Chemistry in a graph: modern insights into commercial organic synthesis planning†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-07-24 DOI:10.1039/D4DD00120F
Claudio Avila, Adam West, Anna C. Vicini, William Waddington, Christopher Brearley, James Clarke and Andrew M. Derrick
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Abstract

Across the chemical sciences, synthesis planning is a key aspect for defining synthesis routes, starting from idea generation, combining literature searches and laboratory experimentation, and including scaling-up considerations for large scale manufacturing. This iterative process, which relies heavily on information sharing, is crucial in pharmaceutical development, where drug candidates are transformed into commercially viable Active Pharmaceutical Ingredients (APIs), impacting the access to medicines for billions of people. In this work, we demonstrate that by capturing chemical pathway ideas digitally, at the point of conception, we can systematically merge these ideas with synthetic knowledge derived from predictive algorithms. This serves as a preliminary step for further route evaluation. To achieve this, we introduce a new method for storing, analysing, and displaying chemical information using graph databases and graph representations, illustrated with the commercial synthesis planning of the GLP-1 inhibitor Lotiglipron. Compared to traditional methods, graph databases naturally fit the substrate-arrow-product model traditionally used by chemists, offering a modern alternative to store and access chemical knowledge. This framework facilitates a universal chemistry approach, allowing to share and combine data from many different sources and organisations, and enabling new ways to optimise the complete route selection process.

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图表中的化学:商业有机合成规划的现代见解
在整个化学科学领域,合成规划是确定合成路线的一个关键环节,它从想法的产生开始,结合文献检索和实验室实验,并包括对大规模生产的放大考虑。这一迭代过程在很大程度上依赖于信息共享,在医药开发中至关重要,候选药物在此过程中被转化为商业上可行的活性药物成分(API),影响着数十亿人的用药。在这项工作中,我们证明了通过在构思时以数字方式捕捉化学途径的想法,我们可以将这些想法与从预测算法中获得的合成知识系统地融合在一起。这是进一步评估途径的第一步。为此,我们介绍了一种使用图形数据库和图形表示法存储、分析和显示化学信息的新方法,并以 GLP-1 抑制剂 Lotiglipron 的商业合成规划为例进行说明。与传统方法相比,图数据库自然地符合化学家传统使用的底物-箭头-产物模型,为存储和访问化学知识提供了一种现代化的选择。这一框架有助于采用通用化学方法,共享和组合来自不同来源和组织的数据,并以新的方式优化整个路线选择过程。
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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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