The right choice of a multi-step route is extremely important in organic synthesis, particularly in its application to medicinal and process chemistry as a part of drug discovery . Both medicinal and process chemists face numerous parameters to optimize in order to solve the route selection task and thereby create economic value. Computer-aided synthesis planning (CASP) tools have been developed to assist in synthesis by generating diverse routes. Despite their success in generation, the evaluation of synthetic routes remains a task for human experts. One reason for this is that computer-aided synthesis planning employs only theoretical data-driven synthesis probabilities, disregarding more specific practical considerations, including diverse experimental conditions optimal in different laboratories, varying budgets of organizations, and personal preferences of chemists. Additionally, despite efforts to improve the quality of single-step retrosynthetic transformations, feasibility prediction of forward synthesis remains challenging, partly due to the lack of high-quality negative reaction data . As a result, generated routes may not be feasible when attempted experimentally, necessitating extensive manual evaluation using the expertise of synthetic chemists or chemical engineers . To assist with the selection, CASP tools attempt to integrate scorers to rank routes were made to meet certain criteria. However, these scores are often simplistic and fail to account for the multi-objective nature of forward synthesis design and the inherently complex nature of chemical reactivity.