Abstract
Background/Objectives: Phosphodiesterase 7 (PDE7), a member of the PDE superfamily, selectively catalyzes the hydrolysis of cyclic adenosine 3',5'-monophosphate (cAMP), thereby regulating the intracellular levels of this second messenger and influencing various physiological functions and processes. There are two subtypes of PDE7, PDE7A and PDE7B, which are encoded by distinct genes. PDE7 inhibitors have been shown to exert therapeutic effects on neurological and respiratory diseases. However, FDA-approved drugs based on the PDE7A inhibitor are still absent, highlighting the need for novel compounds to advance PDE7A inhibitor development. Methods: To address this urgent and important issue, we conducted a comprehensive cheminformatics analysis of compounds with potential for PDE7A inhibition using a curated database to elucidate the chemical characteristics of the highly active PDE7A inhibitors. The specific substructures that significantly enhance the activity of PDE7A inhibitors, including benzenesulfonamido, acylamino, and phenoxyl, were identified by an interpretable machine learning analysis. Subsequently, a machine learning model employing the Random Forest-Morgan pattern was constructed for the qualitative and quantitative prediction of PDE7A inhibitors. Results: As a result, six compounds with potential PDE7A inhibitory activity were screened out from the SPECS compound library. These identified compounds exhibited favorable molecular properties and potent binding affinities with the target protein, holding promise as candidates for further exploration in the development of potent PDE7A inhibitors. Conclusions: The results of the present study would advance the exploration of innovative PDE7A inhibitors and provide valuable insights for future endeavors in the discovery of novel PDE inhibitors.