Determination of Physicochemical Descriptors of Some Penicillin Derivatives Based on Density Function Theory DFT: A QSAR Modelling Approach

Abstract

In this study, a series of penicillin derivatives were systematically analyzed using quantitative structure–activity relationship (QSAR) methodologies to establish predictive models correlating molecular structure with biological activity. A diverse set of physicochemical descriptors was employed, including optimization energy, partition coefficient (log P), chemical hardness, total polarizability, global electrophilicity index, ionization energy, and electron affinity. Molecular geometries were fully optimized, and electronic properties were computed using Density Functional Theory (DFT) as implemented in the GUSSAIN software suite. Multiple linear regression (MLR) analysis was applied to develop a statistically robust QSAR model, yielding high regression coefficients and demonstrating strong predictive reliability. The model provides a theoretical framework for estimating the biological activity of newly synthesized penicillin analogues, facilitating rational drug design and accelerating the identification of potent antimicrobial agents.