Retrosynthetic analysis is a crucial tool in organic chemistry, enabling the design of synthetic routes for target molecules. However, enhancing the quality and relevance of these pathways within specific applicability domains requires expert insight. This project hypothesizes that expert feedback can improve retrosynthetic paths. To test this hypothesis, I propose developing a user-friendly web application that displays retrosynthetic pathways generated by AiZynthfinder, an AI-based retrosynthesis tool. Chemists will use the application to evaluate these pathways, assigning quality grades based on their feasibility, practicality, and alignment with chemical intuition. These grades will then be integrated as a reinforcement component within AiZynthfinder's learning algorithm, refining its ability to generate expert-approved pathways. The project will proceed in three phases: Development of the Web Application: This application will provide an intuitive interface for chemists to review, analyze, and grade retrosynthetic pathways. It will include functionalities such as visualization of reaction schemes, pathway comparison, and a streamlined grading system. Integration of Expert Feedback: Using reinforcement learning techniques, the expert grades will be incorporated into AiZynthfinder to optimize its decision-making process. The model will iteratively improve its pathway generation based on the feedback loop. Evaluation of the Reinforced Model: The enhanced AiZynthfinder model will be assessed on a defined set of test molecules to determine if the generated pathways consistently receive higher grades compared to the original model. If the reinforced model demonstrates improved performance, this will validate the hypothesis that expert feedback is a valuable component in refining retrosynthetic tools. Such advancements could pave the way for further development, including broader expert participation and refinement of the feedback mechanisms. This project has the potential to bridge the gap between computational models and chemical expertise, contributing to more reliable and applicable retrosynthetic tools for real-world applications.