Journal ArticleDOI
Data-driven medicinal chemistry in the era of big data
TLDR
Data-driven medicinal chemistry approaches have the potential to improve decision making in drug discovery projects, providing that all researchers embrace the role of 'data scientist' and uncover the meaningful relationships and patterns in available data.About:
This article is published in Drug Discovery Today.The article was published on 2014-07-01. It has received 113 citations till now. The article focuses on the topics: Big data.read more
Citations
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Journal ArticleDOI
Deep Learning in Drug Discovery.
TL;DR: An overview of this emerging field of molecular informatics, the basic concepts of prominent deep learning methods are presented, and motivation to explore these techniques for their usefulness in computer‐assisted drug discovery and design is offered.
Journal ArticleDOI
Rethinking drug design in the artificial intelligence era
Petra Schneider,W. Patrick Walters,Alleyn T. Plowright,Norman Sieroka,Jennifer Listgarten,Robert Alan Goodnow,Jasmin Fisher,Jasmin Fisher,Johanna M. Jansen,José S. Duca,Thomas S. Rush,Matthias Zentgraf,John Edward Hill,Elizabeth Krutoholow,Matthias Kohler,Jeff Blaney,Kimito Funatsu,Kimito Funatsu,Chris Luebkemann,Chris Luebkemann,Gisbert Schneider +20 more
TL;DR: The views of a diverse group of international experts on the ‘grand challenges’ in small-molecule drug discovery with AI are presented, including obtaining appropriate data sets, generating new hypotheses, optimizing in a multi-objective manner, reducing cycle times and changing the research culture.
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A Structure-Based Drug Discovery Paradigm.
TL;DR: This review focuses on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.
Journal ArticleDOI
In silico ADME/T modelling for rational drug design
Yulan Wang,Jing Xing,Yuan Xu,Nannan Zhou,Jianlong Peng,Zhaoping Xiong,Xian Liu,Xiaomin Luo,Cheng Luo,Kaixian Chen,Mingyue Zheng,Hualiang Jiang +11 more
TL;DR: The development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models are introduced.
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Synthetic organic chemistry driven by artificial intelligence
TL;DR: The underlying concepts of artificial intelligence are examined to demystify AI for bench chemists in order that they may embrace it as a tool rather than fear it as an competitor, spur future research by pinpointing the gaps in knowledge and delineate how chemical AI will run in the era of digital chemistry.
References
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Journal ArticleDOI
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
TL;DR: Experimental and computational approaches to estimate solubility and permeability in discovery and development settings are described in this article, where the rule of 5 is used to predict poor absorption or permeability when there are more than 5 H-bond donors, 10 Hbond acceptors, and the calculated Log P (CLogP) is greater than 5 (or MlogP > 415).
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Lead- and drug-like compounds: the rule-of-five revolution.
TL;DR: This topic is explored in terms ofDrug-like physicochemical features, drug-like structural features, a comparison of drug- like and non-drug-like in drug discovery and a discussion of how drug-Like features relate to clinical success.
Journal ArticleDOI
Drug-like properties and the causes of poor solubility and poor permeability
TL;DR: There are currently about 10000 drug-like compounds, and true diversity does not exist in experimental combinatorial chemistry screening libraries because current ADME experimental screens are multi-mechanisms, and predictions get worse as more data accumulates.
Journal ArticleDOI
ChEMBL: a large-scale bioactivity database for drug discovery
Anna Gaulton,Louisa J. Bellis,A. Patrícia Bento,Jon Chambers,Mark Davies,Anne Hersey,Yvonne Light,Shaun McGlinchey,David Michalovich,Bissan Al-Lazikani,John P. Overington +10 more
TL;DR: ChEMBL is an Open Data database containing binding, functional and ADMET information for a large number of drug-like bioactive compounds to maximize their quality and utility across a wide range of chemical biology and drug-discovery research problems.
Patent
High throughput screen
TL;DR: In this paper, a high throughput screen for determining the effect of test compounds on ion channel or transporter activity was proposed, and a method for monitoring ion channel activity in a membrane.