Li Hongxiang, Liu Xuan, Jiang Guangde, Zhao Huimin
NSF Molecule Maker Lab Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.
Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.
J Chem Inf Model. 2024 Dec 23;64(24):9240-9248. doi: 10.1021/acs.jcim.4c01525. Epub 2024 Dec 8.
Thanks to the growing interest in computer-aided synthesis planning (CASP), a wide variety of retrosynthesis and retrobiosynthesis tools have been developed in the past decades. However, synthesis planning tools for multistep chemoenzymatic reactions are still rare despite the widespread use of enzymatic reactions in chemical synthesis. Herein, we report a reaction type score (RTscore)-guided chemoenzymatic synthesis planning (RTS-CESP) strategy. Briefly, the RTscore is trained using a text-based convolutional neural network (TextCNN) to distinguish synthesis reactions from decomposition reactions and evaluate synthesis efficiency. Once multiple chemical synthesis routes are generated by a retrosynthesis tool for a target molecule, RTscore is used to rank them and find the step(s) that can be replaced by enzymatic reactions to improve synthesis efficiency. As proof of concept, RTS-CESP was applied to 10 molecules with known chemoenzymatic synthesis routes in the literature and was able to predict all of them with six being the top-ranked routes. Moreover, RTS-CESP was employed for 1000 molecules in the boutique database and was able to predict the chemoenzymatic synthesis routes for 554 molecules, outperforming ASKCOS, a state-of-the-art chemoenzymatic synthesis planning tool. Finally, RTS-CESP was used to design a new chemoenzymatic synthesis route for the FDA-approved drug Alclofenac, which was shorter than the literature-reported route and has been experimentally validated.
由于对计算机辅助合成规划(CASP)的兴趣日益浓厚,在过去几十年中开发了各种各样的逆合成和逆生物合成工具。然而,尽管酶促反应在化学合成中广泛使用,但用于多步化学酶促反应的合成规划工具仍然很少。在此,我们报告一种反应类型评分(RTscore)引导的化学酶促合成规划(RTS-CESP)策略。简而言之,使用基于文本的卷积神经网络(TextCNN)训练RTscore,以区分合成反应和分解反应并评估合成效率。一旦逆合成工具为目标分子生成多条化学合成路线,RTscore就用于对它们进行排序,并找到可以被酶促反应取代以提高合成效率的步骤。作为概念验证,RTS-CESP应用于文献中具有已知化学酶促合成路线的10个分子,并且能够预测所有这些分子,其中6条是排名靠前的路线。此外,RTS-CESP用于精品数据库中的1000个分子,并且能够预测554个分子的化学酶促合成路线,优于最先进的化学酶促合成规划工具ASKCOS。最后,RTS-CESP用于为FDA批准的药物阿尔氯芬酸设计一条新的化学酶促合成路线,该路线比文献报道的路线短并且已经通过实验验证。