Exploring Simple, Interpretable, and Predictive QSPR Model of Fullerene C60 Solubility in Organic Solvents

Abstract

Buckminsterfullerene (C60) and its derivatives have currently been used as promising nanomaterial for diagnostic and therapeutic agents. They are applied in pharmaceutical industry due to their nanostructure characteristics, stability and hydrophobic character. Due to its sparingly soluble nature, the solubility of C60 has been of enormous attention among carbon nanostructure investigators owing to its fundamental importance and practical interest in nanotechnology and medical industry. In order to study the diverse role of C60 and its derivatives the dependence of fullerene's solubility on molecular structure of the solvent must be understood. Current study was dedicated to the exploration of the solubility of fullerene C60 in 156 organic solvents using simple, interpretable and predictive 1D and 2D descriptors employing quantitative structure-property relationship (QSPR) technique. The authors employed genetic algorithm followed by multiple linear regression analysis (GA-MLR) to generate the correlation models. The best performance is accomplished by the four-variable MLR model with internal and external prediction coefficient of Q2 = 0.86 and R2pred = 0.89. The study identified vital properties and structural fragments, particularly valuable for guiding future synthetic as well as prediction efforts. The model generated with the highest number of organic solvents to date with simple descriptors can be reproduced in no time to predict the solubility of C60 in any new or existing organic solvents. This approach can be used as an efficient predictor for fullerenes' solubility in various organic solvents.