Use of different types of information to better understand compounds’ Mechanism of Action.

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• Maria-Anna Trapotsi, University of Cambridge
• Wednesday 27 February 2019, 14:15-14:35
• Department of Chemistry, Cambridge, Unilever lecture theatre.

If you have a question about this talk, please contact Lisa Masters.

First Year PhD Report

A principal challenge in the drug discovery process is the development of therapeutic compounds and the understanding of their Mechanism of Action (MoA). The a priori knowledge of a compound’s MoA can facilitate and accelerate drug discovery and inform on potential off-target effects. Hence, it is significant to know the mechanism through which a drug exerts its pharmacological effect as this information can pave the way for the development of a therapeutic drug with well-known on- and off-target effects. The MoA has been extensively investigated on a protein-target level by predicting targets based on bioactivity data with the ultimate goal to elucidate the MoA of drugs and possible off-target effects. The rationale behind these methods relies on the assumption that structurally similar compounds are more likely to exhibit similar properties. However, the premise is not always valid because of the complex biological processes that occur during a biological dysfunction (i.e. disease) and also because compounds exhibit a broad range of activity that could be beyond the bioactivity effect. The dysfunction can occur in different levels of the biological system such as the expression of genes, biological pathways and proteins. Human biology is highly complex and thus it is challenging to develop a safe and efficacious medicine. As such, modern drug discovery has broadened from the classic “single target-based” approach towards the understanding of the compounds’ polypharmacology, gene expression and activity on biological pathways. Bioinformatic approaches can facilitate the process of MoA understanding by using multilevel information from high throughput biological assays with machine learning techniques in order to reveal unexplored connections and interactions of small molecules with the biological system. Therefore, this talk aims to provide an overview of different types of information that can be used to better understand compounds’ MoA.

This talk is part of the Theory - Chemistry Research Interest Group series.

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