Determining the specific toxicity of a chemical compound is challenging, given the variety of factors involved . Traditional approaches for gathering toxicity information pose ethical, legal and resource constraints . Therefore, it is imperative to perform computational toxicity predictions to minimize the effects of such factors. In this work, we explore the tools of informatics to understand the ED nature of the commonly used solvents in perovskite synthesis. The novelty of this work lies in doing a multi-output binary classification of the solvents (refer Figure ) with the aid of an established neural network model .