title: Providing data science support for systems pharmacology and its implications to drug discovery.
abstract: BACKGROUND
  The conventional one-drug-one-target-one-disease drug discovery process has been less successful in tracking multi-genic, multi-faceted complex diseases. Systems pharmacology has emerged as a new discipline to tackle the current challenges in drug discovery. The goal of systems pharmacology is to transform huge, heterogeneous, and dynamic biological and clinical data into interpretable and actionable mechanistic models for decision making in drug discovery and patient treatment. Thus, big data technology and data science will play an essential role in systems pharmacology.
  
  
  METHODS
  This paper critically reviews the impact of three fundamental concepts of data science on systems pharmacology: similarity inference, overfitting avoidance, and disentangling causality from correlation. The authors then discuss recent advances and future directions in applying the three concepts of data science to drug discovery, with a focus on proteome-wide context-specific quantitative drug target deconvolution and personalized adverse drug reaction prediction.
  
  
  CONCLUSIONS
  Data science will facilitate reducing the complexity of systems pharmacology modeling, detecting hidden correlations between complex data sets, and distinguishing causation from correlation. The power of data science can only be fully realized when integrated with mechanism-based multi-scale modeling that explicitly takes into account the hierarchical organization of biological systems from nucleic acid to proteins, to molecular interaction networks, to cells, to tissues, to patients, and to populations.
authors.affiliation: a The Rockefeller University , New York , NY , USA.|b Department of Biological Sciences, Hunter College , The City University of New York , New York , NY , USA.
authors.forename: Thomas
authors.initials: T
authors.lastname: Hart
authors.affiliation: c Department of Computer Science, Hunter College , The City University of New York , New York , NY , USA.|d The Graduate Center , The City University of New York , New York , NY , USA.
authors.forename: Lei
authors.initials: L
authors.lastname: Xie
mesh_terms: D000818:Animals; D015195:Drug Design; D055808:Drug Discovery / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000517:prevention & control; D006801:Humans; D008954:Models, Biological; D058990:Molecular Targeted Therapy; D010600:Pharmacology; D049490:Systems Biology
keywords: Data Science; drug discovery; machine learning; multi-scale modeling; systems pharmacology