Mai 24, 2024 In Unkategorisiert
Investigating the Reliability and Interpretability of System Studying Frameworks for Chemical Retrosynthesis
Digital Discovery, 2024, Accepted Manuscript
DOI: 10.1039/D4DD00007B, Paper
DOI: 10.1039/D4DD00007B, Paper
Open Access
  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…
The content of this RSS Feed (c) The Royal Society of Chemistry
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…
The content of this RSS Feed (c) The Royal Society of Chemistry
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