New Poster: Computational approaches for predicting Molecular Initiating Events


Through our collaboration with University of Cambridge, we have used several in silico methodologies to investigate MIEs using chemical information. Structural features, reactivity patterns and artificial intelligence can be used to make predictions and gain greater understanding of why chemicals lead to toxicological effects. Quantum mechanical calculations have been performed on a subset of ß unsaturated carbonyls to identify their transition states and activation energies during reactions with a DNA nucleobase model nucleophile. Artificial intelligence approaches such as machine learning neural networks are being used to make MIE predictions, and similarity calculations are being investigated to identify how they may allow us to better understand these complex and powerful predictive methods for use in chemical risk assessment. this poster outlines some of the approaches developed.

Tim Allen recently received two prizes at Eurotox 2018 for his poster, which can be seen here.

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