Chemprop has two builtin forms of regularization, intended to help reduce overfitting in trained models. These two regularization techniques were present in the initial release of The second form of regularization is called dropout. During training, dropout regularization will randomly zero out a fraction of the latent variables for that forward pass. This practice has been shown to reduce overfitting and lead to higher quality latent variables. The level of dropout regularization can be specified using the option --dropout <p> where p is the dropout probability. By default, dropout is inactive. We have observed dropout to be a helpful addition to models in a variety of contexts and recommend that users include it in their choices of hyperparameters.