Amin Sk Abdul, Sessa Lucia, Gayen Shovanlal, Piotto Stefano
Department of Pharmacy, University of Salerno, Via Giovanni Paolo II 132, 84084, Fisciano, SA, Italy.
Department of Pharmaceutical Technology, Jadavpur University, Kolkata, West Bengal, 700032, India.
Mol Divers. 2025 Feb 1. doi: 10.1007/s11030-025-11118-5.
Peroxisome proliferator-activated receptor gamma (PPARγ) plays a critical role in adipocyte differentiation and enhances insulin sensitivity. In contemporary drug discovery, in silico design strategies offer significant advantages by revealing essential structural insights for lead optimization. The study is guided by two main objectives: (i) a ligand-based approach to explore the chemical space of PPARγ modulators followed by molecular docking ensembles (MDEs) to investigate ligand-binding interactions, (ii) the development of a supervised ML model for a large dataset of compounds targeting PPARγ. Additionally, the combination of chemical space networks with ML models enables the rapid screening and prediction of PPARγ modulators. These modeling analyses will assist medicinal chemists in designing more potent PPARγ modulators. To further enhance accessibility for the scientific community, we developed an online tool, "PGMP_v1," aimed at prospective screening for PPARγ modulators. The tool "PGMP_v1" is available at the provided link https://github.com/Amincheminfom/PGMP_v1 . The integration of these computational methods has uncovered crucial structural motifs that are essential for PPARγ activity, advancing the development of more effective modulators in the future.
过氧化物酶体增殖物激活受体γ（PPARγ）在脂肪细胞分化中起关键作用，并增强胰岛素敏感性。在当代药物发现中，计算机辅助设计策略通过揭示用于先导优化的基本结构见解而具有显著优势。该研究由两个主要目标指导：（i）基于配体的方法探索PPARγ调节剂的化学空间，随后进行分子对接集合（MDE）以研究配体-结合相互作用，（ii）为靶向PPARγ的大量化合物数据集开发监督式机器学习模型。此外，化学空间网络与机器学习模型的结合能够快速筛选和预测PPARγ调节剂。这些建模分析将有助于药物化学家设计更有效的PPARγ调节剂。为了进一步提高科学界的可及性，我们开发了一个在线工具“PGMP_v1”，旨在对PPARγ调节剂进行前瞻性筛选。工具“PGMP_v1”可通过提供的链接https://github.com/Amincheminfom/PGMP_v1获得。这些计算方法的整合揭示了对PPARγ活性至关重要的关键结构基序，推动了未来更有效调节剂的开发。
