Bioisosteres -chemical groups that can be interchanged in a molecule while preserving its biological properties -are a key concept in medicinal chemistry. In this work, pre-defined functional groups (same as in atom masking) were replaced with the corresponding bioisosteres (if any), as reported in the SwissBioisosters Database. Functional groups were replaced by choosing randomly among their a subset of top-5 frequently reported bioisosteres (see Materials and Methods). The replacement was controlled by a user-defined probability (p). • Augmentation by self-training (Fig. ). We define self-training as the process of feeding a generative deep learning approach its own generated samples. Here, we created 'synthetic' SMILES strings by sampling from a trained CLM on non-augmented SMILES strings, to be used to augment the training set available (for the follow-up training). This was achieved by temperature sampling of a trained CLM using a low temperature value (T = 0.5, see Materials and Methods, Eq. 1). For each strategy, the augmented SMILES strings were used as input of the CLM for training. For chemical language modelling, we used recurrent neural network with long-short term memory, which have found widespread application in drug design and in combination with SMILES enumeration.