These authors contributed equally to this work.
LigMerge: A Fast Algorithm to Generate Models of Novel Potential Ligands from Sets of Known Binders
Article first published online: 27 JUN 2012
© 2012 John Wiley & Sons A/S
Chemical Biology & Drug Design
Volume 80, Issue 3, pages 358–365, September 2012
How to Cite
Lindert, S., Durrant, J. D. and McCammon, J. A. (2012), LigMerge: A Fast Algorithm to Generate Models of Novel Potential Ligands from Sets of Known Binders. Chemical Biology & Drug Design, 80: 358–365. doi: 10.1111/j.1747-0285.2012.01414.x
- Issue published online: 23 JUL 2012
- Article first published online: 27 JUN 2012
- Accepted manuscript online: 17 MAY 2012 12:50PM EST
- Received 23 February 2012, revised 13 April 2012 and accepted for publication 8 May 2012
- biophysical chemistry;
- drug design;
- structure-based drug design
One common practice in drug discovery is to optimize known or suspected ligands in order to improve binding affinity. In performing these optimizations, it is useful to look at as many known inhibitors as possible for guidance. Medicinal chemists often seek to improve potency by altering certain chemical moieties of known/endogenous ligands while retaining those critical for binding. To our knowledge, no automated, ligand-based algorithm exists for systematically ‘swapping’ the chemical moieties of known ligands to generate novel ligands with potentially improved potency. To address this need, we have created a novel algorithm called ‘LigMerge’. LigMerge identifies the maximum (largest) common substructure of two three-dimensional ligand models, superimposes these two substructures, and then systematically mixes and matches the distinct fragments attached to the common substructure at each common atom, thereby generating multiple compound models related to the known inhibitors that can be evaluated using computer docking prior to synthesis and experimental testing. To demonstrate the utility of LigMerge, we identify compounds predicted to inhibit peroxisome proliferator–activated receptor gamma, HIV reverse transcriptase, and dihydrofolate reductase with affinities higher than those of known ligands. We hope that LigMerge will be a helpful tool for the drug design community.