SynPlanner: An End-to-End Tool for Synthesis Planning.

IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Journal of Chemical Information and Modeling Pub Date : 2025-01-13 Epub Date: 2024-12-31 DOI:10.1021/acs.jcim.4c02004
Tagir Akhmetshin, Dmitry Zankov, Philippe Gantzer, Dmitry Babadeev, Anna Pinigina, Timur Madzhidov, Alexandre Varnek
{"title":"SynPlanner: An End-to-End Tool for Synthesis Planning.","authors":"Tagir Akhmetshin, Dmitry Zankov, Philippe Gantzer, Dmitry Babadeev, Anna Pinigina, Timur Madzhidov, Alexandre Varnek","doi":"10.1021/acs.jcim.4c02004","DOIUrl":null,"url":null,"abstract":"<p><p>SynPlanner is an end-to-end tool for designing customized retrosynthetic planners from reaction data. It includes a reaction data curation pipeline (reaction atom-to-atom mapping, reaction standardization, and filtration), reaction rule extraction, retrosynthetic model training, and retrosynthetic planning. The tool is designed to be as flexible as possible, supporting the customization of each step of the pipeline to address different needs in the development of customized retrosynthetic planning solutions. The retrosynthetic planning in SynPlanner is performed by Monte Carlo Tree Search (MCTS) guided by graph neural networks for node expansion (retrosynthetic rule predictions) and evaluation (precursor synthesizability prediction). The solution can be accessed by a simple graphical user interface and a command line interface and is accompanied by a collection of tutorials. SynPlanner is available on GitHub at https://github.com/Laboratoire-de-Chemoinformatique/SynPlanner.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"15-21"},"PeriodicalIF":5.6000,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Information and Modeling ","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acs.jcim.4c02004","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/31 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
引用次数: 0

Abstract

SynPlanner is an end-to-end tool for designing customized retrosynthetic planners from reaction data. It includes a reaction data curation pipeline (reaction atom-to-atom mapping, reaction standardization, and filtration), reaction rule extraction, retrosynthetic model training, and retrosynthetic planning. The tool is designed to be as flexible as possible, supporting the customization of each step of the pipeline to address different needs in the development of customized retrosynthetic planning solutions. The retrosynthetic planning in SynPlanner is performed by Monte Carlo Tree Search (MCTS) guided by graph neural networks for node expansion (retrosynthetic rule predictions) and evaluation (precursor synthesizability prediction). The solution can be accessed by a simple graphical user interface and a command line interface and is accompanied by a collection of tutorials. SynPlanner is available on GitHub at https://github.com/Laboratoire-de-Chemoinformatique/SynPlanner.

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
SynPlanner:一个端到端的综合规划工具。
SynPlanner是一个端到端工具,用于根据反应数据设计定制的逆合成规划器。它包括一个反应数据管理管道(反应原子到原子映射、反应标准化和过滤)、反应规则提取、反合成模型训练和反合成计划。该工具被设计得尽可能灵活,支持管道的每个步骤的定制,以满足定制的反合成规划解决方案开发中的不同需求。SynPlanner中的反合成规划采用蒙特卡罗树搜索(MCTS)进行,通过图神经网络进行节点展开(反合成规则预测)和评估(前体可合成性预测)。该解决方案可以通过简单的图形用户界面和命令行界面访问,并附带一系列教程。SynPlanner可在GitHub上获得https://github.com/Laboratoire-de-Chemoinformatique/SynPlanner。
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
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
期刊最新文献
Employing Automated Machine Learning (AutoML) Methods to Facilitate the In Silico ADMET Properties Prediction. Multitarget Natural Compounds for Ischemic Stroke Treatment: Integration of Deep Learning Prediction and Experimental Validation. FDPSM: Feature-Driven Prediction Modeling of Pathogenic Synonymous Mutations. Join Persistent Homology (JPH)-Based Machine Learning for Metalloprotein-Ligand Binding Affinity Prediction. Fluor-Predictor: An Interpretable Tool for Multiproperty Prediction and Retrieval of Fluorescent Dyes.
×
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