Frontiers in drug discovery最新文献_第2页

Editorial: Women in anti-inflammatory and immunomodulating agents: 2022 社论：抗炎剂和免疫调节剂中的女性：2022

Frontiers in drug discovery Pub Date : 2024-02-22 DOI: 10.3389/fddsv.2024.1380166

Jamie B. Spangler, Vanina A. Medina

引用次数: 0

Editorial: Use of computational tools for designing epigenetic drugs 社论：利用计算工具设计表观遗传药物

Frontiers in drug discovery Pub Date : 2024-02-21 DOI: 10.3389/fddsv.2024.1381450

Y. Sixto-López, A. Uba, Kuldeep K. Roy

引用次数: 0

Machine learning in toxicological sciences: opportunities for assessing drug toxicity 毒理学中的机器学习：评估药物毒性的机遇

Frontiers in drug discovery Pub Date : 2024-02-08 DOI: 10.3389/fddsv.2024.1336025

L. Tonoyan, Arno G. Siraki

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引用次数: 0

Machine learning in toxicological sciences: opportunities for assessing drug toxicity 毒理学中的机器学习：评估药物毒性的机遇

Frontiers in drug discovery Pub Date : 2024-02-08 DOI: 10.3389/fddsv.2024.1336025

L. Tonoyan, Arno G. Siraki

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引用次数: 0

Editorial: Discovery of EGFR tyrosine kinase inhibitors for cancer treatment 社论：发现治疗癌症的表皮生长因子受体酪氨酸激酶抑制剂

Frontiers in drug discovery Pub Date : 2024-02-07 DOI: 10.3389/fddsv.2024.1365318

Panupong Mahalapbutr, T. Rungrotmongkol

引用次数: 0

Editorial: Discovery of EGFR tyrosine kinase inhibitors for cancer treatment 社论：发现治疗癌症的表皮生长因子受体酪氨酸激酶抑制剂

Frontiers in drug discovery Pub Date : 2024-02-07 DOI: 10.3389/fddsv.2024.1365318

Panupong Mahalapbutr, T. Rungrotmongkol

引用次数: 0

Rapid screening of chemicals for their potential to cause specific toxidromes 快速筛选可能导致特定毒血症的化学品

Frontiers in drug discovery Pub Date : 2024-02-05 DOI: 10.3389/fddsv.2024.1324564

Ruifeng Liu, M. AbdulHameed, Zhen Xu, Benjamin Clancy, V. Desai, Anders Wallqvist

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引用次数: 0

Rapid screening of chemicals for their potential to cause specific toxidromes 快速筛选可能导致特定毒血症的化学品

Frontiers in drug discovery Pub Date : 2024-02-05 DOI: 10.3389/fddsv.2024.1324564

Ruifeng Liu, M. AbdulHameed, Zhen Xu, Benjamin Clancy, V. Desai, Anders Wallqvist

{"title":"Rapid screening of chemicals for their potential to cause specific toxidromes","authors":"Ruifeng Liu, M. AbdulHameed, Zhen Xu, Benjamin Clancy, V. Desai, Anders Wallqvist","doi":"10.3389/fddsv.2024.1324564","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1324564","url":null,"abstract":"Toxidromes constitute patterns of symptoms and signs caused by specific toxic effects that guide emergency treatments. Computational identification of chemicals that cause different toxidromes allows us to rapidly screen novel compounds and compound classes as to their potential toxicity. The aim of the current study was to create a computational toolset that can map chemicals to their potential toxidromes. Hence, we evaluated the performance of a state-of-the-art deep learning method—the recently developed communicative message passing neural network (CMPNN)—for its ability to overcome the use of small datasets for training deep learning models. Our results indicated that multi-task training—a technique known for its ability to use multiple small datasets to train conventional deep neural networks—works equally well with CMPNN. We also showed that CMPNN-based ensemble learning results in more reliable predictions than those obtained using a single CMPNN model. In addition, we showed that the standard deviations of individual model predictions from an ensemble of CMPNN models correlated with the errors of ensemble predictions and could be used to estimate the reliability of ensemble predictions. For toxidromes that do not have well-defined molecular mechanisms or sufficient data to train a deep learning model, we used the similarity ensemble approach to develop molecular structural similarity-based toxidrome models. We made the toolset developed in this study publicly accessible via a web user interface at https://toxidrome.bhsai.org/.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"3 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139863559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep generative models in the quest for anticancer drugs: ways forward 探索抗癌药物的深度生成模型：前进之路

Frontiers in drug discovery Pub Date : 2024-02-02 DOI: 10.3389/fddsv.2024.1362956

Virgilio Romanelli, Carmen Cerchia, Antonio Lavecchia

引用次数: 0

Deep generative models in the quest for anticancer drugs: ways forward 探索抗癌药物的深度生成模型：前进之路

Frontiers in drug discovery Pub Date : 2024-02-02 DOI: 10.3389/fddsv.2024.1362956

Virgilio Romanelli, Carmen Cerchia, Antonio Lavecchia

引用次数: 0