Literature DB >> 19391632

Extraction and analysis of chemical modification patterns in drug development.

Daichi Shigemizu1, Michihiro Araki, Shujiro Okuda, Susumu Goto, Minoru Kanehisa.   

Abstract

Most drugs have been continuously modified from prototypic compounds in the drug development process. Such chemical modifications in the history of drug development are expected to contain a wealth of medicinal chemists' knowledge, and the KEGG DRUG structure maps have been compiled to capture this knowledge. Here we attempted to extract the information on the chemical modification patterns from 3745 approved drugs in the KEGG DRUG database and 255 drug pairs in the KEGG DRUG structure maps. We first identified 236 core structures and 506 peripheral fragments from the KEGG DRUG database using bit-represented fingerprints and hierarchical clustering of similar structures. We then examined position-dependent relationships between core structures and peripheral fragments, which revealed the tendency of specific fragments connected to specific modification sites on the core structures. Next we converted the drug pairs into 204 peripheral fragment changes at the modification sites. Each change was represented by the transformation profile defined as a difference of fingerprint bit patterns, and the hierarchical clustering of similar transformation profiles was performed. We thus identified 125 chemical modification patterns that characterize the KEGG DRUG structure maps. These patterns were further applied to the reconstruction of a new structure map. The approach presented here may be applicable to systematic in silico drug modifications.

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Year:  2009        PMID: 19391632     DOI: 10.1021/ci8003804

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  2 in total

1.  KEGG for representation and analysis of molecular networks involving diseases and drugs.

Authors:  Minoru Kanehisa; Susumu Goto; Miho Furumichi; Mao Tanabe; Mika Hirakawa
Journal:  Nucleic Acids Res       Date:  2009-10-30       Impact factor: 16.971

2.  Identification of chemogenomic features from drug-target interaction networks using interpretable classifiers.

Authors:  Yasuo Tabei; Edouard Pauwels; Véronique Stoven; Kazuhiro Takemoto; Yoshihiro Yamanishi
Journal:  Bioinformatics       Date:  2012-09-15       Impact factor: 6.937

  2 in total

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