Perpustakaan Digital ITB

Medicinal chemistry campaigns set the foundation for streamlined molecular design strategies through the development of quantitative structure?activity models. Our group’s enduring underlying interest in reaction mechanism propelled our adaption of a similar strategy to unite mechanistic interrogation and catalyst optimization by relating reaction outputs to molecular descriptors. Through collaborative opportunities, we have recently expanded these predictive statistical modeling tools to electrocatalysis and the design of redox-active organic molecules for application as electrolytes in nonaqueous redox flow batteries. Utilizing small, strategically designed data sets for a given core structure, we develop predictive statistical models that enable rapid virtual screening campaigns to identify analogues with enhanced properties. This process relates structural parameters to the output of interest, providing insight into the structural features that influence the output under study. Furthermore, the weighting of the coefficients for each parameter in the model can furnish mechanistic insight. Such a synergistic implementation of experimental and computational tools for mechanistic insight provides a means of forecasting properties of analogues without necessitating the synthesis and analysis of each molecule of interest. Through collaborative efforts, we have demonstrated the effectiveness of these tools for predicting diverse outputs such as stability, redox potential, and nonaqueous solubility.