Abstract
Retrosynthesis-the process of deconstructing complex molecules into simpler, more accessible precursors-is a cornerstone of drug discovery and material design. While machine learning has improved single-step retrosynthesis prediction, generating complete multi-step retrosynthetic routes remains challenging. In this study, we explore the integration of single-step retrosynthesis models with various planning algorithms to improve multi-step retrosynthetic route generation. We expand the exploration space beyond previously limited settings by incorporating combinations of planning algorithms and single-step retrosynthesis models and diverse datasets, enabling a more comprehensive assessment of retrosynthetic strategies. We evaluated synthetic routes based on both solvability, the ability to generate a complete route, and route feasibility, which reflects their practical executability in the laboratory. Our findings show that the model combination with the highest solvability does not always produce the most feasible routes, underscoring the need for more nuanced evaluation. Through a systematic analysis of combinations of planning algorithms and single-step retrosynthesis models, their performance across different datasets, and various practical metrics, our study provides a more comprehensive evaluation of retrosynthetic planning strategies. These insights contribute to a better understanding of computational retrosynthesis and its alignment with real-world applicability.