Erratum to: The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching

Willighagen, Egon; Mayfield, John W.; Alvarsson, Jonathan; Berg, Arvid; Carlsson, Lars; Jeliazkova, Nina; Kühn, Stefan; Pluskal, Tomáš; Rojas-Chertó, Miquel; Spjuth, Ola; Torrance, Gilleain M.; Evelo, Chris T.; Guha, Rajarshi; Steinbeck, Christoph

doi:10.1186/s13321-017-0231-1

Cited by 33 publications

(19 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The client-side was build using HTML, CSS, and JavaScript in an AngularJS framework. The Chemistry Development Kit (CDK), , MzJava, and JGraphT were used as third-party libraries. Prior to the analysis, preprocessing of the data is required to convert the chemical structures into chemical graphs and for the creation of the target and decoy databases (Figure A).…”

Section: Methodsmentioning

confidence: 99%

Automatic Annotation and Dereplication of Tandem Mass Spectra of Peptidic Natural Products

et al. 2020

View full text Add to dashboard Cite

The various bioactivity types and potencies of peptidic natural products (PNPs) are of high interest for the development of new drugs. In particular, the intrinsic antibiotic properties of PNPs appear essential to combat antimicrobial resistance that is currently threatening the world. The first steps in dereplication and characterization of PNPs often involve tandem mass spectrometry (MS/MS). However, such structurally complex peptides challenge the interpretation of MS/MS results. Only a few software solutions are dedicated to PNP analysis but with a mutually exclusive focus on dereplication or annotation. Hence, key functionalities such as automatic peak annotation or statistically validated scoring systems to support the characterization/identification processes are missing. Here, we present NRPro, a new MS/ MS analysis platform that overcomes some limitations of the existing software and provides a comprehensive toolset for both automatic annotation and dereplication of PNPs.

show abstract

Section: Methodsmentioning

confidence: 99%

Automatic Annotation and Dereplication of Tandem Mass Spectra of Peptidic Natural Products

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The Assay Central software has been previously described. ,− We utilized Assay Central to prepare and merge datasets collated in Molecular Notebook and generate Bayesian machine learning models using the ECFP6 descriptor , from the CDK library . This software first employs a series of rules for the detection of problem data that is corrected by a combination of automated structure standardization (removing salts, neutralizing unbalanced charges, merging duplicate structures with finite activities) and identifying advanced problems to be resolved by human re-curation.…”

Section: Experimental Sectionmentioning

confidence: 99%

Using Bibliometric Analysis and Machine Learning to Identify Compounds Binding to Sialidase-1

Klein

Baker²,

Foil

et al. 2021

ACS Omega

View full text Add to dashboard Cite

Rare diseases impact hundreds of millions of individuals worldwide. However, few therapies exist to treat the rare disease population because financial resources are limited, the number of patients affected is low, bioactivity data is often nonexistent, and very few animal models exist to support preclinical development efforts. Sialidosis is an ultrarare lysosomal storage disorder in which mutations in the NEU1 gene result in the deficiency of the lysosomal enzyme sialidase-1. This enzyme catalyzes the removal of sialic acid moieties from glycoproteins and glycolipids. Therefore, the defective or deficient protein leads to the buildup of sialylated glycoproteins as well as several characteristic symptoms of sialidosis including visual impairment, ataxia, hepatomegaly, dysostosis multiplex, and developmental delay. In this study, we used a bibliometric tool to generate links between lysosomal storage disease (LSD) targets and existing bioactivity data that could be curated in order to build machine learning models and screen compounds in silico. We focused on sialidase as an example, and we used the data curated from the literature to build a Bayesian model which was then used to score compound libraries and rank these molecules for in vitro testing. Two compounds were identified from in vitro testing using microscale thermophoresis, namely sulfameter (K d 2.15 ± 1.02 μM) and mexenone (K d 8.88 ± 4.02 μM), which validated our approach to identifying new molecules binding to this protein, which could represent possible drug candidates that can be evaluated further as potential chaperones for this ultrarare lysosomal disease for which there is currently no treatment. Combining bibliometric and machine learning approaches has the ability to assist in curating small molecule data and model building, respectively, for rare disease drug discovery. This approach also has the capability to identify new compounds that are potential drug candidates.

show abstract

“…We then computed 1024-bit ECFP-6 19 fingerprints using the Chemistry Development Kit (CDK) 20 for all compounds and examined the overlap in fingerprint space (Figure 1c). We observed that the embedding of Canvass compounds in this chemical space shows a resemblance to that of the ChEMBL natural products, as might be expected.…”

Section: Resultsmentioning

confidence: 99%

Canvass: A Crowd-Sourced, Natural-Product Screening Library for Exploring Biological Space

Kearney¹,

Zahoránszky-Kőhalmi²,

Brimacombe³

et al. 2018

ACS Cent. Sci.

Self Cite

View full text Add to dashboard Cite

Natural products and their derivatives continue to be wellsprings of nascent therapeutic potential. However, many laboratories have limited resources for biological evaluation, leaving their previously isolated or synthesized compounds largely or completely untested. To address this issue, the Canvass library of natural products was assembled, in collaboration with academic and industry researchers, for quantitative high-throughput screening (qHTS) across a diverse set of cell-based and biochemical assays. Characterization of the library in terms of physicochemical properties, structural diversity, and similarity to compounds in publicly available libraries indicates that the Canvass library contains many structural elements in common with approved drugs. The assay data generated were analyzed using a variety of quality control metrics, and the resultant assay profiles were explored using statistical methods, such as clustering and compound promiscuity analyses. Individual compounds were then sorted by structural class and activity profiles. Differential behavior based on these classifications, as well as noteworthy activities, are outlined herein. One such highlight is the activity of (−)-2(S)-cathafoline, which was found to stabilize calcium levels in the endoplasmic reticulum. The workflow described here illustrates a pilot effort to broadly survey the biological potential of natural products by utilizing the power of automation and high-throughput screening.

show abstract

Erratum to: The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching

Cited by 33 publications

References 1 publication

Automatic Annotation and Dereplication of Tandem Mass Spectra of Peptidic Natural Products

Automatic Annotation and Dereplication of Tandem Mass Spectra of Peptidic Natural Products

Using Bibliometric Analysis and Machine Learning to Identify Compounds Binding to Sialidase-1

Canvass: A Crowd-Sourced, Natural-Product Screening Library for Exploring Biological Space

Contact Info

Product

Resources

About