Department of Pharmacoinformatics, National Institute of Pharmaceutical Educational and Research (NIPER), Kolkata, India.
Drug Theoretics and Cheminformatics Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India.
SAR QSAR Environ Res. 2021 Jul;32(7):595-614. doi: 10.1080/1062936X.2021.1939150. Epub 2021 Jun 21.
The quantitative structure-property relationship (QSPR) method is commonly used to predict different physicochemical characteristics of interest of chemical compounds with an objective to accelerate the process of design and development of novel chemical compounds in the biotechnology and healthcare industries. In the present report, we have employed a QSPR approach to predict the different properties of the aminoglycoside-derived polymers (i.e. polymer DNA binding and aminoglycoside-derived polymers mediated transgene expression). The final QSPR models were obtained using the partial least squares (PLS) regression approach using only specific categories of two-dimensional descriptors and subsequently evaluated considering different internationally accepted validation metrics. The proposed models are robust and non-random, demonstrating excellent predictive ability using test set compounds. We have also developed different kinds of consensus models using several validated individual models to improve the prediction quality for external set compounds. The present findings provide new insight for exploring the design of an aminoglycoside-derived polymer library based on different identified physicochemical properties as well as predict their property before their synthesis.
定量构效关系 (QSPR) 方法通常用于预测化合物的不同感兴趣的物理化学特性，目的是加速生物技术和医疗保健行业中新型化合物的设计和开发过程。在本报告中，我们采用 QSPR 方法来预测氨基糖苷衍生聚合物的不同性质（即聚合物与 DNA 的结合和氨基糖苷衍生聚合物介导的转基因表达）。最终的 QSPR 模型是使用偏最小二乘 (PLS) 回归方法获得的，仅使用了二维描述符的特定类别，随后使用不同的国际公认的验证指标进行了评估。所提出的模型是稳健的，非随机的，使用测试集化合物证明了其具有出色的预测能力。我们还使用经过验证的多个单独模型开发了不同类型的共识模型，以提高外部化合物集的预测质量。本研究结果为探索基于不同确定的物理化学性质的氨基糖苷衍生聚合物库的设计以及在合成之前预测其性质提供了新的见解。
