GC-NORM-based thermodynamic framework for evaluations of organic reactions involving carbon dioxide utilization

Tang, Kun; Zhuang, Yu; Wang, Wenlong; Liu, Qilei; Zhang, Lei; Du, Jian; Meng, Qingwei

doi:10.1016/j.ces.2023.118913

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…Also, to surrogate synthesis accessibility, the number of steps required to produce a compound is estimated in chemistry, in drug-relevant, and in biological applications or has been integrated into a composite score . Moreover, the scores provide estimates for data concerning the compound, including its price or its thermodynamic properties, and the reaction, its yield, or its feasibility. − …”

Section: Scoring Functionmentioning

confidence: 99%

Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review

Gricourt,

Meyer,

Duigou

et al. 2024

ACS Synth. Biol.

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Retrosynthesis aims to efficiently plan the synthesis of desirable chemicals by strategically breaking down molecules into readily available building block compounds. Having a long history in chemistry, retro-biosynthesis has also been used in the fields of biocatalysis and synthetic biology. Artificial intelligence (AI) is driving us toward new frontiers in synthesis planning and the exploration of chemical spaces, arriving at an opportune moment for promoting bioproduction that would better align with green chemistry, enhancing environmental practices. In this review, we summarize the recent advancements in the application of AI methods and models for retrosynthetic and retrobiosynthetic pathway design. These techniques can be based either on reaction templates or generative models and require scoring functions and planning strategies to navigate through the retrosynthetic graph of possibilities. We finally discuss limitations and promising research directions in this field.

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