| Literature DB >> 29883107 |
Laurent Hoffer1, Yuliia V Voitovich1,2, Brigitt Raux1, Kendall Carrasco1, Christophe Muller3, Aleksey Y Fedorov2, Carine Derviaux3, Agnès Amouric1,3, Stéphane Betzi1, Dragos Horvath4, Alexandre Varnek4, Yves Collette1,3, Sébastien Combes1, Philippe Roche1, Xavier Morelli1,3.
Abstract
Over the past few decades, hit identification has been greatly facilitated by advances in high-throughput and fragment-based screenings. One major hurdle remaining in drug discovery is process automation of hit-to-lead (H2L) optimization. Here, we report a time- and cost-efficient integrated strategy for H2L optimization as well as a partially automated design of potent chemical probes consisting of a focused-chemical-library design and virtual screening coupled with robotic diversity-oriented de novo synthesis and automated in vitro evaluation. The virtual library is generated by combining an activated fragment, corresponding to the substructure binding to the target, with a collection of functionalized building blocks using in silico encoded chemical reactions carefully chosen from a list of one-step organic transformations relevant in medicinal chemistry. The proof of concept was demonstrated using the optimization of bromodomain inhibitors as a test case, leading to the validation of several compounds with improved affinity by several orders of magnitude.Mesh:
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Year: 2018 PMID: 29883107 DOI: 10.1021/acs.jmedchem.8b00653
Source DB: PubMed Journal: J Med Chem ISSN: 0022-2623 Impact factor: 7.446