Interrogating Novel Areas of Chemical Space for Drug Discovery using Chemoinformatics

被引:24
|
作者
Medina-Franco, Jose L. [1 ]
机构
[1] Torrey Pines Inst Mol Studies, Port St Lucie, FL 34987 USA
关键词
chemoinformatics; combinatorial libraries; data mining; molecular complexity; natural products; NATURAL-PRODUCTS; COMBINATORIAL CHEMISTRY; VISUAL CHARACTERIZATION; PHYSICOCHEMICAL PROPERTIES; DIVERSITY QUANTIFICATION; MOLECULAR COMPLEXITY; ACTIVITY LANDSCAPES; COMPOUND LIBRARIES; IN-VIVO; VISUALIZATION;
D O I
10.1002/ddr.21034
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
Preclinical Research Chemoinformatic approaches have an essential role in the systematic description and visualization of the chemical space for drug discovery projects. These methods enable the quantitative comparison of general screening collections and the systematic classification of approved drugs and databases annotated with biological activity to define biologically and medicinally relevant chemical spaces. Profiling of chemical diversity, molecular complexity, and physicochemical properties of compound libraries using chemoinformatic approaches provide a solid basis to generate hypothesis of how to interrogate novel areas of chemical space for enhanced drug discovery. This commentary is focused on the application of chemoinformatic approaches to mine, and to navigate the chemical space of compound collections. The discussion is centered on the concept of chemical space, types of compound libraries used in drug discovery programs, applications of chemical space mining and visualization using chemoinformatic methods, and strategies to expand the pharmaceutical relevant chemical space with emphasis on the notion of molecular complexity.
引用
收藏
页码:430 / 438
页数:9
相关论文
共 50 条
  • [31] Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery
    Rodríguez-Pérez, Raquel
    Bajorath, Jürgen
    Journal of Computer-Aided Molecular Design, 2022, 36 (05): : 355 - 362
  • [32] Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery
    Raquel Rodríguez-Pérez
    Jürgen Bajorath
    Journal of Computer-Aided Molecular Design, 2022, 36 : 355 - 362
  • [33] Balancing novelty with confined chemical space in modern drug discovery
    Medina-Franco, Jose L.
    Martinez-Mayorga, Karina
    Meurice, Nathalie
    EXPERT OPINION ON DRUG DISCOVERY, 2014, 9 (02) : 151 - 165
  • [34] Structural pharmacology and drug discovery: Exploring biological and chemical space
    Blundell, Tom L.
    ACTA CRYSTALLOGRAPHICA A-FOUNDATION AND ADVANCES, 2008, 64 : C10 - C10
  • [35] Exploring biological and chemical space: the new dimension of drug discovery
    Blundell, T.
    FEBS JOURNAL, 2008, 275 : 23 - 23
  • [36] The Race for Chemical and Biological Space: Drug Discovery and Innovative Technologies
    Sawyer, Tomi K.
    CHEMICAL BIOLOGY & DRUG DESIGN, 2009, 73 (01) : 1 - 2
  • [37] Mapping biologically active chemical space to accelerate drug discovery
    Sittampalam, G. Sitta
    Rudnicki, Dobrila D.
    Tagle, Danilo A.
    Simeonov, Anton
    Austin, Christopher P.
    NATURE REVIEWS DRUG DISCOVERY, 2019, 18 (02) : 83 - 84
  • [38] OptiMol: Optimization of Binding Affinities in Chemical Space for Drug Discovery
    Boitreaud, Jacques
    Mallet, Vincent
    Oliver, Carlos
    Waldispuhl, Jerome
    JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2020, 60 (12) : 5658 - 5666
  • [39] Enhancing chemoinformatics with pathway analysis tools: An integrated approach to drug discovery
    Khasanova, Tatiana
    Myshkin, Eugene
    O'Charoen, Sirimon
    Nikolsky, Yuri
    Bureeva, Svetlana
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2012, 243
  • [40] Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery
    Rodriguez-Perez, Raquel
    Bajorath, Juergen
    JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, 2022, 36 (05) : 355 - 362