Chemical and Biological Data - from Compound Selection to Mode of Action Analysis (and Back Again)

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• Dr Andreas Bender (Centre for Molecular Informatics, Dept of Chemistry)
• Wednesday 27 February 2019, 14:00-15:00
• MR4, Centre for Mathematical Sciences, Wilberforce Road, Cambridge.

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Abstract: More and more chemical and biological information is becoming available, both in public databases as well as in company repositories. However, how to make use of this information in chemical biology and drug discovery settings is much less clear. In this work, we will discuss how chemical and biological information from different domains – such as compound bioactivity data, pathway annotations from the bioinformatics domain, and gene expression data – can be used for a variety of purposes. Examples related to understanding compound action include the mode-of-action analysis from phenotypic readouts,[1,2] and anticipating compound toxicities in early discovery and during lead optimization based on gene expression data³. Applications of selecting compounds with the desired activities include proteochemometrics modelling against a range of related protein targets such as enzymes in HIV [4] and against serine proteases⁵ , using gene expression data to select compounds which modulate biological pathways used in cellular differentiation to generate cardiomyocytes,[6] and models for differential activity against particular cell lines⁷. More recent research in the groups includes the modelling compound combinations in the antibacterial context⁸ as well as of cancer cell line screens⁹, and learning from data to perform iterative screening¹⁰, such as by utilizing conformal prediction methods.[11]

This presentation will go through some case studies selected from the above areas of our research.

1. Koutsoukas A, et al. J. Proteomics 2011, 74, 2554 – 2574. 2. Drakakis G, et al. ACS Chem. Biol. 2017, 12, 1593 – 1602. 3. Verbist B, et al. Drug Discov. Today 2015, 20, 505 – 513. 4. Van Westen GJP , et al. PLoS Comp. Biol. 2013, 9, e1002899. 5. Subramanian V., et al. MedChemComm 2017, 8, 1037 – 1045. 6. KalantarMotamedi, Y, et al. Cell Death Discovery 2016, 2, 16007. 7. Cortes-Ciriano I, et al. Bioinformatics 2016, 32, 85 – 95. 8. Mason DJ, et al. J. Med. Chem. 2017, 60, 3902 – 3912. 9. under revision 10. Paricharak S., et al. 2016, 11, 1255 – 1264.

This talk is part of the Computational and Systems Biology series.

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