Figure shows a multioutput classification model that is used to predict the endocrine disruptive nature of an organic chemical using its SMILES code. As mentioned before, SMILES codes are alphanumeric single-line notations that uniquely define a chemical species. Thus, it needs to be encoded in a form suitable for any statistical model. Figure shows how the SMILES of an organic molecule is converted into a fixed-length real-valued vector that serves as the input to the deep neural network shown in Figure . Each unique alphanumeric character in a SMILES is mapped to an integer in Figure -ii that converts the SMILES of the organic molecule in 9a-i into an integer-valued vector of the same length as the SMILES. This encoded integer-valued vector is converted into a fixed-length vector by adding zeros to the beginning of the vector. The deep learning model architecture is shown in Figure . The model architecture has 10 1-D CNN (convolution neural network) layers followed by 2 LSTM (long short-term memory) layers. The CNN layers extract chemical subgroups within the geometry of the organic molecule while the LSTM layers model the dependencies between the extracted subgroups. The output of the last LSTM layer goes through two fully connected layers before being fed into two separate softmax classifier layers for each category. The model was previously trained on a synthetic dataset of SMILES codes for 3326 chemicals from Tox21 dataset and 4492 chemicals from the literature in an earlier work . In this work, we have used the pretrained model for our prediction. For each chemical, we have binary information of active/inactive in two different categories. The model, once trained, is capable of predicting the toxicity for any chemical that may not be a part of the training data. Due to the applicability of the CNN+LSTM network described above, the SMILES codes of the solvents serve as input and the network classifies each solvent as active or inactive EDC with respect to two subclasses -agonist and binding. These two subclasses are representative of a chemical's response to a receptor molecule . An EDC can interact with an estrogen receptor (ER) and alter its biological response. Such an interaction can be quantified in terms of their agonist, antagonist, and binding activities. Previously, by combining the results of several models, researchers have developed a consensus model to classify the chemicals into active/inactive binders, agonists, and antagonists . Our classification of a solvent into an active/inactive agonist/binding subclass is based on the mathematical framework of this consensus model. A chemical can be an active agonist and an inactive binding or viceversa or can have the same response for both the subclasses. However, being "Active" in one of the subclasses is enough for the chemical to be considered a potential EDC. This classification gives a structured database of solvents associated with perovskite synthesis. Table is a list of solvents and their prediction as active or inactive endocrine disruptors for both the subclasses.