In fields like energy storage materials or medicinal chemistry, substances are key to technological advancement and progress: the success of these applications hinges on the specific properties of the materials. However, the processes of discovery and development of new materials often face practical and/or principal obstacles, such as unavailability of compounds or precursors, high production costs, and the need for extensive trials on the practical side, or limited data and/or experience, as well as biased expectations of designers and developers on the other hand. Generative models, a powerful category in machine learning, have the potential to address both of these issues simultaneously, as they can help focus our efforts a priori only on the most likely candidates. Many architectures related to creation of novel data points were developed in recent years, most notably Recurrent Neural Networks (RNN) [1], Generative Adversarial Networks (GAN) [2], Variational Autoencoders (VAE) [3] and Transformers [4]. The transformer architecture, especially, has revolutionized the fields of Natural Language Processing (NLP) [5] and other domains like computer vision [6]. The introduction of the General Pretrained Transformer (GPT) architecture led to significant advancements in generative natural language applications. Generative models have also been applied in the fields of medicine and material science to create new molecules with predefined features, a process known as conditional generation [7, 8]. This application can significantly accelerate the discovery of new candidate molecules. Although current generative models may not provide the optimal solution, they can greatly reduce the size of the chemical space that needs to be evaluated.