Abstract
Ligand- and structure-based drug design approaches complement phenotypic and target screens, respectively, and are the two major frameworks for guiding early-stage drug discovery efforts. Since the beginning of this century, the advent of the genomic era has presented researchers with a myriad of high throughput biological data (parts lists and their interaction networks) to address efficacy and toxicity, augmenting the traditional ligand- and structure-based approaches. This data rich era has also presented us with challenges related to integrating and analyzing these multi-platform and multi-dimensional datasets and translating them into viable hypotheses. Hence in the present paper, we review these existing approaches to drug discovery research and argue the case for a new systems biology based approach. We present the basic principles and the foundational arguments/underlying assumptions of the systems biology based approaches to drug design. Also discussed are systems biology data types (key entities, their attributes and their relationships with each other, and data models/representations), software and tools used for both retrospective and prospective analysis, and the hypotheses that can be inferred. In addition, we summarize some of the existing resources for a systems biology based drug discovery paradigm (open TG-GATEs, DrugMatrix, CMap and LINCs) in terms of their strengths and limitations.
Keywords: Systems biology, Drug discovery, Chemoinformatics, Chemoproteomics, Chemogenomics, Genomics, Phenomics.
Current Topics in Medicinal Chemistry
Title:Systems Biology Approaches to a Rational Drug Discovery Paradigm
Volume: 16 Issue: 9
Author(s): Philip Prathipati and Kenji Mizuguchi
Affiliation:
Keywords: Systems biology, Drug discovery, Chemoinformatics, Chemoproteomics, Chemogenomics, Genomics, Phenomics.
Abstract: Ligand- and structure-based drug design approaches complement phenotypic and target screens, respectively, and are the two major frameworks for guiding early-stage drug discovery efforts. Since the beginning of this century, the advent of the genomic era has presented researchers with a myriad of high throughput biological data (parts lists and their interaction networks) to address efficacy and toxicity, augmenting the traditional ligand- and structure-based approaches. This data rich era has also presented us with challenges related to integrating and analyzing these multi-platform and multi-dimensional datasets and translating them into viable hypotheses. Hence in the present paper, we review these existing approaches to drug discovery research and argue the case for a new systems biology based approach. We present the basic principles and the foundational arguments/underlying assumptions of the systems biology based approaches to drug design. Also discussed are systems biology data types (key entities, their attributes and their relationships with each other, and data models/representations), software and tools used for both retrospective and prospective analysis, and the hypotheses that can be inferred. In addition, we summarize some of the existing resources for a systems biology based drug discovery paradigm (open TG-GATEs, DrugMatrix, CMap and LINCs) in terms of their strengths and limitations.
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Cite this article as:
Prathipati Philip and Mizuguchi Kenji, Systems Biology Approaches to a Rational Drug Discovery Paradigm, Current Topics in Medicinal Chemistry 2016; 16 (9) . https://dx.doi.org/10.2174/1568026615666150826114524
DOI https://dx.doi.org/10.2174/1568026615666150826114524 |
Print ISSN 1568-0266 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-4294 |
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