A membrane permeability database for nonpeptidic macrocycles.

IF 7.2 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-01-03 DOI:10.1038/s41597-024-04302-z
Qiushi Feng, Danjo De Chavez, Jan Kihlberg, Vasanthanathan Poongavanam
{"title":"A membrane permeability database for nonpeptidic macrocycles.","authors":"Qiushi Feng, Danjo De Chavez, Jan Kihlberg, Vasanthanathan Poongavanam","doi":"10.1038/s41597-024-04302-z","DOIUrl":null,"url":null,"abstract":"<p><p>The process of developing new drugs is arduous and costly, particularly for targets classified as \"difficult-to-drug.\" Macrocycles show a particular ability to modulate difficult-to-drug targets, including protein-protein interactions, while still allowing oral administration. However, the determination of membrane permeability, critical for reaching intracellular targets and for oral bioavailability, is laborious and expensive. In silico methods are a cost-effective alternative, enabling predictions prior to compound synthesis. Here, we present a comprehensive online database ( https://swemacrocycledb.com/ ), housing 5638 membrane permeability datapoints for 4216 nonpeptidic macrocycles, curated from the literature, patents, and bioactivity repositories. In addition, we present a new descriptor, the \"amide ratio\" (AR), that quantifies the peptidic nature of macrocyclic compounds, enabling the classification of peptidic, semipeptidic, and nonpeptidic macrocycles. Overall, this resource fills a gap among existing databases, offering valuable insights into the membrane permeability of nonpeptidic and semipeptidic macrocycles, and facilitating predictions for drug discovery projects.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"10"},"PeriodicalIF":7.2000,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11698989/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-024-04302-z","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

The process of developing new drugs is arduous and costly, particularly for targets classified as "difficult-to-drug." Macrocycles show a particular ability to modulate difficult-to-drug targets, including protein-protein interactions, while still allowing oral administration. However, the determination of membrane permeability, critical for reaching intracellular targets and for oral bioavailability, is laborious and expensive. In silico methods are a cost-effective alternative, enabling predictions prior to compound synthesis. Here, we present a comprehensive online database ( https://swemacrocycledb.com/ ), housing 5638 membrane permeability datapoints for 4216 nonpeptidic macrocycles, curated from the literature, patents, and bioactivity repositories. In addition, we present a new descriptor, the "amide ratio" (AR), that quantifies the peptidic nature of macrocyclic compounds, enabling the classification of peptidic, semipeptidic, and nonpeptidic macrocycles. Overall, this resource fills a gap among existing databases, offering valuable insights into the membrane permeability of nonpeptidic and semipeptidic macrocycles, and facilitating predictions for drug discovery projects.

Abstract Image

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
非肽性大环的膜通透性数据库。
开发新药的过程是艰巨而昂贵的,特别是对于那些被归类为“难以药物”的目标。大环显示出一种特殊的调节难以药物靶点的能力,包括蛋白质-蛋白质相互作用,同时仍然允许口服给药。然而,膜通透性的测定对达到细胞内靶点和口服生物利用度至关重要,既费力又昂贵。硅方法是一种成本效益高的替代方法,可以在化合物合成之前进行预测。在这里,我们提供了一个综合的在线数据库(https://swemacrocycledb.com/),包含4216个非肽大环的5638个膜透性数据点,这些数据点来自文献、专利和生物活性库。此外,我们提出了一个新的描述符,“酰胺比”(AR),量化了大环化合物的肽性质,使肽,半肽和非肽的大环分类。总的来说,该资源填补了现有数据库之间的空白,为非肽和半肽大环的膜通透性提供了有价值的见解,并促进了药物发现项目的预测。
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
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
期刊最新文献
A dataset of four Decades of Italian TV Commercials: visual, audio, and linguistic feature descriptors. A telomere-to-telomere gap-free genome assembly of the indica rice (Oryza sativa L.) cytoplasmic male sterile line Funong A. A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning. High-resolution thermal infrared dataset for airborne person detection in SAR missions. A large-scale, LLM-assisted and validated dataset of biomass and waste conversion technologies and feedstocks.
×
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学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
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