In silico Methods for Identification of Potential Therapeutic Targets

At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow...

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Bibliographic Details
Published in:Interdisciplinary sciences : computational life sciences 2022-06, Vol.14 (2), p.285-310
Main Authors: Zhang, Xuting, Wu, Fengxu, Yang, Nan, Zhan, Xiaohui, Liao, Jianbo, Mai, Shangkang, Huang, Zunnan
Format: Article
Language:English
Subjects:
Applications programs
Big Data
Biological activity
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Appl. in Life Sciences
Computer applications
Cycle time
Drug discovery
Genomics
Health Sciences
Infectious diseases
Life Sciences
Mathematical and Computational Physics
Medicine
Review
Side effects
Statistics for Life Sciences
Theoretical
Theoretical and Computational Chemistry
Therapeutic applications
Therapeutic targets
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Summary:At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery. Graphical abstract
ISSN:1913-2751
1867-1462
DOI:10.1007/s12539-021-00491-y