Toward Quantum-Informed Atom Pairs

In the following research, a new modification of traditional
atom
pairs is studied. The atom pairs are enriched with values originating
from quantum chemistry calculations. A random forest machine learning
algorithm is applied to model 10 different properties and biological
activities based on different molecular representations, and it is
evaluated via repeated cross-validation. The predictive power of modified
atom pairs, quantum atom pairs, are compared to the predictive powers
of traditional molecular representations known and widely applied
in cheminformatics. The root mean squared error (RMSE), R2, area under the receiver operating characteristic curve
(AUC) and balanced accuracy were used to evaluate the predictive power
of the applied molecular representations. Research has shown that
while performing regression tasks, quantum atom pairs provide better
fits to the data than do their precursors.