<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kunde, Pushkar D.</style></author><author><style face="normal" font="default" size="100%">Ramkumar, Sudha</style></author><author><style face="normal" font="default" size="100%">Kamble, Sanjay P.</style></author><author><style face="normal" font="default" size="100%">RaviKumar, Ameeta</style></author><author><style face="normal" font="default" size="100%">Kulkarni, Bhaskar D.</style></author><author><style face="normal" font="default" size="100%">Kumar, V. Ravi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">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</style></title><secondary-title><style face="normal" font="default" size="100%">Chemical Biology &amp; Drug Design</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">drug discovery</style></keyword><keyword><style  face="normal" font="default" size="100%">electron affinity</style></keyword><keyword><style  face="normal" font="default" size="100%">Electronegativity</style></keyword><keyword><style  face="normal" font="default" size="100%">molecular field descriptors</style></keyword><keyword><style  face="normal" font="default" size="100%">partial least squares</style></keyword><keyword><style  face="normal" font="default" size="100%">QSAR</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">AUG</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">98</style></volume><pages><style face="normal" font="default" size="100%">258-269</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">2.817</style></custom4></record></records></xml>