通过应用人工液态的机器学习模型作为固态的代理来预测水的绝对溶解度。

IF 3 3区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
Sadra Kashef Ol Gheta, Anne Bonin, Thomas Gerlach, Andreas H. Göller
{"title":"通过应用人工液态的机器学习模型作为固态的代理来预测水的绝对溶解度。","authors":"Sadra Kashef Ol Gheta,&nbsp;Anne Bonin,&nbsp;Thomas Gerlach,&nbsp;Andreas H. Göller","doi":"10.1007/s10822-023-00538-w","DOIUrl":null,"url":null,"abstract":"<div><p>In this study, we use machine learning algorithms with QM-derived COSMO-RS descriptors, along with Morgan fingerprints, to predict the absolute solubility of drug-like compounds. The QM-derived descriptors account for the molecular properties of the solute, i.e., the solute–solute interactions in an artificial-liquid-state (super-cooled liquid), and the solute–solvent interactions in solution. We employ two main approaches to predict solubility: (i) a hypothetical pathway that involves melting the solute at room temperature T = T¯ (<span>\\({\\Delta }_{fus}{G}_{A}^{\\ominus }\\)</span>) and mixing the artificially liquid solute into the solvent (<span>\\({\\Delta }_{m}{G}_{\\left(A:B\\right)}^{\\ominus }\\)</span>). In this approach <span>\\({\\Delta }_{fus}{G}_{A}^{\\ominus }\\)</span> is predicted using machine learning models, and the <span>\\({\\Delta }_{m}{G}_{\\left(A:B\\right)}^{\\ominus }\\)</span> is obtained from COSMO-RS calculations; (ii) direct solubility prediction using machine learning algorithms. The models were trained on a large number of Bayer in-house compounds for which water solubility data is available at physiological pH of 6.5 and ambient temperature. We also evaluated our models using external datasets from a solubility challenge. Our models present great improvements compared to the absolute solubility prediction with the QSAR model for the artificial liquid state as implemented in the COSMO<i>therm</i> software, for both in-house and external datasets. We are furthermore able to demonstrate the superiority of QM-derived descriptors compared to cheminformatics descriptors. We finally present low-cost alternative models using fragment-based COSMO<i>quick</i> calculations with only marginal reduction in the quality of predicted solubility.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 12","pages":"765 - 789"},"PeriodicalIF":3.0000,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state\",\"authors\":\"Sadra Kashef Ol Gheta,&nbsp;Anne Bonin,&nbsp;Thomas Gerlach,&nbsp;Andreas H. Göller\",\"doi\":\"10.1007/s10822-023-00538-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this study, we use machine learning algorithms with QM-derived COSMO-RS descriptors, along with Morgan fingerprints, to predict the absolute solubility of drug-like compounds. The QM-derived descriptors account for the molecular properties of the solute, i.e., the solute–solute interactions in an artificial-liquid-state (super-cooled liquid), and the solute–solvent interactions in solution. We employ two main approaches to predict solubility: (i) a hypothetical pathway that involves melting the solute at room temperature T = T¯ (<span>\\\\({\\\\Delta }_{fus}{G}_{A}^{\\\\ominus }\\\\)</span>) and mixing the artificially liquid solute into the solvent (<span>\\\\({\\\\Delta }_{m}{G}_{\\\\left(A:B\\\\right)}^{\\\\ominus }\\\\)</span>). In this approach <span>\\\\({\\\\Delta }_{fus}{G}_{A}^{\\\\ominus }\\\\)</span> is predicted using machine learning models, and the <span>\\\\({\\\\Delta }_{m}{G}_{\\\\left(A:B\\\\right)}^{\\\\ominus }\\\\)</span> is obtained from COSMO-RS calculations; (ii) direct solubility prediction using machine learning algorithms. The models were trained on a large number of Bayer in-house compounds for which water solubility data is available at physiological pH of 6.5 and ambient temperature. We also evaluated our models using external datasets from a solubility challenge. Our models present great improvements compared to the absolute solubility prediction with the QSAR model for the artificial liquid state as implemented in the COSMO<i>therm</i> software, for both in-house and external datasets. We are furthermore able to demonstrate the superiority of QM-derived descriptors compared to cheminformatics descriptors. We finally present low-cost alternative models using fragment-based COSMO<i>quick</i> calculations with only marginal reduction in the quality of predicted solubility.</p></div>\",\"PeriodicalId\":621,\"journal\":{\"name\":\"Journal of Computer-Aided Molecular Design\",\"volume\":\"37 12\",\"pages\":\"765 - 789\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2023-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computer-Aided Molecular Design\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10822-023-00538-w\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer-Aided Molecular Design","FirstCategoryId":"99","ListUrlMain":"https://link.springer.com/article/10.1007/s10822-023-00538-w","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

在这项研究中,我们使用机器学习算法和QM衍生的COSMO-RS描述符,以及Morgan指纹,来预测类药物化合物的绝对溶解度。QM导出的描述符说明了溶质的分子性质,即人工液态(过冷液体)中的溶质-溶质相互作用,以及溶液中的溶质与溶剂相互作用。我们采用两种主要方法来预测溶解度:(i)一种假设的途径,涉及在室温T下熔化溶质 = T([公式:见正文]),并将人工液体溶质混合到溶剂中([公式,见正文]])。在这种方法中,使用机器学习模型预测[公式:见文本],并且从COSMO-RS计算中获得[公式:看文本];(ii)使用机器学习算法的直接溶解度预测。这些模型是在大量拜耳内部化合物上训练的,这些化合物的水溶性数据在6.5的生理pH和环境温度下可用。我们还使用溶解度挑战的外部数据集评估了我们的模型。与在COSMOtherm软件中实现的人工液态的QSAR模型的绝对溶解度预测相比,我们的模型在内部和外部数据集中都有很大的改进。我们还能够证明QM衍生描述符与化学信息学描述符相比的优越性。最后,我们提出了使用基于片段的COSMOquick计算的低成本替代模型,预测溶解度的质量仅略有降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state

Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state

In this study, we use machine learning algorithms with QM-derived COSMO-RS descriptors, along with Morgan fingerprints, to predict the absolute solubility of drug-like compounds. The QM-derived descriptors account for the molecular properties of the solute, i.e., the solute–solute interactions in an artificial-liquid-state (super-cooled liquid), and the solute–solvent interactions in solution. We employ two main approaches to predict solubility: (i) a hypothetical pathway that involves melting the solute at room temperature T = T¯ (\({\Delta }_{fus}{G}_{A}^{\ominus }\)) and mixing the artificially liquid solute into the solvent (\({\Delta }_{m}{G}_{\left(A:B\right)}^{\ominus }\)). In this approach \({\Delta }_{fus}{G}_{A}^{\ominus }\) is predicted using machine learning models, and the \({\Delta }_{m}{G}_{\left(A:B\right)}^{\ominus }\) is obtained from COSMO-RS calculations; (ii) direct solubility prediction using machine learning algorithms. The models were trained on a large number of Bayer in-house compounds for which water solubility data is available at physiological pH of 6.5 and ambient temperature. We also evaluated our models using external datasets from a solubility challenge. Our models present great improvements compared to the absolute solubility prediction with the QSAR model for the artificial liquid state as implemented in the COSMOtherm software, for both in-house and external datasets. We are furthermore able to demonstrate the superiority of QM-derived descriptors compared to cheminformatics descriptors. We finally present low-cost alternative models using fragment-based COSMOquick calculations with only marginal reduction in the quality of predicted solubility.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Computer-Aided Molecular Design
Journal of Computer-Aided Molecular Design 生物-计算机:跨学科应用
CiteScore
8.00
自引率
8.60%
发文量
56
审稿时长
3 months
期刊介绍: The Journal of Computer-Aided Molecular Design provides a form for disseminating information on both the theory and the application of computer-based methods in the analysis and design of molecules. The scope of the journal encompasses papers which report new and original research and applications in the following areas: - theoretical chemistry; - computational chemistry; - computer and molecular graphics; - molecular modeling; - protein engineering; - drug design; - expert systems; - general structure-property relationships; - molecular dynamics; - chemical database development and usage.
×
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:481959085
Book学术官方微信