In this work, we developed a pipeline for the expert-augmented deep learning system, as shown in Figure , that combines machine learning capabilities with domain expertise to generate comprehensive route assessments. Initially, we used historical route data to train a deep-learning model that predicts route-specific distance metrics. A data-driven scoring model was trained to provide target-specific and generalizable scores across the chemical space for synthetic routes. By learning to predict the distance between reference synthetic routes published in medicinal organic chemistry journals and artificial pathways generated by an open-source CASP program AiZynthFinder , the model encapsulates a comprehensive implicit evaluation of synthesis routes. We constructed the evaluation model using the DeepSets neural network architecture , incorporating an embedding of both the route and the route property values. Moreover, the model was set to learn route scores specific to the target molecule, incorporating the molecular fingerprints of specific reactions as a part of the input. We performed experiments using three types of molecular fingerprint embeddings eventually choosing the one that resembles general chemistry knowledge the best. Crucially, we leveraged expert chemical knowledge to carefully select the scoring components and reaction embeddings, as well as to define and validate the route quality