Nara Institute of Science and Technology (NAIST), Ikoma, Nara 630-0101, Japan.
Osaka University, Suita, Osaka 567-0047, Japan.
Methods. 2023 Jun;214:35-45. doi: 10.1016/j.ymeth.2023.04.001. Epub 2023 Apr 3.
Novel kinds of antibiotics are needed to combat the emergence of antibacterial resistance. Natural products (NPs) have shown potential as antibiotic candidates. Current experimental methods are not yet capable of exploring the massive, redundant, and noise-involved chemical space of NPs. In silico approaches are needed to select NPs as antibiotic candidates.
This study screens out NPs with antibacterial efficacy guided by both TCM and modern medicine and constructed a dataset aiming to serve the new antibiotic design.
A knowledge-based network is proposed in this study involving NPs, herbs, the concepts of TCM, and the treatment protocols (or etiologies) of infectious in modern medicine. Using this network, the NPs candidates are screened out and compose the dataset. Feature selection of machine learning approaches is conducted to evaluate the constructed dataset and statistically validate the im- portance of all NPs candidates for different antibiotics by a classification task.
The extensive experiments prove the constructed dataset reaches a convincing classification performance with a 0.9421 weighted accuracy, 0.9324 recall, and 0.9409 precision. The further visu- alizations of sample importance prove the comprehensive evaluation for model interpretation based on medical value considerations.
需要新型抗生素来对抗抗菌耐药性的出现。天然产物 (NPs) 已显示出作为抗生素候选物的潜力。目前的实验方法还不能探索 NPs 大量的、冗余的、涉及噪音的化学空间。需要计算方法来选择 NPs 作为抗生素候选物。
本研究旨在通过中医和现代医学的指导，筛选出具有抗菌功效的 NPs，并构建一个数据集，旨在为新型抗生素设计提供服务。
本研究提出了一种基于知识的网络，涉及 NPs、草药、中医概念和现代医学中传染病的治疗方案（或病因）。利用这个网络，筛选出 NPs 候选物并组成数据集。通过机器学习方法的特征选择，对构建的数据集进行评估，并通过分类任务对所有 NPs 候选物对不同抗生素的重要性进行统计学验证。
广泛的实验证明，构建的数据集达到了令人信服的分类性能，加权准确率为 0.9421，召回率为 0.9324，精度为 0.9409。进一步的样本重要性可视化证明了基于医学价值考虑的模型解释的综合评估。
