Laboratory of Food, Drugs, & Cosmetics (LTMAC), University of Brasilia, 70910-900, Brasília, DF, Brazil.
Nanomedicine (Lond). 2024;19(14):1271-1283. doi: 10.1080/17435889.2024.2359355. Epub 2024 Jun 21.
Artificial intelligence has revolutionized many sectors with unparalleled predictive capabilities supported by machine learning (ML). So far, this tool has not been able to provide the same level of development in pharmaceutical nanotechnology. This review discusses the current data science methodologies related to polymeric drug-loaded nanoparticle production from an innovative multidisciplinary perspective while considering the strictest data science practices. Several methodological and data interpretation flaws were identified by analyzing the few qualified ML studies. Most issues lie in following appropriate analysis steps, such as cross-validation, balancing data, or testing alternative models. Thus, better-planned studies following the recommended data science analysis steps along with adequate numbers of experiments would change the current landscape, allowing the exploration of the full potential of ML.
人工智能凭借机器学习（ML）的无与伦比的预测能力，彻底改变了许多领域。到目前为止，这一工具在药物纳米技术方面还未能提供同等水平的发展。本综述从创新的多学科角度讨论了当前与聚合物载药纳米颗粒生产相关的数据科学方法，并考虑了最严格的数据科学实践。通过分析少数合格的 ML 研究，确定了几个方法学和数据解释缺陷。大多数问题在于遵循适当的分析步骤，例如交叉验证、平衡数据或测试替代模型。因此，按照推荐的数据科学分析步骤进行更好规划的研究，并增加足够数量的实验，将改变当前的局面，从而能够充分挖掘 ML 的潜力。
