The use of computational methods that utilizes Artificial Intelligence (AI) and Machine Learning (ML) throughout the different stages of the drug discovery process has become increasingly important during the last decade as greater amount of pharmaceutical data and computational resources become more readily available . By leveraging the vast amounts of data and computational power now available, AI and ML methods have the potential to rapidly process complex biological, chemical, and pharmacological information, enabling automation of various decision-making processes in a fraction of the time. Through innovative approaches like deep learning, reinforcement learning, and generative models, AI-frameworks can be used to explore vast chemical spaces and identify promising compounds with drug-like properties and desired pharmaceutical activity. These innovations have, and will continue to, accelerate the early stages of drug discovery by reducing the time and resources required to identify and validate potential drug candidates. More specifically, molecular de novo design promises to impact the drug discovery process by generating novel pharmaceutically active compounds with desirable properties in a time and cost-efficient manner . Several de novo design methods have recently been developed [4] . Among those, the REINVENT platform uses reinforcement learning to explore the chemical space in an efficient way by using a scoring function to guide the optimization of the biological profile of the generated de novo compound . In a drug discovery program, the question: "Which molecule to design next?" is inevitably followed by "How to make it?". Some components of the chemistry accessibility and feasibility can be partially learned during the set-up of these generative models or can be implemented in their scoring functions. However, a reasonable synthesis route of the suggested de novo compound is typically not provided and needs to be solved with the help of additional Computer-Assisted Synthesis Prediction (CASP) tools, such as AiZynthFinder .