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.
Authors
- Adam Bess (ORCID: https://orcid.org/0000-0003-1156-076X)
- Chris Alvin (ORCID: https://orcid.org/0000-0001-6044-2159)
- Sean Rowland
- Meisam Adibifard
- Supratik Mukhopadhyay (ORCID: https://orcid.org/0000-0003-0839-1133)
Institutions
- Louisiana State University (US)
- Furman University (US)
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
- 0.00