Background reading for Chemogenomics, Systems Chemical Biology and the Semantic Web in [[#|Drug Discovery]]

Assay Guidance Manual
Free book, very useful to get to know the basic of wet lab

Chemogenomics (concerned with compounds/drugs and targets/genes only)

Key academic groups and initiatives: Wild (IU), Hopkins (Dundee), Tropsha, Oprea

Keiser et al. Predicting new molecular targets for known drugs. Nature 2009, 462, 12, p175-182. [[#|Work]] of Soichet lab to develop and validate Structure Ensemble Approach (SEA) for predicting new drug-target associations through ligand similarity. Out of 30 associations experimentally tested, 23 were experimentally validated, with 5 potent inhibitors and 1 [[#|confirmed]] in mouse model.

Vieth et al. Kinomics: characterizing the therapeutically validated Kinase space. [[#|Drug Discovery]] Today, 2005, 10, 12, 839-846. [[#|Work]] done at Eli Lilly in Indianapolis. Suggests new Kinase targets for compounds based on sequence similarity and statistical analysis.

Paolini et. al. Global mapping of pharmacological space. Nature Biotechnology. 24, 7, 2006. [[#|Work]] done at Pfizer UK (Hopkins) to create a pharmacology interaction network mapping compounds to targets.

Metz et al. Navigating the Kinome. Nature Chemical Biology 2011, 7, 200-202. [[#|Work at]] Abbott (Hajduk group) to statistically analyze screening data of >3,800 compounds against 172 Kinases (full data matrix). Built Kinome networks built on both sequence similarity and ligand similarity (Hopkins Pharmacology Interaction Strength - the fraction of ligands with similar affinities between two targets).

Bender et al. Chemogenomic Data Analysis: Prediction of Small-Molecule Targets and the Advent of Biological Fingerprints. Combinatorial Chemistry & High Throughput Screening, 2007, 10, p719-731.

Cases, M. and Mestres, J. A chemogenomic approach to drug discovery: focus on cardiovascular diseases. [[#|Drug Discovery]] Today, 2009, 14, 479-485

Systems Chemical Biology (relating compounds/drugs with entities beyond just targets/genes)

Key academic groups and initiatives: Wild (IU), Bourne (UCSD), Hopkins (Dundee)

Oprea, et al. Systems Chemical Biology. Nature Chemical Biology 2007, 3, 8, p447-450. Makes case for the integration of cheminformatics, bioinformatics and other methods for [[#|drug discovery]], and introduces the term systems chemical biology for this.

Barabasi,A Network medicine: a network-based approach to human disease.
Nature Reviews Genetics 12, 56-68(January 2011)

Paolini,A.G . Global Mapping pharmcological space.
Nature Biotechnology 24 , 805 - 815 (2006)

Jia,J .Mechanisms of Drug combinations .Interaction and Network Perspectives . Nature Reviews Drug Discovery 8 111-128 (feburary 2009)
Hopkins, A. Network pharmacology: the next paradigm in drug discovery Nature Chemical Biology 2008, 4, 11, p682-690

Wang, et al. Finding complex biological relationships in recent PubMed articles using Bio-LDA. PLoS One, 6 (3), e17243 (Wild group)
Zhu, Q et al. WENDI: A tool for finding non-obvious relationships between compounds and biological properties, genes, diseases and scholarly publications. Journal of Cheminformatics, 2010, 2, 6 (Wild group)
Xie et al. Drug Discovery Using Chemical Systems Biology: Identification of the Protein-Ligand Binding Network To Explain the Side Effects of CETP Inhibitors. PLoS Computational Biology, 2009, 5, 5 (Bourne group) .
Kinnings et al. Drug Discovery Using Chemical Systems Biology: Repositioning the Safe Medicine Comtan to Treat Multi-Drug and Extensively Drug Resistant Tuberculosis. PLoS Computational Biology, 2009, 5, 7 (Bourne group) - repurposing of Parkinson's Disease compounds for drug resistant tuberculosis.
Chang et. al. Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model. PLoS Computational Biology, 2010, 6, 9 (Bourne group)

Kinnings et al. The Mycobacterium tuberculosis Drugome and Its Polypharmacological Implications. PLoS Computational Biology, 2010, 6, 11 (Borne Group)

Iskar et al. Drug-Induced Regulation of Target Expression. PLoS Computational Biology, 2010, 6, 9 (Bork group)

Campillos et al. Drug target identification using side-effect similarity. Science, 2008, 321, 263 (Bork group)

Jia et al. Mechanism of drug combinations: interaction and network perspectives. Nature reviews Drug Discovery 8 ,111-128 (feburary 2009)

Audouze et al. Deciphering Diseases and Biological Targets for Environmental Chemicals using Toxicogenomics Networks. PLoS Computational Biology, 2010, 6, 5
Yao et al. Electronic health records: Implications for drug discovery. Drug Discovery Today, 2011, 16, 594-599
Ma'ayan etal.Network Analysis of FDA Approved Drugs and their Targets
Chen B et al. Assessing Drug Target Association using Semantic Linked Data. PLoS Computational Biology, 2012, 8(7), e1002574
Vashist R et al .Crowd Sourcing a New Paradigm for Interactome driven drug target identification in Mycobacterium Tuberculosis.PLoS One 7(7): e39808.

Integrative Infrastructure and Strategy (including Semantic Web)

Key academic groups and initiatives: Wild (IU), OpenPhacts,

Ruttenberg et al. Advancing translational research with the Semantic Web. BMC Bioinformatics, 2007, 8(Suppl 3):S2.

Blomberg et al. Knowledge Driven Drug Discovery goes Semantic. EFMC Yearbook 2011.

Zhu et al. Semantic inference using Chemogenomics Data for Drug Discovery. BMC Bioinformatics, 2011, 12, 256. (Wild Group)
Wild, D.J. Strategies for Using Information Effectively in Early-stage Drug Discovery, in Ekins, S. (ed), Computer Applications in Pharmaceutical Research and Development. Wiley-Interscience, Hoboken, 2006.
Wild, D.J et al .Systems Chemical Biology and the Semantic Web: what they mean for the future of drug discovery research, Drug Discovery Today, 2012, 17, 469-474
Chen, B., Dong. X., Jiao, D., Wang, H., Zhu, Q., Ding, Y., Wild, D.J. Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data. BMC Bioinformatics 2010, 11, 255.
Guha, R., Gilbert, K., Fox, G., Pierce, M., Wild, D.J. and Yuan, H. Advances in Cheminformatics Methodologies and Infrastructure to Support the Data Mining of Large, Heterogeneous Chemical Datasets. Current Computer-Aided Drug Design. 2010; 6(1) pp 50-67.

Slater et al. Beyond Data Integration. Drug Discovery Today, 2008, 13, 584-589

Wild, D.J. Mining large heterogenous datasets in drug discovery. Expert Opinion on Drug Discovery. 2009; 4(10), pp 995-1004

Ekins, S. and Williams, A.J. When pharmaceutical companies publish large datasets: an abundance of riches or fools's gold? Drug Discovery Today. 2010, 15, 812-815

Campbell et al. Visualizing the drug target landscape. Drug Discovery Today. 2010, 15, 3-15