QSPR modelling for investigation of different properties of aminoglycoside-derived polymers using 2D descriptors.

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.