Chemical Space Mimicry for Drug Discovery
Journal of Chemical Information and Modeling2017Vol. 57(4), pp. 875–882
Citations Over TimeTop 10% of 2017 papers
William Yuan, Dadi Jiang, Dhanya K. Nambiar, Lydia P. Liew, Michael P. Hay, Joshua Bloomstein, Peter L. Lu, Brandon E. Turner, Quynh‐Thu Le, Robert Tibshirani, Purvesh Khatri, Mark G. Moloney, Albert C. Koong
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.
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