| Literature DB >> 28257191 |
William Yuan1,2, Dadi Jiang2, Dhanya K Nambiar2, Lydia P Liew3, Michael P Hay3, Joshua Bloomstein2, Peter Lu2, Brandon Turner2, Quynh-Thu Le2, Robert Tibshirani4, Purvesh Khatri2,5, Mark G Moloney6, Albert C Koong2.
Abstract
We describe a new library generation method, Machine-based Identification of Molecules Inside Characterized Space (MIMICS), that generates sets of molecules inspired by a text-based input. MIMICS-generated libraries were found to preserve distributions of properties while simultaneously increasing structural diversity. Newly identified MIMICS-generated compounds were found to be bioactive as inhibitors of specific components of the unfolded protein response (UPR) and the VEGFR2 pathway in cell-based assays, thus confirming the applicability of this methodology toward drug design applications. Wider application of MIMICS could facilitate the efficient utilization of chemical space.Entities:
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Year: 2017 PMID: 28257191 PMCID: PMC5802964 DOI: 10.1021/acs.jcim.6b00754
Source DB: PubMed Journal: J Chem Inf Model ISSN: 1549-9596 Impact factor: 4.956