Integrating Experimental and Computational Approaches: Rational Design and Discovery of Bioactive Sulfonamide Derivatives via QSPR Analysis and Topological Indices
Muhammad Danish, Anum Shezadi, Tehreem Liaquat, Ayesha Bibi, Manha Arshad · International Journal of Computational Materials Science and Engineering · 2025
Carboxylate esters derived from sulfonamide were synthesized and characterized using FTIR, NMR, Mass and X-ray crystallography. All [Formula: see text]the synthesized compounds were evaluated for their in vitro biological activity, including enzyme inhibition against Acetylcholine esterase (AChE) and Butyrylcholine esterase (BChE), as well as anti-microbial activity. The anti-microbial evaluation was conducted against six bacterial species, i.e., Halomonas halophila, Shigella sonnei, Bacillus subtilis, Chromohalobacter salexigens, Staphylococcus aureus, Escherichia coli, and one fungal species (Aspergillus niger). Additionally, anti-oxidant activity was measured using 2-2’-dienyl-1-picrylhydrazyl (DPPH) free radical scavenging assay. The results indicated that compound VI exhibited excellent anti-oxidant and enzyme inhibition activity against both AChE and BChE. Regarding anti-bacterial potential, compound IX showed relatively high selective activity among all compounds, while compound VII displayed fungicidal effects. This study reveals that these synthesized sulfonamide esters have potential as a therapeutic tool and warrant further investigation. This research also investigates the predictive capacity of degree-based topological indices (TIs) for assessing molecular properties relevant to drug development, specifically for tranexamic acid-based sulfonamides with potential anti-cancer applications. By utilizing graph-theoretical techniques and edge partition methods, we computed various TIs and applied linear regression to establish quantitative structure–property relationship models. The TIs showed the highest correlation with properties, indicating their effectiveness as predictive descriptors. These results underscore the utility of mathematical descriptors in forecasting biological and physicochemical properties, thereby offering a computational framework for screening and optimizing drug-like compounds.