Results from the previous sections highlight the most frequently used solvents and the corresponding perovskites. These solvents are used in various stages of perovskite synthesis and can pose occupational, safety and environmental hazards. Hence it is important to identify toxic chemicals with an aim to eliminate or reduce their use and develop safer alternatives. In this work, we have screened the identified solvents for their hazardous nature in terms of endocrine disrupting effects and suggested alternatives for some using a data-driven model. We used a recently developed deep neural network model to assess the toxicity of the prevailing solvents. This model uses a machine-readable notation that uniquely defines the geometric structure of an organic molecule. We obtained the Simplified Molecular-Input Line-Entry System (SMILES) codes of these solvents from a database of chemicals published by PubChem . SMILES notation is a well-known molecular structure descriptor that is used in different data-driven property prediction works. These SMILES codes are the machine-readable notations that are used for representing chemical species in a single line using a set of ASCII characters. The toxicity nature of these solvents is assessed in terms of two categories of endocrine disruption such as 'Agonist' and 'Binding' class. The data-driven model used in this exercise comprises of convolution and long short-term memory layers, that work on the SMILES codes to assess the toxicity of the extracted solvents in these two categories.