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.
Graphical Abstract
Call for Papers in Thematic Issues
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