Galileo: Three-dimensional searching in large combinatorial fragment spaces on the example of pharmacophores

Meyenburg, Christian; Dolfus, Uschi; Briem, Hans; Rarey, Matthias

doi:10.1007/s10822-022-00485-y

Cited by 13 publications

(13 citation statements)

References 64 publications

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“…In addition, a genetic algorithm-based approach has been reported for exploring synthon-based libraries using pharmacophore similarity as the scoring function. 31 Ligand shape similarity is an additional important 3D method that, to our knowledge, has not been applied to synthon-based virtual screening. In this paper, we describe such a method that we term Shape-Aware Synthon Search (SASS).…”

Section: ■ Introductionmentioning

confidence: 99%

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Shape-Aware Synthon Search (SASS) for Virtual Screening of Synthon-Based Chemical Spaces

Cheng,

Beroza

2024

J. Chem. Inf. Model.

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Virtual screening of large-scale chemical libraries has become increasingly useful for identifying high-quality candidates for drug discovery. While it is possible to exhaustively screen chemical spaces that number on the order of billions, indirect combinatorial approaches are needed to efficiently navigate larger, synthon-based virtual spaces. We describe Shape-Aware Synthon Search (SASS), a synthon-based virtual screening method that carries out shape similarity searches in the synthon space instead of the enumerated product space. SASS can replicate results from exhaustive searches in ultralarge, combinatorial spaces with high recall on a variety of query molecules while only scoring a small subspace of possible enumerated products, thereby significantly accelerating large-scale, shape-based virtual screening.

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Section: ■ Introductionmentioning

confidence: 99%

“…Those methods have hit rates of well over 10%. In addition, a genetic algorithm-based approach has been reported for exploring synthon-based libraries using pharmacophore similarity as the scoring function …”

Section: Introductionmentioning

confidence: 99%

Shape-Aware Synthon Search (SASS) for Virtual Screening of Synthon-Based Chemical Spaces

Cheng,

Beroza

2024

J. Chem. Inf. Model.

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“…Usually, large chemical spaces are described in a combinatorial manner from the building blocks and organic chemistry reactions required to synthesize them . If ligand-based approaches are now available to efficiently query these large chemical spaces, − structure-based approaches including macromolecular target information (e.g., topology of a binding site) still need to be developed to exhaustively mine multibillion chemical spaces. Several computational methods have indeed been described for such a task, − albeit with moderate to severe restrictions.…”

Section: Introductionmentioning

confidence: 99%

Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces

Sindt,

Seyller,

Eguida

et al. 2024

ACS Cent. Sci.

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Ultralarge chemical spaces describing several billion compounds are revolutionizing hit identification in early drug discovery. Because of their size, such chemical spaces cannot be fully enumerated and require ad-hoc computational tools to navigate them and pick potentially interesting hits. We here propose a structure-based approach to ultralarge chemical space screening in which commercial chemical reagents are first docked to the target of interest and then directly connected according to organic chemistry and topological rules, to enumerate drug-like compounds under three-dimensional constraints of the target. When applied to bespoke chemical spaces of different sizes and chemical complexity targeting two receptors of pharmaceutical interest (estrogen β receptor, dopamine D3 receptor), the computational method was able to quickly enumerate hits that were either known ligands (or very close analogs) of targeted receptors as well as chemically novel candidates that could be experimentally confirmed by in vitro binding assays. The proposed approach is generic, can be applied to any docking algorithm, and requires few computational resources to prioritize easily synthesizable hits from billion-sized chemical spaces.

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“…[ 31 , 33 ] However, even the combinatorial way of a structure-based molecular docking routine for chemical spaces remains a task of heavy computational effort. [ 31 – 34 ]…”

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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Galileo: Three-dimensional searching in large combinatorial fragment spaces on the example of pharmacophores

Cited by 13 publications

References 64 publications

Shape-Aware Synthon Search (SASS) for Virtual Screening of Synthon-Based Chemical Spaces

Shape-Aware Synthon Search (SASS) for Virtual Screening of Synthon-Based Chemical Spaces

Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces

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

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