Building a BioChemformatics Database

Jensen, Jan Holst; Høeg-Jensen, Thomas; Padkjær, Søren Berg

doi:10.1021/ci800128b

Cited by 8 publications

(15 citation statements)

References 15 publications

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“…Furthermore, any notations mentioned in the Peptide section can be used for protein representations. In 2008, the Protein Line Notation (PLN) [16] was created by Biochemfusion and is implemented in PubChem. Pseudo-atoms were used to represent a simplified version of a residue structure, which enabled a lossless conversion between chemistry and sequence formats.…”

Section: Proteinsmentioning

confidence: 99%

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Molecular representations in AI-driven drug discovery: a review and practical guide

et al. 2020

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The technological advances of the past century, marked by the computer revolution and the advent of high-throughput screening technologies in drug discovery, opened the path to the computational analysis and visualization of bioactive molecules. For this purpose, it became necessary to represent molecules in a syntax that would be readable by computers and understandable by scientists of various fields. A large number of chemical representations have been developed over the years, their numerosity being due to the fast development of computers and the complexity of producing a representation that encompasses all structural and chemical characteristics. We present here some of the most popular electronic molecular and macromolecular representations used in drug discovery, many of which are based on graph representations. Furthermore, we describe applications of these representations in AI-driven drug discovery. Our aim is to provide a brief guide on structural representations that are essential to the practice of AI in drug discovery. This review serves as a guide for researchers who have little experience with the handling of chemical representations and plan to work on applications at the interface of these fields.

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Section: Proteinsmentioning

confidence: 99%

“…At the time, memory efficiency was an important factor impacting the development of chemical notations. Popular representations used nowadays, however, were largely developed in and since the 70′s to represent small molecules [9][10][11], macromolecules [12][13][14][15][16][17] and chemical reactions [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Molecular representations in AI-driven drug discovery: a review and practical guide

et al. 2020

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“…The algorithm works internally with the condensed molfile format. − Molfile formats describe molecular structures in atomic detail with all atoms and bonds. The Proteax software converts between sequence based formats, full atomic description, and a condensed format in between (illustrated in Figure ).…”

Section: Methodsmentioning

confidence: 99%

pICalculax: Improved Prediction of Isoelectric Point for Modified Peptides

Bjerrum¹,

Jensen²,

Tolborg

2017

J. Chem. Inf. Model.

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The isoelectric point of a peptide is a physicochemical property that can be accurately predicted from the sequence of the peptide when the peptide is built from natural amino acids. Peptides can however have chemical modifications, such as phosphorylations, amidations, and unnatural amino acids, which can result in erroneous predictions if not accounted for. Here we report on an open source program, pICalculax, which in an extensible way can handle pI calculations of modified peptides. Tests on a database of modified peptides and experimentally determined pI values show an improvement in pI predictions when taking the modifications into account. The correlation coefficient improves from 0.45 to 0.91, and the root-mean-square deviation likewise improves from 3.3 to 0.9. The program is available at https://github.com/EBjerrum/pICalculax.

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“…Also used are in-house developed cartridges for either search purposes such as the ABCD Chemical Cartridge 530 or for both search and registration as OSIRIS. 513 An important consideration is the wide range of different chemistries that these cartridges must support: small organic molecules, peptides, 531 sugars, polymers, mixtures and formulations (e.g., different ratios of enantiomers), Markush formula, and antibody−drug conjugates. Apart from chemical registration, the use of chemical cartridges for structural searches has the advantage of performing faster than classical cheminformatic tools as they are indexed.…”

Section: Chemical Cartridgesmentioning

confidence: 99%

Information Retrieval and Text Mining Technologies for Chemistry

et al. 2017

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Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.

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Building a BioChemformatics Database

Cited by 8 publications

References 15 publications

Molecular representations in AI-driven drug discovery: a review and practical guide

Molecular representations in AI-driven drug discovery: a review and practical guide

pICalculax: Improved Prediction of Isoelectric Point for Modified Peptides

Information Retrieval and Text Mining Technologies for Chemistry

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