Machine learning-driven generation and screening of potential ionic liquids for cellulose dissolution

IF 5.7 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Mengyang Qu, Gyanendra Sharma, Naoki Wada, Hisaki Ikebata, Shigeyuki Matsunami, Kenji Takahashi
{"title":"Machine learning-driven generation and screening of potential ionic liquids for cellulose dissolution","authors":"Mengyang Qu,&nbsp;Gyanendra Sharma,&nbsp;Naoki Wada,&nbsp;Hisaki Ikebata,&nbsp;Shigeyuki Matsunami,&nbsp;Kenji Takahashi","doi":"10.1186/s13321-025-01018-z","DOIUrl":null,"url":null,"abstract":"<div><p>Cellulose, a highly versatile material, faces challenges in processing due to its limited solubility in common solvents. Ionic liquids have been found to possess high solvating capacities for cellulose. However, the experimental development of ionic liquids with optimal cellulose solubilities remains a time-consuming trial-and-error process. In this work, a virtual molecular library containing billions of potentially de novo ionic liquid candidates has been generated utilizing Monte Carlo tree search and recurrent neural network techniques. The library is subsequently screened through two predictive machine learning models, which have been pre-trained for predicting cellulose solubility and melting point of ionic liquids. The promising candidates were further validated and screened using the Conductor-like Screening Model for Real Solvents (COSMO-RS) model. Our work offers an efficient workflow and virtual molecular library, which should facilitate theoretical and experimental development of novel ionic liquids.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":5.7000,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-01018-z","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cheminformatics","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1186/s13321-025-01018-z","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Cellulose, a highly versatile material, faces challenges in processing due to its limited solubility in common solvents. Ionic liquids have been found to possess high solvating capacities for cellulose. However, the experimental development of ionic liquids with optimal cellulose solubilities remains a time-consuming trial-and-error process. In this work, a virtual molecular library containing billions of potentially de novo ionic liquid candidates has been generated utilizing Monte Carlo tree search and recurrent neural network techniques. The library is subsequently screened through two predictive machine learning models, which have been pre-trained for predicting cellulose solubility and melting point of ionic liquids. The promising candidates were further validated and screened using the Conductor-like Screening Model for Real Solvents (COSMO-RS) model. Our work offers an efficient workflow and virtual molecular library, which should facilitate theoretical and experimental development of novel ionic liquids.

机器学习驱动的纤维素溶解潜在离子液体的生成和筛选
纤维素是一种用途广泛的材料,由于其在普通溶剂中的溶解度有限,在加工过程中面临着挑战。离子液体对纤维素具有很高的溶剂化能力。然而,具有最佳纤维素溶解度的离子液体的实验开发仍然是一个耗时的试错过程。在这项工作中,利用蒙特卡罗树搜索和递归神经网络技术生成了一个包含数十亿个潜在的新生离子液体候选物的虚拟分子库。该文库随后通过两个预测机器学习模型进行筛选,这些模型已经过预训练,用于预测离子液体的纤维素溶解度和熔点。使用真实溶剂类导体筛选模型(cosmos - rs)模型进一步验证和筛选有希望的候选物。我们的工作提供了一个高效的工作流程和虚拟分子库,这将促进新型离子液体的理论和实验发展。
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 求助全文
来源期刊
Journal of Cheminformatics
Journal of Cheminformatics CHEMISTRY, MULTIDISCIPLINARY-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
14.10
自引率
7.00%
发文量
82
审稿时长
3 months
期刊介绍: Journal of Cheminformatics is an open access journal publishing original peer-reviewed research in all aspects of cheminformatics and molecular modelling. Coverage includes, but is not limited to: chemical information systems, software and databases, and molecular modelling, chemical structure representations and their use in structure, substructure, and similarity searching of chemical substance and chemical reaction databases, computer and molecular graphics, computer-aided molecular design, expert systems, QSAR, and data mining techniques.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信