From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Issue 11 (November 2017)

• Record Type: Journal Article
• Authors: Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao
• Is Part Of: Drug discovery today. Volume 22:Issue 11(2017)
• Page Start: 1680
• Page End: 1685
• Publication Date: 2017-11
• Subjects: Drugs -- Design -- Periodicals; Drugs -- Research -- Periodicals; 615.1
• DOI: 10.1016/j.drudis.2017.08.010
• Languages: English
• ISSNs: 1359-6446

Abstract

Highlights: Six commonly used machine learning methods in QSAR models are summarized. Newly developed combinatorial QSAR and hybrid QSAR methods are discussed. Deep learning methods are new trend in modern drug discovery under big data era. Clinical drug candidates discovered by computational methods are also offered. Abstract : Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure–activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era.