Title:Bioinformatics Approaches for Anti-cancer Drug Discovery
Volume: 21
Issue: 1
Author(s): Kening Li, Yuxin Du, Lu Li and Dong-Qing Wei*
Affiliation:
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240,China
Keywords:
Drug discovery, bioinformatics, cancer therapy, precision medicine, multi-omic data, biomarkers.
Abstract: Drug discovery is important in cancer therapy and precision medicines. Traditional approaches
of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening,
but these methods are usually expensive and laborious. In the last decade, omics data explosion
provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of
drug discovery. High-throughput transcriptome data were widely used in biomarkers’ identification
and drug prediction by integrating with drug-response data. Moreover, biological network theory and
methodology were also successfully applied to the anti-cancer drug discovery, such as studies based
on protein-protein interaction network, drug-target network and disease-gene network. In this review,
we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug
combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics
and biological network. We believe that the general overview of available databases and current
computational methods will be helpful for the development of novel cancer therapy strategies.
