e MolFrag 2 and e Synth 2: Sound Fragment-Based Molecular Decomposition and Reconstruction

Abstract We present eMolFrag2 and eSynth2, enhanced successors to eMolFrag and eSynth for fragment-based molecular decomposition and reconstruction. eMolFrag2 is a complete reimplementation of eMolFrag in Python, using RDKit and graph-based molecular representations to preserve connectivity, identify free atoms, combine adjacent linkers, and avoid aromaticity ambiguities that impair downstream reconstruction. eSynth2 generalizes fragment recombination by removing hard-coded molecular-property filters, supporting multiple bonds to a single brick atom, and adding a targeted reconstruction mode for soundness validation. Across 36,534 nonredundant DUD-E active compounds, the eMolFrag2 → eSynth2 workflow reconstructed every input molecule exactly (Tanimoto coefficient, TC = 1.0), whereas the original eMolFrag→ eSynth workflow reconstructed 51 of the 100 molecules in a benchmark subset. The upgraded workflow also produced broader recombinant chemical spaces, including higher entropy in a four-molecule diversity analysis (2.78 versus 2.70). Both tools are open-source, platform-agnostic, and available for academic and commercial use.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2026-10-14
DOI
https://doi.org/10.1021/acs.jcim.6c01714
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
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article

e MolFrag 2 and e Synth 2: Sound Fragment-Based Molecular Decomposition and Reconstruction

Adam Bess, Chris Alvin, Sean Rowland, Meisam Adibifard et al.
Journal of Chemical Information and Modeling
Computational Drug Discovery Methods
article

e MolFrag 2 and e Synth 2: Sound Fragment-Based Molecular Decomposition and Reconstruction

Adam Bess, Chris Alvin, Sean Rowland, Meisam Adibifard, Supratik Mukhopadhyay
article en

Abstract

Abstract We present eMolFrag2 and eSynth2, enhanced successors to eMolFrag and eSynth for fragment-based molecular decomposition and reconstruction. eMolFrag2 is a complete reimplementation of eMolFrag in Python, using RDKit and graph-based molecular representations to preserve connectivity, identify free atoms, combine adjacent linkers, and avoid aromaticity ambiguities that impair downstream reconstruction. eSynth2 generalizes fragment recombination by removing hard-coded molecular-property filters, supporting multiple bonds to a single brick atom, and adding a targeted reconstruction mode for soundness validation. Across 36,534 nonredundant DUD-E active compounds, the eMolFrag2 → eSynth2 workflow reconstructed every input molecule exactly (Tanimoto coefficient, TC = 1.0), whereas the original eMolFrag→ eSynth workflow reconstructed 51 of the 100 molecules in a benchmark subset. The upgraded workflow also produced broader recombinant chemical spaces, including higher entropy in a four-molecule diversity analysis (2.78 versus 2.70). Both tools are open-source, platform-agnostic, and available for academic and commercial use.

Journal of Chemical Information and Modeling
Louisiana State University (US), Furman University (US)
Openalex Percentile: Top 14%
Computational Drug Discovery Methods
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