Chemical Space Mimicry for Drug Discovery
- PMID: 28257191
- PMCID: PMC5802964
- DOI: 10.1021/acs.jcim.6b00754
Chemical Space Mimicry for Drug Discovery
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.
Conflict of interest statement
The authors declare no competing financial interest.
Figures
References
-
- Anderson E, Veith GD, Weininger D. SMILES: A Line Notation and Computerized Interpreter for Chemical Structures. Environmental Research Laboratory, U.S. Environmental Protection Agency; Duluth, MN: 1987. (Report EPA/600/M-87/021).
-
- Karpathy A. Multi-layer Recurrent Neural Networks for character-level language models in Torch. 2015 https://github.com/karpathy/char-rnn (accessed September 15, 2016)
MeSH terms
Substances
Grants and funding
LinkOut - more resources
Full Text Sources
Other Literature Sources
