ArticlePLoS computational biology2014
Knowledge-based fragment binding prediction.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Unsupervised learning reveals landscape of local structural motifs across protein classes.Bioinformatics (Oxford, England) · 2025Article
- Navigating structure-based drug discovery with emerging innovations in physics- and knowledge-based approaches.npj drug discovery · 2025Review
- Predicting fragment binding modes using customized Lennard-Jones potentials in short molecular dynamics simulations.Computational and structural biotechnology journal · 2025Article
- Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening.Journal of chemical information and modeling · 2024Article
- COLLAPSE: A representation learning framework for identification and characterization of protein structural sites.Protein science : a publication of the Protein Society · 2023Article
- Defining A Global Map of Functional Group-based 3D Ligand-binding Motifs.Genomics, proteomics & bioinformatics · 2022Article
- CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities.Journal of computer-aided molecular design · 2021Article
- A deep learning framework to predict binding preference of RNA constituents on protein surface.Nature communications · 2019Article
- High precision protein functional site detection using 3D convolutional neural networks.Bioinformatics (Oxford, England) · 2019Article
- Ranking Enzyme Structures in the PDB by Bound Ligand Similarity to Biological Substrates.Structure (London, England : 1993) · 2018Article
- Identification of Histamine HScientific reports · 2017Article
- 3D deep convolutional neural networks for amino acid environment similarity analysis.BMC bioinformatics · 2017Article
- Combination therapeutics in complex diseases.Journal of cellular and molecular medicine · 2016Review
- IsoMIF Finder: online detection of binding site molecular interaction field similarities.Bioinformatics (Oxford, England) · 2016Article
- Considerations of Protein Subpockets in Fragment-Based Drug Design.Chemical biology & drug design · 2016Article
- Modeling enzyme-ligand binding in drug discovery.Journal of cheminformatics · 2015Review
- PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins.Nucleic acids research · 2015Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening.
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