An algorithm to identify functional groups in organic molecules

Ertl, Peter

doi:10.1186/s13321-017-0225-z

Cited by 109 publications

(95 citation statements)

References 13 publications

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“…It is worth to mention that AutoWeka includes the state-of-the-art machine-learning algorithms, like SVM, random forest, and logistic regression, among others, so the resulting learning model can be considered as the most suitable for the classification task at hand. For identifying chemical groups (these are different than the features calculated by PaDel descriptor) in peptides and non-peptidic chemical compounds, we used a Python implementation developed for that purpose [ 22 ]. Briefly, molecules in SMILES format were integrated into the python code, and the program named rdkitpy was installed as instructed by the developers of Python program; the code searches for 3080 known chemical groups in molecules.…”

Section: Methodsmentioning

confidence: 99%

Relevant Features of Polypharmacologic Human-Target Antimicrobials Discovered by Machine-Learning Techniques

et al. 2020

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Polypharmacologic human-targeted antimicrobials (polyHAM) are potentially useful in the treatment of complex human diseases where the microbiome is important (e.g., diabetes, hypertension). We previously reported a machine-learning approach to identify polyHAM from FDA-approved human targeted drugs using a heterologous approach (training with peptides and non-peptide compounds). Here we discover that polyHAM are more likely to be found among antimicrobials displaying a broad-spectrum antibiotic activity and that topological, but not chemical features, are most informative to classify this activity. A heterologous machine-learning approach was trained with broad-spectrum antimicrobials and tested with human metabolites; these metabolites were labeled as antimicrobials or non-antimicrobials based on a naïve text-mining approach. Human metabolites are not commonly recognized as antimicrobials yet circulate in the human body where microbes are found and our heterologous model was able to classify those with antimicrobial activity. These results provide the basis to develop applications aimed to design human diets that purposely alter metabolic compounds proportions as a way to control human microbiome.

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Section: Methodsmentioning

confidence: 99%

Relevant Features of Polypharmacologic Human-Target Antimicrobials Discovered by Machine-Learning Techniques

et al. 2020

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“…For the functional group analysis, the fully automated algorithm suggested in (31) was used. The algorithm is based on processing heteroatoms and their environment with the addition of some other functionalities.…”

Section: Methodsmentioning

confidence: 99%

Enhanced taxonomy annotation of antiviral activity data from ChEMBL

et al. 2019

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The discovery of antiviral drugs is a rapidly developing area of medicinal chemistry research. The emergence of resistant variants and outbreaks of poorly studied viral diseases make this area constantly developing. The amount of antiviral activity data available in ChEMBL consistently grows, but virus taxonomy annotation of these data is not sufficient for thorough studies of antiviral chemical space. We developed a procedure for semi-automatic extraction of antiviral activity data from ChEMBL and mapped them to the virus taxonomy developed by the International Committee for Taxonomy of Viruses (ICTV). The procedure is based on the lists of virus-related values of ChEMBL annotation fields and a dictionary of virus names and acronyms mapped to ICTV taxa. Application of this data extraction procedure allows retrieving from ChEMBL 1.6 times more assays linked to 2.5 times more compounds and data points than ChEMBL web interface allows. Mapping of these data to ICTV taxa allows analyzing all the compounds tested against each viral species. Activity values and structures of the compounds were standardized, and the antiviral activity profile was created for each standard structure. Data set compiled using this algorithm was called ViralChEMBL. As case studies, we compared descriptor and scaffold distributions for the full ChEMBL and its `viral’ and `non-viral’ subsets, identified the most studied compounds and created a self-organizing map for ViralChEMBL. Our approach to data annotation appeared to be a very efficient tool for the study of antiviral chemical space.

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“…to construct quantitative structure–activity relationships (QSAR) in order to support drug discovery. In a recent publication [1] Peter Ertl proposed a new purely rule-driven approach to identify FGs of an organic molecule. This effort may be regarded as the first genuine algorithmic method to tackle FG identification in contrast to the common manual FG definition performed by chemists.…”

Section: Introductionmentioning

confidence: 99%

ErtlFunctionalGroupsFinder: automated rule-based functional group detection with the Chemistry Development Kit (CDK)

Fritsch¹,

Neumann²,

Schaub³

et al. 2019

J Cheminform

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The Ertl algorithm for automated functional groups (FG) detection and extraction of organic molecules is implemented on the basis of the Chemistry Development Kit (CDK). A distinct impact of the chosen CDK aromaticity model is demonstrated by an FG analysis of the ChEMBL database compounds. The average performance of less than a millisecond for a single-molecule FG extraction allows for fast processing of even large compound databases.

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An algorithm to identify functional groups in organic molecules

Cited by 109 publications

References 13 publications

Relevant Features of Polypharmacologic Human-Target Antimicrobials Discovered by Machine-Learning Techniques

Relevant Features of Polypharmacologic Human-Target Antimicrobials Discovered by Machine-Learning Techniques

Enhanced taxonomy annotation of antiviral activity data from ChEMBL

ErtlFunctionalGroupsFinder: automated rule-based functional group detection with the Chemistry Development Kit (CDK)

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