LigMerge: A Fast Algorithm to Generate Models of Novel Potential Ligands from Sets of Known Binders

These authors contributed equally to this work. One common practice in drug discovery is to opti-mize known or suspected ligands in order to improve binding affinity. In performing these opti-mizations, it is useful to look at as many known inhibitors as possible for guidance. Medicinal chemists often seek to improve potency by alter-ing certain chemical moieties of known ⁄endoge-nous ligands while retaining those critical for binding. To our knowledge, no automated, ligand-based algorithm exists for systematically ’swap-ping ’ 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 iden-tifies the maximum (largest) common substructure of two three-dimensional ligand models, superim-poses these two substructures, and then systemat-ically mixes and matches the distinct fragments attached to the common substructure at each common atom, thereby generating multiple com-pound models related to the known inhibitors that can be evaluated using computer docking prior to synthesis and experimental testing. To demon-strate the utility of LigMerge, we identify com-pounds 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. Key words: biophysical chemistry, drug design, structure-based drug desig