Diéguez-Santana Karel, González-Díaz Humberto
Universidad Regional Amazónica Ikiam, Parroquia Muyuna km 7 vía Alto Tena, 150150, Tena-Napo, Ecuador; Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940, Leioa, Spain.
Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940, Leioa, Spain; Basque Center for Biophysics CSIC-UPVEH, University of Basque Country UPV/EHU, 48940, Leioa, Spain; IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Biscay, Spain.
Comput Biol Med. 2023 Mar;155:106638. doi: 10.1016/j.compbiomed.2023.106638. Epub 2023 Feb 7.
Machine learning (ML) methods are used in cheminformatics processes to predict the activity of an unknown drug and thus discover new potential antibacterial drugs. This article conducts a bibliometric study to analyse the contributions of leading authors, universities/organisations and countries in terms of productivity, citations and bibliographic linkage. A sample of 1596 Scopus documents for the period 2006-2022 is the basis of the study. In order to develop the analysis, bibliometrix R-Tool and VOSviewer software were used. We determined essential topics related to the application of ML in the field of antibacterial development (Computer model in antibacterial drug design, and Learning algorithms and systems for forecasting). We identified obsolete and saturated areas of research. At the same time, we proposed emerging topics according to the various analyses carried out on the corpus of published scientific literature (Title, abstract and keywords). Finally, the applied methodology contributed to building a broader and more specific "big picture" of ML research in antibacterial studies for the focus of future projects.
机器学习（ML）方法被用于化学信息学过程中，以预测未知药物的活性，从而发现新的潜在抗菌药物。本文进行了一项文献计量学研究，从生产力、引用率和文献关联方面分析主要作者、大学/组织和国家所做的贡献。以2006年至2022年期间1596篇Scopus文献作为研究样本。为开展分析，使用了文献计量学R工具和VOSviewer软件。我们确定了与ML在抗菌药物研发领域应用相关的核心主题（抗菌药物设计中的计算机模型，以及预测的学习算法和系统）。我们识别出了过时和饱和的研究领域。同时，根据对已发表科学文献（标题、摘要和关键词）语料库所做的各种分析，我们提出了新兴主题。最后，所应用的方法有助于构建一个更广泛、更具体的关于抗菌研究中ML研究的“全景图”，作为未来项目的重点。
