Exploiting machine learning for end-to-end drug discovery and development
Exploiting machine learning for end-to-end drug discovery and development
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DOI：
10.1038/s41563-019-0338-z
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发表时间：
2019-05-01
期刊：
影响因子：
41.2
通讯作者：
Clark, Alex M.
中科院分区：
文献类型：
--
作者：
Ekins, Sean;Puhl, Ana C.;Clark, Alex M.
各种机器学习方法，如朴素贝叶斯、支持向量机和最近的深度神经网络，正在证明它们在药物发现和开发中的实用性。这些利用了从高通量筛选数据创建的通常更大的数据集，并允许以更高的准确性水平预测靶标和分子特性的生物活性。我们才刚刚开始利用这些技术的潜力，但它们可能已经从根本上改变了识别新分子和/或重新利用旧药物的研究过程。这种机器学习模型在端到端（E2 E）应用中的综合应用具有广泛的相关性，对开发未来的疗法及其靶向具有相当大的影响。
A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting.