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<p>Phosphodiesterases (PDEs), particularly PDE1C, regulate cyclic nucleotide signaling and are promising therapeutic targets for diseases such as cardiovascular disorders, pulmonary hypertension, neurocognitive conditions, and certain cancers. However, the development of selective PDE1C inhibitors is hindered by the structural diversity and functional redundancy within the PDE family, with only one inhibitor, ITI-214, reaching clinical trials. Traditional experimental screening methods are resource-intensive and often yield suboptimal results, necessitating more efficient approaches. In this study, we employed an integrated computational strategy combining machine learning (ML), molecular docking, and molecular dynamics (MD) simulations to rapidly screen for novel PDE1C inhibitors. An ML model was developed to predict PDE1C inhibitory activity, validated with an out-of-sample dataset, and applied to compounds pre-selected via molecular docking (docking score ≤ -10.00&#xa0;kcal/mol) to estimate pIC<sub>50</sub> values. Six representative compounds were subjected to 100&#xa0;ns MD simulations to assess binding stability with the PDE1C protein. Top-ranked compounds underwent in vitro validation, confirming two candidates with high PDE1C inhibitory potency. This multi-tiered approach enhances screening efficiency, mitigates individual method limitations, and provides a robust framework for identifying PDE1C inhibitors, paving the way for further lead optimization and preclinical development.</p>

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Needle-in-a-haystack approach: rapid screening of PDE1C inhibitors through the combination of machine learning, molecular docking, molecular dynamics simulations and experimental validation

  • 期刊论文

  • 发表日期：

    2025年12月29日

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  • Yihuan Zhao,
  • Kun Fang,
  • Qiandan Yang,
  • Jiawang Yan,
  • Yaofeng Zhou

摘要

Phosphodiesterases (PDEs), particularly PDE1C, regulate cyclic nucleotide signaling and are promising therapeutic targets for diseases such as cardiovascular disorders, pulmonary hypertension, neurocognitive conditions, and certain cancers. However, the development of selective PDE1C inhibitors is hindered by the structural diversity and functional redundancy within the PDE family, with only one inhibitor, ITI-214, reaching clinical trials. Traditional experimental screening methods are resource-intensive and often yield suboptimal results, necessitating more efficient approaches. In this study, we employed an integrated computational strategy combining machine learning (ML), molecular docking, and molecular dynamics (MD) simulations to rapidly screen for novel PDE1C inhibitors. An ML model was developed to predict PDE1C inhibitory activity, validated with an out-of-sample dataset, and applied to compounds pre-selected via molecular docking (docking score ≤ -10.00 kcal/mol) to estimate pIC50 values. Six representative compounds were subjected to 100 ns MD simulations to assess binding stability with the PDE1C protein. Top-ranked compounds underwent in vitro validation, confirming two candidates with high PDE1C inhibitory potency. This multi-tiered approach enhances screening efficiency, mitigates individual method limitations, and provides a robust framework for identifying PDE1C inhibitors, paving the way for further lead optimization and preclinical development.