De novo molecular design and generative models
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TLDR
In this paper, the authors present de novo approaches according to the coarseness of their molecular representation: that is, whether molecular design is modeled on an atom-based, fragment-based or reaction-based paradigm.About:
This article is published in Drug Discovery Today.The article was published on 2021-06-01 and is currently open access. It has received 55 citations till now.read more
Citations
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Natural product drug discovery in the artificial intelligence era
TL;DR: In this article , the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity are discussed.
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Artificial Intelligence Technologies for COVID-19 De Novo Drug Design
TL;DR: The most significant research on artificial intelligence in de novo drug design for COVID-19 pharmaceutical research is reviewed, finding many novel molecules have been designed/identified using artificial intelligence methods with astonishing results in terms of time and effectiveness.
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Generative machine learning for de novo drug discovery: A systematic review
TL;DR: A systematic literature review of experimental articles and reviews over the last five years, machine learning models, challenges associated with computational molecule design along with proposed solutions, and molecular encoding methods are discussed in this article .
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Enhancing preclinical drug discovery with artificial intelligence
TL;DR: In this paper , the authors provide an overview of current AI technologies and a glimpse of how AI is reimagining preclinical drug discovery by highlighting examples where AI has made a real impact.
Journal ArticleDOI
Enhancing preclinical drug discovery with artificial intelligence.
TL;DR: In this article, the authors provide an overview of current AI technologies and offer a glimpse of how AI is reimagining preclinical drug discovery by highlighting examples where AI has made a real impact.
References
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Quantifying the chemical beauty of drugs
TL;DR: The utility of QED is extended by applying it to the problem of molecular target druggability assessment by prioritizing a large set of published bioactive compounds and may also capture the abstract notion of aesthetics in medicinal chemistry.
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Planning chemical syntheses with deep neural networks and symbolic AI
TL;DR: This work combines Monte Carlo tree search with an expansion policy network that guides the search, and a filter network to pre-select the most promising retrosynthetic steps that solve for almost twice as many molecules, thirty times faster than the traditional computer-aided search method.
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Inverse molecular design using machine learning: Generative models for matter engineering
TL;DR: Methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality, are reviewed.
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Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
TL;DR: This work shows that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing, and demonstrates that the properties of the generated molecules correlate very well with those of the molecules used to train the model.
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Deep learning enables rapid identification of potent DDR1 kinase inhibitors.
Alex Zhavoronkov,Yan A. Ivanenkov,Alexander Aliper,Mark S. Veselov,Vladimir A. Aladinskiy,Anastasiya V Aladinskaya,Victor A Terentiev,Daniil Polykovskiy,Maksim Kuznetsov,Arip Asadulaev,Yury Volkov,Artem Zholus,Rim Shayakhmetov,Alexander Zhebrak,Lidiya I Minaeva,Bogdan A Zagribelnyy,Lennart H Lee,Richard Soll,David Madge,Li Xing,Tao Guo,Alán Aspuru-Guzik +21 more
TL;DR: A machine learning model allows the identification of new small-molecule kinase inhibitors in days and is used to discover potent inhibitors of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, in 21 days.