URSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment
Synthesis planning aiming to find pathways of reactions for a target molecule is one of the most important and challenging tasks in drug discovery. Recent progress has produced both specialized deep-learning retrosynthesis systems and general-purpose large language models, but objective comparison remains difficult due to the lack of flexible, chemically interpretable benchmarking protocols. In the current study, we are introducing the URSA (Utilitarian RetroSynthesis Assessment) evaluation framework that provides the opportunity to benchmark the synthetic routes not only from a formal perspec
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- LinkedLinked via arxiv author · 85%Bogdan Zagribelnyy →
“URSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment”
- LinkedLinked via arxiv author · 85%Ivan Ilin →
“URSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment”
- LinkedLinked via arxiv author · 85%Nikita Bondarev →
“URSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment”