Leveraging binding pose metadynamics to optimise target fishing predictions for three diverse ligands and their true targets

Computational target fishing plays an important role in target identification, particularly in drug discovery campaigns utilizing phenotypic screening. Numerous approaches exist to predict potential targets for a given ligand, but true targets may be inconsistently ranked. More advanced simulation methods may provide benefit in such cases by reranking these initial predictions. We evaluated the ability of binding pose metadynamics to improve the predicted rankings for three diverse ligands and their six true targets. Initial predictions using pharmacophore mapping showed no true targets ranked in the top 50 and two targets each ranked within the 50–100, 100–150, and 250–300 ranges respectively. Following binding pose metadynamics, ranking of true targets improved for four out of the six targets and included the highest ranked predictions overall, while rankings deteriorated for two targets. The revised rankings predicted two true targets ranked within the top 50, and one target each within the 50–100, 100–150, 150–200, and 200–250 ranges respectively. The findings of this study demonstrate that binding pose metadynamics may be of benefit in refining initial predictions from structure‐based target fishing algorithms, thereby improving the efficiency of the target identification process in drug discovery efforts. The ability of binding pose metadynamics to improve the rankings of true targets predicted via pharmacophore mapping was investigated using three diverse ligands and their six true targets. Binding pose metadynamics improved the predicted rankings for four out of the six true targets, while also providing the highest‐ranked predictions.