启发式
回顾性分析
集合(抽象数据类型)
资源(消歧)
化学
计算机科学
单调函数
人工智能
数据科学
管理科学
工作(物理)
有机合成
有机分子
数据集
逻辑程序
理论计算机科学
战略规划
专家系统
合成数据
非单调逻辑
生化工程
作者
Maciej Czuba,Konrad Barnowski,Karol Molga,Zofia Pławecka,Agnieszka Wołos,Szymon Stodółkiewicz,Sebastian Baś,Wojciech D. Petrykowski,Wiktor Beker,Evan Saillard,Konrad Łęgowski,Rafał Roszak,Paweł Kowalczyk,Barbara Mikulak-Klucznik,Piotr Janasz,Emilia Mielke,Patrycja Pietruszewska,Maja Łazuchiewicz,Sylwia Wójcik,Jakub Zabielski
摘要
The design of synthetic routes to complex, stereochemically rich targets remains one of the most demanding challenges in organic chemistry. To interrogate the strategic logic underlying such efforts, we curated and digitized over 3,000 classical total syntheses comprising close to 60,000 individual steps, now freely available as the AbSynth collection. Analyses across this data set reveal that synthesis planning cannot be reduced to purely local, one-step-at-a-time decision-making: known complexity metrics and scoring functions fail to vary monotonically along synthetic trajectories, and conventional bond-disconnection heuristics apply only sporadically and lack the specificity required for automation. Instead, our study shows that nearly half of all steps correspond to structurally unproductive - yet strategically essential - plateaus of skeletal complexity. Navigating these plateaus requires retrosynthetic algorithms to embrace multistep reasoning rather than incremental scoring. We identify several such multistep heuristics, including tolerable lengths of nonskeletal disconnections and the "longevity" of functional groups across routes, and anticipate that AbSynth will serve as a resource for uncovering additional trends in synthesis design. By providing a century-spanning, machine-readable data set of complete routes, this work offers a foundation for advancing chemical AI toward the complexity of human expert planning.
科研通智能强力驱动
Strongly Powered by AbleSci AI