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J Comput Chem. 2017 Nov 15;38(30):2641-2663. doi: 10.1002/jcc.25052. Epub 2017 Sep 22.

Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets.

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Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, 11794.
Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, New York, 11794.
Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, 94158.
Institute of Chemical Biology and Drug Discovery, Stony Brook University, Stony Brook, New York, 11794.
Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, 11794.


De novo design can be used to explore vast areas of chemical space in computational lead discovery. As a complement to virtual screening, from-scratch construction of molecules is not limited to compounds in pre-existing vendor catalogs. Here, we present an iterative fragment growth method, integrated into the program DOCK, in which new molecules are built using rules for allowable connections based on known molecules. The method leverages DOCK's advanced scoring and pruning approaches and users can define very specific criteria in terms of properties or features to customize growth toward a particular region of chemical space. The code was validated using three increasingly difficult classes of calculations: (1) Rebuilding known X-ray ligands taken from 663 complexes using only their component parts (focused libraries), (2) construction of new ligands in 57 drug target sites using a library derived from ∼13M drug-like compounds (generic libraries), and (3) application to a challenging protein-protein interface on the viral drug target HIVgp41. The computational testing confirms that the de novo DOCK routines are robust and working as envisioned, and the compelling results highlight the potential utility for designing new molecules against a wide variety of important protein targets.


DOCK; ZINC; chemical space; de novo design; drug discovery; footprint similarity; fragment libraries; scoring functions; structure-based design

[Available on 2018-11-15]

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