The second variant used the adversarial training both during pre-training and fine-tuning. Due to limits on computational resources, the 𝛼 and 𝜖 hyperparameters were set to 0.5 and kept constant for all the training stages. This means that the original and perturbed model inputs had equal contribution to the model updates, and that the values of the original embeddings were modified by 50% in the direction of the gradient to afford the adversarial embeddings. The results showed that the adversarial approaches improved the performance metrics for all but one split for yield prediction on the Buchwald-Hartwig set from Ahneman et al. Similar observation was made for the classification with this reaction set, and the most improvement was seen for the difficult splits number one and five (Figure , Tables ). Improvements were also made for regression splits three, four and five of the Suzuki set from Perera et al., but in this case a small performance decrease was observed for the problematic classification end-task (Figure , Tables ). Interestingly, the performance of the two tested adversarial variants was generally comparable across the different splits, with no clear benefit of the added data augmentation during fine-tuning. Due to the small to moderate impact on the model performance and relatively large variance of the collected metrics, the adversarial training generally had no statistical significance, despite the clear performance trends in the metrics (Figures ). Thus, we conclude that adversarial training can improve the predictive synthesis models through dynamic data augmentation targeting the weak points of the model, with no requirements for auxiliary input or additional data manipulation, tagging or preparation by the practitioner.