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
{"title":"Chemistry in a graph: modern insights into commercial organic synthesis planning†","authors":"Claudio Avila, Adam West, Anna C. Vicini, William Waddington, Christopher Brearley, James Clarke and Andrew M. Derrick","doi":"10.1039/D4DD00120F","DOIUrl":null,"url":null,"abstract":"<p >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.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d4dd00120f?page=search","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital discovery","FirstCategoryId":"1085","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2024/dd/d4dd00120f","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

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.

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
2.80
自引率
0.00%
发文量
0
期刊最新文献
Back cover 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 Artificial intelligence-enabled optimization of battery-grade lithium carbonate production†
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1