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SAFR: Enabling Fragment-Based Drug Discovery with a Synthetic Binding Pose Data Set.

Joan Cabot-March1,2, Xavier Jalencas1, Jordi Mestres1,2

  • 1Chemotargets SL, Parc Cientific de Barcelona, Baldiri Reixac 4 (TR-03), 08028 Barcelona, Catalonia, Spain.

Journal of Chemical Information and Modeling
|April 16, 2026
PubMed
Summary
This summary is machine-generated.

A new dataset, SAFR, enhances computational Fragment-Based Drug Discovery (FBDD) by providing 818,385 fragment-protein interactions. This resource supports drug design by offering novel binding poses for fragment screening and optimization.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Structural biology

Background:

  • Fragment-Based Drug Discovery (FBDD) is effective for identifying potent drug candidates from small fragments.
  • Computational FBDD relies heavily on high-quality structural data of fragment binding poses, which is often limited.
  • Existing databases may lack sufficient coverage of diverse fragment-protein interactions.

Purpose of the Study:

  • To introduce the Structurally Augmented Fragment Repository (SAFR), a novel dataset designed to address data limitations in computational FBDD.
  • To provide a comprehensive resource of fragment-protein interactions to support in silico drug design.
  • To facilitate fragment screening and hit-to-lead optimization in drug discovery campaigns.

Main Methods:

  • A filtering protocol was applied to 89,375 high-confidence binding poses from public sources, using 2D similarity, 3D superposition, docking, and interaction features.
  • Bioactive ligands were fragmented within their predicted binding poses to generate fragment-protein interactions.
  • The resulting dataset was analyzed for novelty compared to existing protein-bound ligand structures in the Protein Data Bank (PDB).

Main Results:

  • The SAFR dataset contains 818,385 fragment-protein interactions involving 157,080 unique fragments and 1,142 proteins.
  • 270,155 unique fragment-protein interactions were identified.
  • A significant majority (88%) of these unique interactions (237,284) are not represented in existing PDB structures, highlighting the novelty of the dataset.

Conclusions:

  • SAFR is a valuable new resource for computational Fragment-Based Drug Discovery.
  • The dataset effectively supports in silico fragment screening and hit-to-lead optimization.
  • SAFR provides novel fragment-protein interaction data, expanding the possibilities for drug design.