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Generating diversity and securing completeness in algorithmic retrosynthesis
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OA
AI
DOI：10.1186/s13321-025-00981-x.png)
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Abstract
En 中文
Chemical synthesis planning has considerably benefited from advances in the field of machine learning. Neural networks can reliably and accurately predict reactions leading to a given, possibly complex, molecule. In this work we focus on algorithms for assembling such predictions to a full synthesis plan that, starting from simple building blocks, produces a given target molecule, a procedure known as retrosynthesis. Objective functions for this task are hard to define and context-specific. In order to generate a diverse set of synthesis plans for chemists to select from, we capture the concept of diversity in a novel chemical diversity score (CDS). Our experiments show that our algorithm outperforms the algorithm predominantly employed in this domain, Monte-Carlo Tree Search, with respect to diversity in terms of our score as well as time efficiency.
Keywords:
Computer-Assisted Synthesis Planning (CASP)
Retrosynthesis
DFPN
Chemical diversity score
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