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LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP

التفاصيل البيبلوغرافية
العنوان: LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP
المؤلفون: Yitian Wang, Jiacheng Xiong, Fu Xiao, Wei Zhang, Kaiyang Cheng, Jingxin Rao, Buying Niu, Xiaochu Tong, Ning Qu, Runze Zhang, Dingyan Wang, Kaixian Chen, Xutong Li, Mingyue Zheng
المصدر: Journal of Cheminformatics, Vol 15, Iss 1, Pp 1-13 (2023)
بيانات النشر: BMC, 2023.
سنة النشر: 2023
المجموعة: LCC:Information technology
LCC:Chemistry
مصطلحات موضوعية: logD7.4, Lipid solubility, Graph neural network, Molecular property prediction, Information technology, T58.5-58.64, Chemistry, QD1-999
الوصف: Abstract Lipophilicity is a fundamental physical property that significantly affects various aspects of drug behavior, including solubility, permeability, metabolism, distribution, protein binding, and toxicity. Accurate prediction of lipophilicity, measured by the logD7.4 value (the distribution coefficient between n-octanol and buffer at physiological pH 7.4), is crucial for successful drug discovery and design. However, the limited availability of data for logD modeling poses a significant challenge to achieving satisfactory generalization capability. To address this challenge, we have developed a novel logD7.4 prediction model called RTlogD, which leverages knowledge from multiple sources. RTlogD combines pre-training on a chromatographic retention time (RT) dataset since the RT is influenced by lipophilicity. Additionally, microscopic pKa values are incorporated as atomic features, providing valuable insights into ionizable sites and ionization capacity. Furthermore, logP is integrated as an auxiliary task within a multitask learning framework. We conducted ablation studies and presented a detailed analysis, showcasing the effectiveness and interpretability of RT, pKa, and logP in the RTlogD model. Notably, our RTlogD model demonstrated superior performance compared to commonly used algorithms and prediction tools. These results underscore the potential of the RTlogD model to improve the accuracy and generalization of logD prediction in drug discovery and design. In summary, the RTlogD model addresses the challenge of limited data availability in logD modeling by leveraging knowledge from RT, microscopic pKa, and logP. Incorporating these factors enhances the predictive capabilities of our model, and it holds promise for real-world applications in drug discovery and design scenarios. Graphical Abstract
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1758-2946
Relation: https://doaj.org/toc/1758-2946
DOI: 10.1186/s13321-023-00754-4
URL الوصول: https://doaj.org/article/52f75671a2754928aeef022ac8dc4bea
رقم الأكسشن: edsdoj.52f75671a2754928aeef022ac8dc4bea
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:17582946
DOI:10.1186/s13321-023-00754-4