Machine learning and AI-based approaches for bioactive ligand discovery
and GPCR-ligand recognition
(2020)cite arxiv:2001.06545Comment: 2nd submission fixed the mis-formatted quotation characters (i.e., â).Abstract
In the last decade, machine learning and artificial intelligence applications
have received a significant boost in performance and attention in both academic
research and industry. The success behind most of the recent state-of-the-art
methods can be attributed to the latest developments in deep learning. When
applied to various scientific domains that are concerned with the processing of
non-tabular data, for example, image or text, deep learning has been shown to
outperform not only conventional machine learning but also highly specialized
tools developed by domain experts. This review aims to summarize AI-based
research for GPCR bioactive ligand discovery with a particular focus on the
most recent achievements and research trends. To make this article accessible
to a broad audience of computational scientists, we provide instructive
explanations of the underlying methodology, including overviews of the most
commonly used deep learning architectures and feature representations of
molecular data. We highlight the latest AI-based research that has led to the
successful discovery of GPCR bioactive ligands. However, an equal focus of this
review is on the discussion of machine learning-based technology that has been
applied to ligand discovery in general and has the potential to pave the way
for successful GPCR bioactive ligand discovery in the future. This review
concludes with a brief outlook highlighting the recent research trends in deep
learning, such as active learning and semi-supervised learning, which have
great potential for advancing bioactive ligand discovery.
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[2001.06545] Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition
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