New Poster: Chemistry and Machine Learning for Predictive Toxicology

Computational approaches to assist risk assessment are becoming more and more popular as part of alternative approaches to animal experiments. These approaches can quickly assess many chemicals at low cost, and do not raise ethical concerns. Most of these approaches involve collecting large pools of experimental data, modelling an aspect of the chemistry of chemicals suitable for their adverse outcomes and fitting the calculated parameters to the experimental data. This involves a combination of chemistry, statistics, computer science and biology. In this poster, Tim Allen (Unilever sponsored Post Doctoral Researcher at the University of Cambridge) outlines the modelling approaches he is developing to make predictions of molecular toxicity potential.

View the poster here.

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