Computational ADMET, Drug-Likeness, QSPR and Molecular Docking Analysis of Phytochemicals from Avicennia marina

The growing demand for novel therapeutics with multi-target efficacy and minimal toxicity has accelerated the exploration of plant-derived bioactive compounds using computational approaches. Avicennia marina, a mangrove species known for its traditional medicinal use, was investigated in this study for its pharmacological potential through a comprehensive in silico approach. Phytochemicals present in the hydroalcoholic leaf extract were subjected to pharmacokinetic (ADME), drug-likeness, toxicity, Quantitative Structure–Property Relationship and molecular docking analyses to evaluate their suitability as drug candidates. ADMET profiles were predicted using the pkCSM server, while toxicity parameters, including LD₅₀, organ toxicity, and cytochrome P450 inhibition, were evaluated via the ProTox-III platform. Drug-likeness was assessed according to Lipinski’s Rule of Five using Chemsketch 2019.2, indicating that most compounds complied with the criteria for oral bioavailability. A Quantitative Structure–Property Relationship (QSPR) study was performed using a wide array of 2D and 3D molecular descriptors, establishing correlations between physicochemical properties and molecular structures with the aid of SPSS software. Molecular docking was conducted using AutoDock Vina against selected protein targets—DNA gyrase, sterol 14-alpha demethylase (CYP51), main protease (Mpro), and catalase—to evaluate binding affinity and inhibitory potential. The study revealed that several phytochemicals, including 4-(4-Hydroxyphenyl)-2-butanone, O-[2-galloyl-6-p-coumaroylglucoside], Catechin 3',5-diglucoside, and Paromamine, exhibited favorable ADMET profiles, strong binding affinities, and significant drug-likeness. These findings highlight the therapeutic potential of Avicennia marina phytoconstituents as promising leads for antibacterial, antifungal, antiviral (anti-COVID-19), and antioxidant drug development, warranting further experimental validation.