Towards a Computational Ecotoxicity Assay

06 March 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Thousands of anthropogenic chemicals are released into the environment each year, posing potential hazards to human and environmental health. Toxic chemicals may cause a variety of adverse health effects, triggering immediate symptoms or delayed effects over longer periods of time. It is thus crucial to develop methods that can rapidly screen and predict the toxicity of chemicals, to limit the potential harmful impacts of chemical pollutants. Computational methods are being increasingly used in toxicity predictions. Here, the method of molecular docking is assessed for screening potential toxicity of a variety of xenobiotic compounds, including pesticides, pharmaceuticals, pollutants and toxins deriving from the chemical industry. The method predicts the binding energy of the pollutants to a set of carefully selected receptors, under the assumption that toxicity in many cases is related to interference with biochemical pathways. The strength of the applied method lies in its rapid generation of interaction maps between potential toxins and the targeted enzymes, which could quickly yield molecularlevel information and insight into potential perturbation pathways, aiding in the prioritisation of chemicals for further tests. Two scoring functions are compared, Autodock Vina and the machine-learning scoring function RF-Score-VS. The results are promising, though hampered by the accuracy of the scoring functions. The strengths and weaknesses of the docking protocol are discussed, as well as future directions for improving the accuracy for the purpose of toxicity predictions.


machine Learning

Supplementary materials


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