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Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics.
OMICS: A Journal of Integrative Biology ( IF 3.3 ) Pub Date : 2019-10-25 , DOI: 10.1089/omi.2019.0151
Maria Koromina 1 , Maria-Theodora Pandi 1 , George P Patrinos 1, 2, 3
Affiliation  

Pharmaceutical industry and the art and science of drug development are sorely in need of novel transformative technologies in the current age of digital health and artificial intelligence (AI). Often described as game-changing technologies, AI and machine learning algorithms have slowly but surely begun to revolutionize pharmaceutical industry and drug development over the past 5 years. In this expert review, we describe the most frequently used machine learning algorithms in drug development pipelines and the -omics databases well poised to support machine learning and drug discovery. Subsequently, we analyze the emerging new computational approaches to drug discovery and the in silico pipelines for drug repositioning and the synergies among -omics system sciences, AI and machine learning. As with system sciences, AI and machine learning embody a system scale and Big Data driven vision for drug discovery and development. We conclude with a future outlook on the ways in which machine learning approaches can be implemented to buttress and expedite drug discovery and precision medicine. As AI and machine learning are rapidly entering pharmaceutical industry and the art and science of drug development, we need to critically examine the attendant prospects and challenges to benefit patients and public health.

中文翻译:

通过人工智能,机器学习和Omics重新思考药物的重新定位和开发。

在当前的数字健康和人工智能(AI)时代,制药行业以及药物开发的艺术和科学迫切需要新颖的转化技术。人工智能和机器学习算法通常被认为是改变游戏规则的技术,在过去的五年中,它已经缓慢但肯定会开始彻底改变制药行业和药物开发。在这篇专家评论中,我们描述了药物开发流程中最常用的机器学习算法以及准备支持机器学习和药物发现的-omics数据库。随后,我们分析了用于药物发现的新兴新计算方法以及用于药物重新定位的计算机模拟流水线以及组学系统科学,人工智能和机器学习之间的协同作用。与系统科学一样,人工智能和机器学习体现了系统规模和大数据驱动的药物发现和开发愿景。我们以对机器学习方法可以用来支持和加速药物发现和精密医学的方法的未来展望结束。随着人工智能和机器学习正在迅速进入制药行业以及药物开发的艺术和科学领域,我们需要认真研究随之而来的前景和挑战,以造福于患者和公众健康。
更新日期:2019-11-01
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