High-throughput electronic biology: mining information for drug discovery

KEY POINTS * In the past 10 years, the life sciences have seen a proliferation of electronic data, emerging from systems such as databases, text-mining technologies, high-throughput

techniques and 'omics' platforms (for example, DNA microarray). * In this article, we review some of the recent developments in the field of electronic biology (eBiology), which

uses these resources as a substrate for new drug discovery. As extensive reviews on data sets and tools already exist, we highlight how these resources can be applied directly to solve

bottlenecks in the industry. * A number of approaches to the application of eBiology in drug discovery are identified, ranging from deep 'systems biology' to

'project-specific' analyses and focus on high-throughput techniques. * A set of examples are given, which look at the power of applying multiple resources simultaneously to build

layers of evidence and end with the identification of novel drug targets. In these scenarios, the expert in that disease area is a key partner, without which the exercise is unlikely to

succeed. * Although there are an increasing number of examples of target mining in the literature, there is also a need to consider how one then translates these biological hypotheses into

drug discovery programmes. To this end, we consider the data sources and techniques that can be used to apply a business focus to the results of this mining. * As hypotheses turn into real

programmes, we consider further workflows to support these later stages of discovery, looking at issues such as druggability, selectivity and understanding the action of a compound both _in

vitro_ and _in vivo_. Although these areas are traditionally addressed by computational chemists, there is much to be gained by the use of techniques and resources familiar to eBiologists. *

Last, we discuss the future needs in eBiology and how the area must be primarily led through scientific creativity, rather than technical considerations. ABSTRACT The vast range of _in

silico_ resources that are available in life sciences research hold much promise towards aiding the drug discovery process. To fully realize this opportunity, computational scientists must

consider the practical issues of data integration and identify how best to apply these resources scientifically. In this article we describe _in silico_ approaches that are driven towards

the identification of testable laboratory hypotheses; we also address common challenges in the field. We focus on flexible, high-throughput techniques, which may be initiated independently

of 'wet-lab' experimentation, and which may be applied to multiple disease areas. The utility of these approaches in drug discovery highlights the contribution that _in silico_

techniques can make and emphasizes the need for collaboration between the areas of disease research and computational science. Access through your institution Buy or subscribe This is a

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CRISPR SCREENING Article 10 February 2022 MOVING TARGETS IN DRUG DISCOVERY Article Open access 19 November 2020 DECODING DISEASE: FROM GENOMES TO NETWORKS TO PHENOTYPES Article 02 August

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thank T. Turi, J. Lanfear, S. Campbell, I. Harrow, J. Keeling and the Pfizer PharmaMatrix team for their valuable insight and support while preparing this article. We also wish to gratefully

acknowledge the input and suggestions from the reviewers. Finally, we wish to acknowledge the contribution of those who develop and maintain a vast landscape of _in silico_ resources and

apologize to those who we have been unable to cite in this article. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Computational Biology Group, Pfizer, Groton, Connecticut, USA William Loging

* Computational Biology Group, Pfizer, Sandwich, Kent, UK Lee Harland * eBiology Group, Pfizer, Sandwich, Kent, UK Bryn Williams-Jones Authors * William Loging View author publications You

can also search for this author inPubMed Google Scholar * Lee Harland View author publications You can also search for this author inPubMed Google Scholar * Bryn Williams-Jones View author

publications You can also search for this author inPubMed Google Scholar CORRESPONDING AUTHOR Correspondence to William Loging. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no

competing financial interests. RELATED LINKS RELATED LINKS DATABASES OMIM Alzheimer disease Crohn disease Marfan syndrome Parkinson disease type 2 diabetes FURTHER INFORMATION BioCarta

Biomarkers.org Ensembl Gene Expression Omnibus Gene Ontology Mouse Genome Informatics database _Nature Biotechnology_ Community Consultation Online Mendelian Inheritance in Man Protein Data

Bank Simplified Molecular Input Line Entry Specification GLOSSARY * _In silico_ A term used to describe experiments or experimental results that are held electronically. * Unified medical

language system Controlled compendium of vocabularies across the spectrum of life sciences. * Medical subject heading The US National Library of Medicine's controlled vocabulary used

for indexing articles for Medline. * Single nucleotide polymorphism A specific location in a DNA sequence at which different people can have a different DNA base. Differences in a single

base could change the protein sequence, leading to disease (for example, sickle-cell disease), or have no known consequences. * Translational medicine The testing of novel therapeutic

strategies (in humans) that were developed through basic laboratory experimentation. Observations taken 'from the bedside to the bench' also constitute translational medicine. *

RSS RSS is a family of data formats used to publish frequently updated digital content, such as news feeds and alerts from many web sites on the internet. * Quantitative structure–activity

relationships (QSAR). Mathematical relationships linking chemical structure and pharmacological activity in a quantitative manner for a series of compounds. Methods that can be used in QSAR

include various regression and pattern-recognition techniques. * Ames positivity A biological assay that assesses DNA damage caused by small molecule drugs in bacterial cells. RIGHTS AND

PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Loging, W., Harland, L. & Williams-Jones, B. High-throughput electronic biology: mining information for drug

discovery. _Nat Rev Drug Discov_ 6, 220–230 (2007). https://doi.org/10.1038/nrd2265 Download citation * Issue Date: March 2007 * DOI: https://doi.org/10.1038/nrd2265 SHARE THIS ARTICLE

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