The impact of chemoinformatics on drug discovery in the pharmaceutical industry

被引:63
作者
Martinez-Mayorga, Karina [1 ]
Madariaga-Mazon, Abraham [1 ]
Medina-Franco, Jose L. [2 ]
Maggiora, Gerald [3 ]
机构
[1] Univ Nacl Autonoma Mexico, Inst Quim, Mexico City, DF, Mexico
[2] Univ Nacl Autonoma Mexico, Fac Quim, Mexico City, DF, Mexico
[3] Univ Arizona, BIO5 Inst, Tucson, AZ USA
关键词
Chemoinformatics; polypharmacology; polyspecificity; molecular modeling; artificial intelligence; big data; ACTIVITY CLIFFS; CHEMICAL SPACE; CHEMINFORMATICS; INFORMATION; MOLECULES; DATABASES; LIGAND; OPPORTUNITIES; COMBINATION; DESCRIPTORS;
D O I
10.1080/17460441.2020.1696307
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Introduction: Even though there have been substantial advances in our understanding of biological systems, research in drug discovery is only just now beginning to utilize this type of information. The single-target paradigm, which exemplifies the reductionist approach, remains a mainstay of drug research today. A deeper view of the complexity involved in drug discovery is necessary to advance on this field. Areas covered: This perspective provides a summary of research areas where cheminformatics has played a key role in drug discovery, including of the available resources as well as a personal perspective of the challenges still faced in the field. Expert opinion: Although great strides have been made in the handling and analysis of biological and pharmacological data, more must be done to link the data to biological pathways. This is crucial if one is to understand how drugs modify disease phenotypes, although this will involve a shift from the single drug/single target paradigm that remains a mainstay of drug research. Moreover, such a shift would require an increased awareness of the role of physiology in the mechanism of drug action, which will require the introduction of new mathematical, computer, and biological methods for chemoinformaticians to be trained in.
引用
收藏
页码:293 / 306
页数:14
相关论文
共 109 条
[1]   Polypharmacology: Challenges and Opportunities in Drug Discovery [J].
Anighoro, Andrew ;
Bajorath, Juergen ;
Rastelli, Giulio .
JOURNAL OF MEDICINAL CHEMISTRY, 2014, 57 (19) :7874-7887
[2]  
[Anonymous], INNOV PHARM TECHNOL
[3]   Quantitative estimation of pesticide-likeness for agrochemical discovery [J].
Avram, Sorin ;
Funar-Timofei, Simona ;
Borota, Ana ;
Chennamaneni, Sridhar Rao ;
Manchala, Anil Kumar ;
Muresan, Sorel .
JOURNAL OF CHEMINFORMATICS, 2014, 6
[4]   Integration of virtual and high-throughput screening [J].
Bajorath, F .
NATURE REVIEWS DRUG DISCOVERY, 2002, 1 (11) :882-894
[5]   Understanding chemoinformatics: a unifying approach [J].
Bajorath, J .
DRUG DISCOVERY TODAY, 2004, 9 (01) :13-14
[6]  
Bajorath J., 2013, DRUG DISCOV TODAY, V10, pe419
[7]   Representation and identification of activity cliffs [J].
Bajorath, Juergen .
EXPERT OPINION ON DRUG DISCOVERY, 2017, 12 (09) :879-883
[8]  
Bajorath J, 2017, METHODS MOL BIOL, V1526, P231, DOI 10.1007/978-1-4939-6613-4_13
[9]   Extending accessible chemical space for the identification of novel leads [J].
Bajorath, Juergen .
EXPERT OPINION ON DRUG DISCOVERY, 2016, 11 (09) :825-829
[10]   A review of ligand-based virtual screening web tools and screening algorithms in large molecular databases in the age of big data [J].
Banegas-Luna, Antonio-Jesus ;
Ceron-Carrasco, Jose P. ;
Perez-Sanchez, Horacio .
FUTURE MEDICINAL CHEMISTRY, 2018, 10 (22) :2641-2658