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Prediction of Mitochondrial Toxicity Indexes for Pharmacological Compounds

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Abstract

The pharmaceutical industry is facing new challenges. The development of a new drug takes around 12 years to reach the market, with a cost of around £1.5bn per drug and only around 1 in each 5.000 drugs manages to reach it. The toxicity of the drugs on the biological tissue is one of the critical points in the drug development process, as it may cause the termination or recall of a drug. To measure such toxicity, the assessment of the perturbation of the mitochondria is one of the techniques that can be used. This dissertation aims to create a Machine Learning (ML) model that early predicts the toxicity levels of pharmacological compounds. Such a model could then be used to identify and prevent the development of new drugs with a toxic composition. This work is done in cooperation with the MitoXT group, based at the UC-BIOTECH, Center for Neuroscience and Cell Biology, that will provide a dataset containing the information regarding the toxic effects of pharmacological compounds on the mitochondria.

Keywords

Machine Learning, Classification, Clustering, Mitochondria, Drug Toxicity

MSc Thesis

Prediction of Mitochondrial Toxicity Indexes for Pharmacological Compounds 2017

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