Investigating the Reliability and Interpretability of Device Studying Frameworks for Chemical Retrosynthesis

Digital Discovery, 2024, Accepted Manuscript
DOI: 10.1039/D4DD00007B, Paper

Open Access

&nbsp This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Friedrich Hastedt, Rowan Mark Bailey, Klaus Hellgardt, Sophia Yaliraki, Antonio Del Rio Chanona, Dongda Zhang
Machine learning models for chemical retrosynthesis have attracted substantial interest in recent years. Unaddressed challenges, particularly the absence of robust evaluation metrics for performance comparison, and the lack of black-box…
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