Predicting Physiological Effects of Chemical Substances Using Natural Language Processing

In this paper, we apply natural language processing methods to develop models for predicting physiological effects of chemical substances based on their molecular structures. Using string representations of structure as a starting point, we vectorize molecules using two different approaches resulting in sparse and dense vector representations, respectively. We use these representations to train predictive models for a variety of physiological effects such as toxicity, cell cycle arrest and proliferation. Using standard chemical datasets, we empirically demonstrate that such models can achieve high predictive accu-racy.