Feature Trees

Brown, Nathan

doi:10.1002/9783527665143.ch09

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…[8][9][10][11] The imminent interest in individual chemical spaces brought a huge variety of free, [12] proprietary, [7,[13][14][15][16][17] and make-on-demand [18][19][20][21][22] spaces as well as tools to create those. [23][24][25] While navigating such spaces using a variety of 2D methods is well established by now and incredibly fast, [26][27][28][29] comparable 3D methods are largely missing as of today. Yet, the ability to take molecular shape into account and thereby facilitate scaffold hopping is extremely desirable but more complex.…”

Section: Introductionmentioning

confidence: 99%

SpaceGrow: efficient shape-based virtual screening of billion-sized combinatorial fragment spaces

Hönig,

Flachsenberg,

Ehrt

et al. 2024

J Comput Aided Mol Des

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The growing size of make-on-demand chemical libraries is posing new challenges to cheminformatics. These ultra-large chemical libraries became too large for exhaustive enumeration. Using a combinatorial approach instead, the resource requirement scales approximately with the number of synthons instead of the number of molecules. This gives access to billions or trillions of compounds as so-called chemical spaces with moderate hardware and in a reasonable time frame. While extremely performant ligand-based 2D methods exist in this context, 3D methods still largely rely on exhaustive enumeration and therefore fail to apply. Here, we present SpaceGrow: a novel shape-based 3D approach for ligand-based virtual screening of billions of compounds within hours on a single CPU. Compared to a conventional superposition tool, SpaceGrow shows comparable pose reproduction capacity based on RMSD and superior ranking performance while being orders of magnitude faster. Result assessment of two differently sized subsets of the eXplore space reveals a higher probability of finding superior results in larger spaces highlighting the potential of searching in ultra-large spaces. Furthermore, the application of SpaceGrow in a drug discovery workflow was investigated in four examples involving G protein-coupled receptors (GPCRs) with the aim to identify compounds with similar binding capabilities and molecular novelty. Graphical abstract SpaceGrow descriptor comparison for an example cut in the molecule of interest. Scoring scheme is implied for one fragment of this cut.

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