Evidence map›Paper›PMID 42288933›Full record

ArticleJournal of cheminformatics2026

CRUSH-Cleavage Rules Using SMIRKS Heuristics: an enhanced molecular fragmentation algorithm.

Edgar López-López, José L Medina-Franco, Filip Miljković

Abstract read
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Article in Journal of cheminformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Edgar López-LópezDIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, 04510, Mexico City, Mexico.
José L Medina-FrancoDIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, 04510, Mexico City, Mexico.
Filip MiljkovićDepartment of Pharmaceutical Biosciences, Uppsala University, Box 591, 75124, Uppsala, Sweden. filip.miljkovic@uu.se.ORCID http://orcid.org/0000-0001-5365-505X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecular fragmentation methods are central to fragment-based discovery, virtual screening, and de novo molecular design, yet most existing approaches remain constrained by classical retrosynthetic rules that limit the scope and chemical space exploration beyond medicinal chemistry applications. Here we introduce CRUSH (Cleavage Rules Using SMIRKS Heuristics), an enhanced, chemistry-aware fragmentation algorithm that extends traditional retrosynthetic bond disconnection and building-block generation strategies to enable fine-grained and chemically meaningful molecular deconstruction. CRUSH adopts a flexible bond cleavage strategy in which a curated set of SMIRKS-based heuristics is applied exhaustively across all eligible bonds at each fragmentation step, enabling systematic exploration of multiple alternative disconnection pathways rather than committing to the first applicable retrosynthetic rule. Using a unified benchmarking framework across five chemically diverse datasets, including drug-like molecules, natural products, peptides, macrocycles, and alimentary compounds, we demonstrate that CRUSH consistently increases fragment yield and substantially expands chemical space coverage compared with the well-established methods. Notably, CRUSH-derived fragments access exclusive regions of chemical space that remain unexplored by existing approaches, a result that is consistent across both low- and high-resolution molecular fingerprint representations. Structurally, CRUSH generates smaller and less complex fragments, a profile particularly advantageous for fragment-based and diversity-driven workflows. Collectively, these results establish CRUSH as a robust and versatile molecular fragmentation framework that complements, and in exploratory contexts surpasses, current methods, providing a strong foundation for fragment-based chemoinformatics applications across multiple chemistry domains. As part of this work, implementation of the CRUSH fragmentation algorithm is made freely available at https://github.com/EdgL2/CRUSH .Scientific contributionThis study introduces CRUSH, a chemistry-aware molecular fragmentation method that extends beyond traditional medicinal chemistry retrosynthetic rules to enable fine-grained and chemically meaningful bond disconnections. CRUSH consistently increases fragment yield and uniquely expands chemical space coverage across diverse datasets and molecular representations, generating smaller and less complex fragments well suited for fragment-based, exploratory, and de novo design workflows.

Indexed as

Chemical librariesChemical spaceCompound designFragment librariesMolecular fragmentationOpen source

Identifiers

PMID42288933
PMCPMC13267520

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.