Toxin formation predetection using deep neural network

Abstract

Chemical reaction product prediction is a fundamental exercise for chemists. Various product prediction models have been developed, but because most models are focused on main product prediction, the prediction of toxic byproducts was difficult. Therefore, we developed a binary classification model for toxic byproducts. The model is trained for 17 toxins and in every case, binary classification shows better performance than the previous state-of-art model. Our binary classification model cannot completely replace past models. However, by using both models together, we can predict reaction products more accurately and reliably.