In an attempt to further assess the model's potential to generate valid, unique and novel SMILES strings, we investigated the effect of the so-called sampling temperature T (Eq. 1). The sampling temperature (T >0) governs the randomness of the chosen token at each step of sequence generation. For T→0, the most likely token according to the estimated probability distribution is selected; with increasing values of T, the chances of selecting the most likely token decrease, so the model generates more diverse sequences (Fig. ). In the extreme case of T→∞, tokens will be selected with equal probabilities. We investigated the influence of four temperatures with respect to the probability distribution learned by the model: two conservative values (T = 0.2 and T = 0.7), one unbiased value (T = 1.0), and one more permissive value (T = 1.2). The highest levels of valid, unique and novel SMILES strings were obtained at a sampling temperature of T = 0.7 (Table ). Combining both data augmentation and temperature sampling led to an optimized chemical language model as indicated by the increased levels of validity, uniqueness and novelty of sampled molecules (Table ). In subsequent experiments, the model trained with 10-fold data augmentation and T = 0.7 was used for generating application-focused libraries.