On the use of electronegativity and electron affinity based pseudo-molecular field descriptors in developing correlations for quantitative structure-activity relationship modeling of drug activities

For quantitative structure-activity relationship (QSAR) modeling in ligand-based drug discovery programs, pseudo-molecular field (PMF) descriptors using intrinsic atomic properties, namely, electronegativity and electron affinity are studied. In combination with partial least squares analysis and Procrustes transformation, these PMF descriptors were employed successfully to develop correlations that predict the activities of target protein inhibitors involved in various diseases (cancer, neurodegenerative disorders, HIV, and malaria). The results show that the present QSAR approach is competitive to existing QSAR models. In order to demonstrate the use of this algorithm, we present results of screening naturally occurring molecules with unknown bioactivities. The pIC(50) predictions can screen molecules that have desirable activity before assessment by docking studies.

Automated summary

In this study, PMF descriptors utilizing intrinsic atomic properties were utilized to predict the activities of target protein inhibitors, showing competitiveness to existing QSAR models. The algorithm was also demonstrated to effectively screen naturally occurring molecules with unknown bioactivities.