ArticleJournal of chemical information and modeling2017
Break Down in Order To Build Up: Decomposing Small Molecules for Fragment-Based Drug Design with eMolFrag.
Article in Journal of chemical information and modeling, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed.
- CCVAN Leverages Conditional Molecular Generation Through Conditional VAE and Wasserstein GAN.Interdisciplinary sciences, computational life sciences · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Ai derivation and exploration of antibiotic class spaces.Journal of cheminformatics · 2026Article
- Review
- Fragmenstein: predicting protein-ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding-based methodology.Journal of cheminformatics · 2025Article
- Group graph: a molecular graph representation with enhanced performance, efficiency and interpretability.Journal of cheminformatics · 2024Article
- Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN.Nature communications · 2024Article
- High-resolution RNA tertiary structures in Zika virus stem-loop A for the development of inhibitory small molecules.RNA (New York, N.Y.) · 2024Article
- Molecular fragmentation as a crucial step in the AI-based drug development pathway.Communications chemistry · 2024Review
- Expanding Bioactive Fragment Space with the Generated Database GDB-13s.Journal of chemical information and modeling · 2023Article
- ACFIS 2.0: an improved web-server for fragment-based drug discovery via a dynamic screening strategy.Nucleic acids research · 2023Article
- Towards systematic exploration of chemical space: building the fragment library module in molecular property diagnostic suite.Molecular diversity · 2023Article
- MacFrag: segmenting large-scale molecules to obtain diverse fragments with high qualities.Bioinformatics (Oxford, England) · 2023Article
- Identification of oral therapeutics using an AI platform against the virus responsible for COVID-19, SARS-CoV-2.Frontiers in pharmacology · 2023Article
- OSCAR: an extensive repository of chemically and functionally diverse organocatalysts.Chemical science · 2022Article
- Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases.Drug discovery today · 2022Review
- Molecular generation by Fast Assembly of (Deep)SMILES fragments.Journal of cheminformatics · 2021Article
- KLIFS: an overhaul after the first 5 years of supporting kinase research.Nucleic acids research · 2021Article
- Machine Learning Algorithm Identifies an Antibiotic Vocabulary for Permeating Gram-Negative Bacteria.Journal of chemical information and modeling · 2020Article
- CReM: chemically reasonable mutations framework for structure generation.Journal of cheminformatics · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Constructing high-quality libraries of molecular building blocks is essential for successful fragment-based drug discovery. In this communication, we describe eMolFrag, a new open-source software to decompose organic compounds into nonredundant fragments retaining molecular connectivity information. Given a collection of molecules, eMolFrag generates a set of unique fragments comprising larger moieties, bricks, and smaller linkers connecting bricks. These building blocks can subsequently be used to construct virtual screening libraries for targeted drug discovery. The robustness and computational performance of eMolFrag is assessed against the Directory of Useful Decoys, Enhanced database conducted in serial and parallel modes with up to 16 computing cores. Further, the application of eMolFrag in de novo drug design is illustrated using the adenosine receptor. eMolFrag is implemented in Python, and it is available as stand-alone software and a web server at www.brylinski.org/emolfrag and https://github.com/liutairan/eMolFrag .
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