Artificial intelligence in drug discovery: recent advances and future perspectives
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Author / Producer
Date
2021
Publication Type
Review Article
ETH Bibliography
yes
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Abstract
Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other factors, continue to fuel this development. Much of the initial skepticism regarding applications of AI in pharmaceutical discovery has started to vanish, consequently benefitting medicinal chemistry.
Areas covered: The current status of AI in chemoinformatics is reviewed. The topics discussed herein include quantitative structure-activity/property relationship and structure-based modeling, de novo molecular design, and chemical synthesis prediction. Advantages and limitations of current deep learning applications are highlighted, together with a perspective on next-generation AI for drug discovery.
Expert opinion: Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions. Open data sharing and model development will play a central role in the advancement of drug discovery with AI.
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Publication status
published
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Book title
Journal / series
Volume
16 (9)
Pages / Article No.
949 - 959
Publisher
Taylor & Francis
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Drug discovery; artificial intelligence; QSAR; de novo drug design; synthesis prediction
Organisational unit
03852 - Schneider, Gisbert / Schneider, Gisbert
Notes
Funding
182176 - De novo molecular design by deep learning (SNF)