The recent advancement of machine learning (ML) technologies has shown great potential in expediting various subtasks during the synthesis design . Notable examples include deep learning based quantitative structure-activity relationship (QSAR) model facilitating drug molecule design and catalyst design , rapid identification of promising synthetic routes using machine-learning-aided synthesis planning , guiding automated high-throughput experimental platforms to search for optimal reaction conditions , and direct translation of multistep synthesis procedures from literature to experimental execution via natural language processing (NLP) models . Despite this rapid involvement of machine learning methods in synthesis related tasks, the monolithic input-to-output nature of existing machine learning methods makes them to only function as powerful single-purpose tools for experts, while the goal of fully autonomous end-to-end synthesis reaction design and development still remains to be realized.