Abstract
With the rapid development of computer science in scopes of theory, software, and hardware, artificial intelligence (mainly in form of machine learning and more complex deep learning) combined with advanced cheminformatics is playing an increasingly important role in drug discovery process. This development has also facilitated privileged scaffold-related research. By definition, a privileged scaffold is a structure that frequently occurs in diverse bioactive molecules, either has a diverse family affinity or is selective to multiple family members in a superfamily, whilst it is different from the“frequent hitters”, or the “pan-assay interference compounds”. The long history of the use of this concept has witnessed a functional shift from stand-alone technology towards an integrated component in the drug discovery toolbox. Meanwhile, continuous efforts have been dedicated to deepening the understandings of the features of known privileged scaffolds. In this contribution, we focus on the current privileged scaffold-related research driven by state-of-art artificial intelligence approaches and cheminformatics. Representative cases with an emphasis on distinct research aspects are presented, including an update of the knowledge on privileged scaffolds, proofof- concept tools, and workflows to identify privileged scaffolds and to carry on de novo design, informatic SAR models with diversely complex data sets to provide an instructive prediction on new potential molecules bearing privileged scaffolds.
Keywords: Artificial intelligence, Machine learning, Deep learning, Cheminformatics, Privileged scaffold, Drug discovery.
Current Topics in Medicinal Chemistry
Title:Artificial Intelligence and Cheminformatics-Guided Modern Privileged Scaffold Research
Volume: 21 Issue: 28
Author(s): Han-Yue Qiu, Rasmus Praetorius Clausen, Yun He*Hai-Liang Zhu*
Affiliation:
- School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331,China
- State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing 210023,China
Keywords: Artificial intelligence, Machine learning, Deep learning, Cheminformatics, Privileged scaffold, Drug discovery.
Abstract: With the rapid development of computer science in scopes of theory, software, and hardware, artificial intelligence (mainly in form of machine learning and more complex deep learning) combined with advanced cheminformatics is playing an increasingly important role in drug discovery process. This development has also facilitated privileged scaffold-related research. By definition, a privileged scaffold is a structure that frequently occurs in diverse bioactive molecules, either has a diverse family affinity or is selective to multiple family members in a superfamily, whilst it is different from the“frequent hitters”, or the “pan-assay interference compounds”. The long history of the use of this concept has witnessed a functional shift from stand-alone technology towards an integrated component in the drug discovery toolbox. Meanwhile, continuous efforts have been dedicated to deepening the understandings of the features of known privileged scaffolds. In this contribution, we focus on the current privileged scaffold-related research driven by state-of-art artificial intelligence approaches and cheminformatics. Representative cases with an emphasis on distinct research aspects are presented, including an update of the knowledge on privileged scaffolds, proofof- concept tools, and workflows to identify privileged scaffolds and to carry on de novo design, informatic SAR models with diversely complex data sets to provide an instructive prediction on new potential molecules bearing privileged scaffolds.
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Cite this article as:
Qiu Han-Yue , Clausen Praetorius Rasmus , He Yun *, Zhu Hai-Liang *, Artificial Intelligence and Cheminformatics-Guided Modern Privileged Scaffold Research, Current Topics in Medicinal Chemistry 2021; 21 (28) . https://dx.doi.org/10.2174/1568026621666210512020434
DOI https://dx.doi.org/10.2174/1568026621666210512020434 |
Print ISSN 1568-0266 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-4294 |
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