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人工智能驱动的设计药物组合:从药物研发到个性化医疗。

Artificial Intelligence-Driven Designer Drug Combinations: From Drug Development to Personalized Medicine.

机构信息

1 Cancer Science Institute of Singapore, National University of Singapore, Singapore.

2 Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.

出版信息

SLAS Technol. 2019 Feb;24(1):124-125. doi: 10.1177/2472630318800774. Epub 2018 Sep 24.

Abstract

Artificial intelligence holds great promise in transforming how drugs are designed and patients are treated. In a study recently published in Science Translational Medicine, a unique artificial intelligence platform makes efficient use of small experimental datasets to design new drug combinations as well as identify the best drug combinations for specific patient samples. This quadratic phenotypic optimization platform (QPOP) does not rely on previous assumptions of molecular mechanisms of disease, but rather uses system-specific experimental data to determine the best drug combinations for a specific disease model or a patient sample. In this commentary, we explore how QPOP was applied toward multiple myeloma in the study. We also discuss how this study demonstrates the potential for applications of QPOP toward improving therapeutic regimen design and personalized medicine.

摘要

人工智能在改变药物设计和患者治疗方式方面具有巨大的潜力。在最近发表在《科学转化医学》杂志上的一项研究中,一个独特的人工智能平台高效利用小型实验数据集来设计新的药物组合,并确定针对特定患者样本的最佳药物组合。这个二次表型优化平台(QPOP)不依赖于疾病分子机制的先前假设,而是利用特定的实验数据来确定针对特定疾病模型或患者样本的最佳药物组合。在这篇评论中,我们探讨了 QPOP 在这项研究中如何应用于多发性骨髓瘤。我们还讨论了这项研究如何展示了 QPOP 在改善治疗方案设计和个性化医学方面的应用潜力。

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