The emergence of sophisticated computational tools, including in silico modeling, molecular docking, and pharmacokinetic simulations, has transformed approaches to drug discovery and delivery system design [1]. These advanced technologies enable precise prediction of molecular behaviors and interactions, accelerating the development of targeted and efficient drug delivery platforms [2]. Among such platforms, pkCSM and SwissDock have shown strong capabilities in predicting absorption, distribution, metabolism, and excretion (ADME) properties and elucidating drug–target interactions [3], paving the way for next-generation therapeutic strategies [4].

Resveratrol is a naturally occurring polyphenol found in grapes, berries, peanuts, and red wine, with pharmacological activities that include antioxidant, anti-inflammatory, antimicrobial, and anticancer effects [5]. These benefits are mediated through free radical scavenging, modulation of signaling cascades, and regulation of inflammatory and metabolic pathways [6]. Its therapeutic relevance spans multiple disease contexts, from neurodegenerative disorders [7] to cancer [8] and cardiovascular disease [9]. However, its clinical application is severely limited by poor oral bioavailability (<1 %), stemming from rapid gastrointestinal degradation, extensive hepatic first-pass metabolism, and low aqueous solubility [[10], [11], [12]].

Liposomal encapsulation offers a biomimetic strategy to address these pharmacokinetic limitations. Liposomes possess a phospholipid bilayer structure that can encapsulate both hydrophilic and hydrophobic agents, enhancing stability, protecting cargo from enzymatic degradation, and improving bioavailability [13,14]. They also enable controlled release and targeted delivery [15], with prior studies reporting improved antioxidant and anti-inflammatory effects of liposomal resveratrol compared to its free form [[16], [17], [18]].

Despite these advantages, critical aspects of liposomal resveratrol systems such as their antimicrobial potential, long-term stability, and release kinetics remain underexplored. Addressing these gaps, the present study integrates computational predictions with experimental formulation and characterization to design an optimized liposomal delivery system for resveratrol. By leveraging pkCSM, SwissTargetPrediction, and SwissDock, we aim to rationally guide liposome formulation parameters and evaluate their physicochemical stability, release behavior, and biological activity. This integrated approach seeks to provide both mechanistic insight and a practical delivery strategy for enhancing resveratrol's therapeutic potential, particularly in oxidative stress-related and infectious disease contexts.