathione Interaction

Contact Info

Product

Resources

About