ArticleBioinformatics (Oxford, England)2017
Spresso: an ultrafast compound pre-screening method based on compound decomposition.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Prioritising search for virtual screening via preliminary interpretable low-feature likelihood-based rankings of drug-target activity measures.BMC bioinformatics · 2026Article
- COFFEE-PRESC: A Fast Prescreening Method Using Compound Retrieval by Pairwise Positional Relationship of Representative Fragments.Journal of chemical information and modeling · 2026Article
- QUBO Problem Formulation of Fragment-Based Protein-Ligand Flexible Docking.Entropy (Basel, Switzerland) · 2024Article
- AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection.Scientific reports · 2023Article
- Effective Protein-Ligand Docking Strategy via Fragment Reuse and a Proof-of-Concept Implementation.ACS omega · 2022Article
- Solving Generalized Polyomino Puzzles Using the Ising Model.Entropy (Basel, Switzerland) · 2022Article
- V-Dock: Fast Generation of Novel Drug-like Molecules Using Machine-Learning-Based Docking Score and Molecular Optimization.International journal of molecular sciences · 2021Article
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Authors and funding
6 authors.
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Abstract
motivationRecently, the number of available protein tertiary structures and compounds has increased. However, structure-based virtual screening is computationally expensive owing to docking simulations. Thus, methods that filter out obviously unnecessary compounds prior to computationally expensive docking simulations have been proposed. However, the calculation speed of these methods is not fast enough to evaluate ≥ 10 million compounds.
resultsIn this article, we propose a novel, docking-based pre-screening protocol named Spresso (Speedy PRE-Screening method with Segmented cOmpounds). Partial structures (fragments) are common among many compounds; therefore, the number of fragment variations needed for evaluation is smaller than that of compounds. Our method increases calculation speeds by ∼200-fold compared to conventional methods. AVAILABILITY AND IMPLEMENTATION: Spresso is written in C ++ and Python, and is available as an open-source code (http://www.bi.cs.titech.ac.jp/spresso/) under the GPLv3 license. CONTACT: akiyama@c.titech.ac.jp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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