Toxicology studies are subject to several concerns, and they raise the importance of an early detection of the potential for toxicity of chemical compounds which is currently evaluated through in vitro assays assessing their bioactivity, or using costly and ethically questionable in vivo tests on animals. Thus we investigate the prediction of the bioactivity of chemical compounds from their physico-chemical structure, and propose that it be automated using machine learning (ML) techniques based on data from in vitro assessment of several hundred chemical compounds. We provide the results of tests with this approach using several ML techniques, using both a restricted dataset and a larger one. Since the available empirical data is unbalanced, we also use data augmentation techniques to improve the classification accuracy, and present the resulting improvements.

G-Networks and their simplified version known as the Random Neural Network have often been used to classify data. In this paper, we present a use of the Random Neural Network to the early detection of potential of toxicity chemical compounds through the prediction of their bioactivity from the compounds’ physico-chemical structure, and propose that it be automated using machine learning (ML) techniques. Specifically the Random Neural Network is shown to be an effective analytical tool to this effect, and the approach is illustrated and compared with several ML techniques.