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.

Autor: Kunde PD; Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory (CSIR-NCL), Pune, India.; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India., Ramkumar S; Organic Chemistry Division, CSIR-National Chemical Laboratory (CSIR-NCL), Pune, India., Kamble SP; Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory (CSIR-NCL), Pune, India.; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India., Ravikumar A; Institute of Bioinformatics and Biotechnology (IBB), Savitribai Phule Pune University, Pune, India., Kulkarni BD; Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory (CSIR-NCL), Pune, India.; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India., Kumar VR; Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory (CSIR-NCL), Pune, India.; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.
Jazyk: angličtina
Zdroj: Chemical biology & drug design [Chem Biol Drug Des] 2021 Aug; Vol. 98 (2), pp. 258-269. Date of Electronic Publication: 2021 Jun 09.
DOI: 10.1111/cbdd.13895
Abstrakt: 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.
(© 2021 John Wiley & Sons A/S.)
Databáze: MEDLINE
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