Machine learning approaches for modeling the physiochemical characteristics of polycyclic aromatic hydrocarbons.

Supervised machine learning methods like random forests and extreme gradient boosting plays an important role in drug development for predicting bioactivity and resolving structure-activity correlations. These approaches use topological descriptors in the study of polycyclic aromatic hydrocarbons that represent molecular structural characteristics to enhance the prediction capacity of quantitative structure-property relationships (QSPR). The objective is to identify the physoichemical properties such as density, boiling point, flash point, enthalpy, polarizability, surface tension, molar volume, molecular weight and complexity that significantly impact physicochemical attributes. The combination of machine learning and QSPR also demonstrates the potential of computational techniques in drug development. Then effective algorithms are constructed to express the link between the eccentricity-based topological indices and the physicochemical characteristics of each of the polycyclic aromatic hydrocarbons, which grows our understanding of their behavior and paves the way for future development of environmental forecasting techniques and toxicological evaluations of polycyclic aromatic hydrocarbons.