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. (9th June 2021)

• Record Type: Journal Article
• Authors: Kunde, Pushkar D.; Ramkumar, Sudha; Kamble, Sanjay P.; Ravikumar, Ameeta; Kulkarni, Bhaskar D.; Kumar, V. Ravi
• Is Part Of: Chemical biology & drug design. Volume 98:Number 2(2021)
• Page Start: 258
• Page End: 269
• Publication Date: 2021-06-09
• Subjects: drug discovery -- electron affinity -- electronegativity -- molecular field descriptors -- partial least squares -- QSAR; Drugs -- Design -- Periodicals; Pharmaceutical chemistry -- Periodicals; Biochemistry -- Periodicals; 615.19005
• DOI: 10.1111/cbdd.13895
• Languages: English
• ISSNs: 1747-0277

Abstract

Abstract: 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 pIC50 predictions can screen molecules that have desirable activity before assessment by docking studies. Abstract : For QSAR modelling in drug discovery programs, pseudo‐molecular field descriptors using intrinsic atomic properties, namely, electronegativity and electron affinity are studied. In combination with partial least squares and Procrustes transformation, correlations were successfully developed to predict the activity of target protein inhibitors involved in cancer, neurodegenerative disorders, HIV, and malaria. We demonstrate the algorithm by presenting results of screening naturally occurring molecules with unknown bioactivities. The pIC50 predictions can screen molecules that have desirable activity before assessment by docking studies.