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.