建立人工神经网络模型,预测新型口服抗冠状病毒SARS-CoV-2药物的PAMPA有效渗透率。

IF 2 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Network Modeling and Analysis in Health Informatics and Bioinformatics Pub Date : 2023-01-01 DOI:10.1007/s13721-023-00410-9
Chrysoula Gousiadou, Philip Doganis, Haralambos Sarimveis
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引用次数: 2

摘要

为应对新冠肺炎大流行,科学界加大了抗疫药物研发力度。为了加强这些努力,“COVID登月计划”项目一直在接受公众对计算分类、合成和测试分子的建议。该项目旨在确定具有抗病毒活性的低分子量分子,用于口服治疗。药物穿过肠细胞膜并进入循环的能力决定性地影响其生物利用度,因此需要在药物发现的早期阶段优化通透性。在我们目前的工作中,作为对正在进行的科学努力的贡献,我们采用人工神经网络算法开发QSAR工具,用于模拟口服药物的PAMPA有效渗透性(被动扩散)。我们确定了61个与解释药物细胞渗透性最相关的特征,并利用它们开发了一个堆叠回归集合模型,随后用于预测通过COVID Moonshot项目提供的数据集中包含的分子的渗透性。我们的模型被证明是稳健的,并可能为预测尚未合成的分子的潜在渗透性提供一个有希望的框架,从而指导药物设计的过程。补充信息:在线版本包含补充资料,下载地址:10.1007/s13721-023-00410-9。
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Development of artificial neural network models to predict the PAMPA effective permeability of new, orally administered drugs active against the coronavirus SARS-CoV-2.

Responding to the pandemic caused by SARS-CoV-2, the scientific community intensified efforts to provide drugs effective against the virus. To strengthen these efforts, the "COVID Moonshot" project has been accepting public suggestions for computationally triaged, synthesized, and tested molecules. The project aimed to identify molecules of low molecular weight with activity against the virus, for oral treatment. The ability of a drug to cross the intestinal cell membranes and enter circulation decisively influences its bioavailability, and hence the need to optimize permeability in the early stages of drug discovery. In our present work, as a contribution to the ongoing scientific efforts, we employed artificial neural network algorithms to develop QSAR tools for modelling the PAMPA effective permeability (passive diffusion) of orally administered drugs. We identified a set of 61 features most relevant in explaining drug cell permeability and used them to develop a stacked regression ensemble model, subsequently used to predict the permeability of molecules included in datasets made available through the COVID Moonshot project. Our model was shown to be robust and may provide a promising framework for predicting the potential permeability of molecules not yet synthesized, thus guiding the process of drug design.

Supplementary information: The online version contains supplementary material available at 10.1007/s13721-023-00410-9.

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来源期刊
CiteScore
5.40
自引率
4.30%
发文量
43
期刊介绍: NetMAHIB publishes original research articles and reviews reporting how graph theory, statistics, linear algebra and machine learning techniques can be effectively used for modelling and analysis in health informatics and bioinformatics. It aims at creating a synergy between these disciplines by providing a forum for disseminating the latest developments and research findings; hence, results can be shared with readers across institutions, governments, researchers, students, and the industry. The journal emphasizes fundamental contributions on new methodologies, discoveries and techniques that have general applicability and which form the basis for network based modelling, knowledge discovery, knowledge sharing and decision support to the benefit of patients, healthcare professionals and society in traditional and advanced emerging settings, including eHealth and mHealth .
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