Computer‐assisted synthetic planning considering reaction kinetics based on transition state automated generation method

Liu, Qilei; Tang, Kun; Zhang, Lei; Du, Jian; Meng, Qingwei

doi:10.1002/aic.18092

Cited by 6 publications

(2 citation statements)

References 70 publications

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“…85 Here, reactions that are unlikely to occur are filtered out before advancing to the next depth level. Similarly, in the field of biocatalysis, Liu et al 86 employ thermodynamic estimations as a filtering criterion to mitigate combinatorial explosion during breadth-first searches.…”

Section: ■ Multistep Retrosynthesismentioning

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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Section: ■ Multistep Retrosynthesismentioning

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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show abstract

“…Alternatively, classical computational approaches, including quantum chemical calculation (QCC) − and kinetic model-based regression, have been successfully employed for calculating different rate coefficients of polymerization reactions (e.g., propagation rate coefficients ( k p ), activation/deactivation rate coefficients, and RAFT equilibrium constants). Attractively, data-driven computational approaches, , such as quantitative structure–property relationship (QSPR) modeling and machine learning (ML) algorithms are becoming an effective tool to predict molecular properties and kinetic parameters. − Recently, a so-called k p (T, NI)-QSPR model was developed by our group to calculate k p values for a wide range of monomers . Junkers et al developed a predictive machine learning model for calculating the k p values of (meth)acrylates with linear and branched structures, in which both absolute rate coefficients and Arrhenius parameters are predicted with good accuracy .…”

Section: Introductionmentioning

confidence: 99%

In Silico Prediction of the Temperature-Dependent Decomposition Rate Coefficients of Symmetrical Azo Compounds

Shi,

Han,

Yan

et al. 2023

Ind. Eng. Chem. Res.

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The decomposition rate coefficient (k d) is a crucial kinetic parameter for the decomposition of azo compounds. In this work, we develop quantitative structure–property relationship (QSPR) models for the first time to predict the decomposition kinetic parameters at a wide range of temperature for symmetrical azo compounds, including azonitriles, alkyl azo, and aryl azo. The models are developed based on norm index (NI) and quantum chemical (QC) descriptors, so-called ln k d (T, NI, QC) models. Based on the collected data, solvents have an apparent influence on the k d values for aryl azo, and the ln k d (T, NI, QC, sol)aryl azo model with better performance than ln k d (T, NI, QC)aryl azo model is thus developed. Meanwhile, the as-developed models are endowed with excellent accuracy in calculating the k d values of initiators, which can be further applied to new initiators beyond the data set used for modeling. Interestingly, both Arrhenius parameters and their temperatures at which the half-lives (t 1/2) are 0.1, 1, and 10 h of azo initiators can be extrapolated from the established models. These data offer an insightful understanding of decomposition kinetics and facilitate the selection of appropriate initiators in radical polymerization. In the long term, it is expected that QSPR models with higher predictive accuracy based on the data collected from a wider range of experimental conditions can be developed, enabling the design and screening of new azo initiators.

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TSeC: an efficient transition state search tool driven by machine learning potential

Tang,

Zhang,

Meng

et al. 2024

Computer Aided Chemical Engineering

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Computer‐assisted synthetic planning considering reaction kinetics based on transition state automated generation method

Cited by 6 publications

References 70 publications

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

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

In Silico Prediction of the Temperature-Dependent Decomposition Rate Coefficients of Symmetrical Azo Compounds

TSeC: an efficient transition state search tool driven by machine learning potential

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