Literature DB >> 25548930

Impacts of bioinformatics to medicinal chemistry.

Kuo-Chen Chou1.   

Abstract

Facing the explosive growth of biological sequence data, such as those of protein/peptide and DNA/RNA, generated in the post-genomic age, many bioinformatical and mathematical approaches as well as physicochemical concepts have been introduced to timely derive useful informations from these biological sequences, in order to stimulate the development of medical science and drug design. Meanwhile, because of the rapid penetrations from these disciplines, medicinal chemistry is currently undergoing an unprecedented revolution. In this minireview, we are to summarize the progresses by focusing on the following six aspects. (1) Use the pseudo amino acid composition or PseAAC to predict various attributes of protein/peptide sequences that are useful for drug development. (2) Use pseudo oligonucleotide composition or PseKNC to do the same for DNA/RNA sequences. (3) Introduce the multi-label approach to study those systems where the constituent elements bear multiple characters and functions. (4) Utilize the graphical rules and "wenxiang" diagrams to analyze complicated biomedical systems. (5) Recent development in identifying the interactions of drugs with its various types of target proteins in cellular networking. (6) Distorted key theory and its application in developing peptide drugs.

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Year:  2015        PMID: 25548930     DOI: 10.2174/1573406411666141229162834

Source DB:  PubMed          Journal:  Med Chem        ISSN: 1573-4064            Impact factor:   2.745


  105 in total

1.  Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.

Authors:  Guang-Hui Liu; Hong-Bin Shen; Dong-Jun Yu
Journal:  J Membr Biol       Date:  2015-11-12       Impact factor: 1.843

2.  iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.

Authors:  Muhammad Kabir; Maqsood Hayat
Journal:  Mol Genet Genomics       Date:  2015-08-30       Impact factor: 3.291

3.  repRNA: a web server for generating various feature vectors of RNA sequences.

Authors:  Bin Liu; Fule Liu; Longyun Fang; Xiaolong Wang; Kuo-Chen Chou
Journal:  Mol Genet Genomics       Date:  2015-06-18       Impact factor: 3.291

4.  iN6-methylat (5-step): identifying DNA N6-methyladenine sites in rice genome using continuous bag of nucleobases via Chou's 5-step rule.

Authors:  Nguyen Quoc Khanh Le
Journal:  Mol Genet Genomics       Date:  2019-05-04       Impact factor: 3.291

5.  DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information.

Authors:  Farman Ali; Saeed Ahmed; Zar Nawab Khan Swati; Shahid Akbar
Journal:  J Comput Aided Mol Des       Date:  2019-05-23       Impact factor: 3.686

6.  iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC.

Authors:  Yaser Daanial Khan; Nouman Rasool; Waqar Hussain; Sher Afzal Khan; Kuo-Chen Chou
Journal:  Mol Biol Rep       Date:  2018-10-11       Impact factor: 2.316

7.  MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters.

Authors:  Meng Zhang; Fuyi Li; Tatiana T Marquez-Lago; André Leier; Cunshuo Fan; Chee Keong Kwoh; Kuo-Chen Chou; Jiangning Song; Cangzhi Jia
Journal:  Bioinformatics       Date:  2019-09-01       Impact factor: 6.937

8.  Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC.

Authors:  Ahmad Hassan Butt; Nouman Rasool; Yaser Daanial Khan
Journal:  Mol Biol Rep       Date:  2018-09-20       Impact factor: 2.316

9.  In silico prediction of chemical subcellular localization via multi-classification methods.

Authors:  Hongbin Yang; Xiao Li; Yingchun Cai; Qin Wang; Weihua Li; Guixia Liu; Yun Tang
Journal:  Medchemcomm       Date:  2017-03-29       Impact factor: 3.597

10.  Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types.

Authors:  Weizhong Lin; Dong Xu
Journal:  Bioinformatics       Date:  2016-08-26       Impact factor: 6.937

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