Development of a Pharmacophore Modeling Method and its Application to Phosphodiesterase-4 Inhibitors

The drug discovery process is an extremely time-consuming task. Therefore efficient methodologies for accelerating the process are highly desired. In silico drug discovery is one of the most promising techniques to accelerate the drug discovery process. Lead generation with such technologies has attracted great attention in recent years and many studies have been continuously published. In ligand-based virtual screening, the quality of pharmacophore models from potent known ligands affects the success of lead generation efforts. Many methods for producing pharmacophore models have been reported. However, they have both merits and demerits, and no solid method have been established. In this study, we propose a novel pharmacophore modeling method using a molecular alignment technology base on Hopfield neural network (HNN). In order to validate the proposed method, it is applied to phosphodiesterase-4 (PDE4) inhibitors. Pre-calculated conformers of six known inhibitors are aligned using HNN based molecular alignment method. Aligned molecules are ranked by a newly developed simple scoring function and subsequently pharmacophore models are extracted. The obtained pharmacophore models are validated using x-ray crystal structures of PDE4 and previous works. It has been demonstrated that our method successfully produces pharmacophore models for PDE4.