ASKCOS: Open-Source, Data-Driven Synthesis Planning DOI
Zhengkai Tu,

Sourabh J. Choure,

Mun Hong Fong

и другие.

Accounts of Chemical Research, Год журнала: 2025, Номер unknown

Опубликована: Май 21, 2025

ConspectusThe advancement of machine learning and the availability large-scale reaction datasets have accelerated development data-driven models for computer-aided synthesis planning (CASP) in past decade. In this Account, we describe range methods that been incorporated into newest version ASKCOS, an open-source software suite developing since 2016. This ongoing effort has driven by importance bridging gap between research development, making advances available through a freely practical tool. ASKCOS integrates modules retrosynthetic planning, complementary capabilities condition prediction product prediction, several supplementary utilities with various roles planning. For developed Interactive Path Planner (IPP) user-guided search as well Tree Builder automatic two well-known tree algorithms, Monte Carlo Search (MCTS) Retro*. Four one-step retrosynthesis covering template-based template-free strategies form basis predictions can be used simultaneously to combine their advantages propose diverse suggestions. Strategies assessing feasibility proposed steps evaluating full pathways are built on top pioneering efforts made subtasks recommendation, pathway scoring clustering, outcomes including major product, impurities, site selectivity, regioselectivity. addition, also auxiliary based our work solubility quantum mechanical descriptor which provide more insight suitability solvents or hypothetical selectivity desired transformations. each these capabilities, highlight its relevance context present comprehensive overview how it is not only but other recent advancements field. We detail chemists easily interact via user-friendly interfaces. assisted hundreds medicinal, synthetic, process day-to-day tasks complementing expert decision route ideation. It belief CASP tools important part modern chemistry offer ever-increasing utility accessibility.

Язык: Английский

ASKCOS: Open-Source, Data-Driven Synthesis Planning DOI
Zhengkai Tu,

Sourabh J. Choure,

Mun Hong Fong

и другие.

Accounts of Chemical Research, Год журнала: 2025, Номер unknown

Опубликована: Май 21, 2025

ConspectusThe advancement of machine learning and the availability large-scale reaction datasets have accelerated development data-driven models for computer-aided synthesis planning (CASP) in past decade. In this Account, we describe range methods that been incorporated into newest version ASKCOS, an open-source software suite developing since 2016. This ongoing effort has driven by importance bridging gap between research development, making advances available through a freely practical tool. ASKCOS integrates modules retrosynthetic planning, complementary capabilities condition prediction product prediction, several supplementary utilities with various roles planning. For developed Interactive Path Planner (IPP) user-guided search as well Tree Builder automatic two well-known tree algorithms, Monte Carlo Search (MCTS) Retro*. Four one-step retrosynthesis covering template-based template-free strategies form basis predictions can be used simultaneously to combine their advantages propose diverse suggestions. Strategies assessing feasibility proposed steps evaluating full pathways are built on top pioneering efforts made subtasks recommendation, pathway scoring clustering, outcomes including major product, impurities, site selectivity, regioselectivity. addition, also auxiliary based our work solubility quantum mechanical descriptor which provide more insight suitability solvents or hypothetical selectivity desired transformations. each these capabilities, highlight its relevance context present comprehensive overview how it is not only but other recent advancements field. We detail chemists easily interact via user-friendly interfaces. assisted hundreds medicinal, synthetic, process day-to-day tasks complementing expert decision route ideation. It belief CASP tools important part modern chemistry offer ever-increasing utility accessibility.

Язык: Английский

Процитировано

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