Long Short-Term Memory (LSTM) networks are RNNs that can be used to model sequence data such as natural language. Previous studies reported the use of LSTMs to learn directly from SMILES which led the community towards descriptor-free QSAR models. The LSTM networks built in this study were fed with canonical SMILES that are first encoded into onehot vectors and then passed to the computing cell which performs as many computations as the length of the input SMILES in a loop. At each step, one character of SMILES is taken as input and the computed activation value is passed to the next step which takes the next character as input. In this way, the information from previous characters is persisted while the next characters are being processed. Finally, the network produces a prediction probability between 0 and 1. These values can be used to obtain the binary classification labels.