An Integrated QSAR-MD-DCCM Pipeline: A Predictive Computational Platform for the Rational Design and Dynamic Functional Validation of Dual-Target Directed Ligands

<b>Background:</b> The development of Multi-Target-Directed Ligands (MTDLs) has emerged as a significant strategy for addressing complex, overlapping pathologies such as cancer and Alzheimer's disease (AD). This study aims to provide a robust computational framework for the design of dual-target inhibitors. <b>Methods:</b> This study presents an integrated and rigorous computational pipeline combining Quantitative Structure-Activity Relationship (QSAR) modeling, Molecular Docking, and Molecular Dynamics (MD) simulations with Dynamic Cross-Correlation Matrix (DCCM) analysis. Using a dataset of 57 known tubulin inhibitors, two high-performing QSAR models were developed to guide the rational design of 16 novel trimethoxyphenyl-based analogues. <b>Results:</b> Following ADMET and drug-likeness filtering, Lead Candidates <b>15</b> and <b>16</b> were identified. Quantitative activity predictions confirmed their enhanced potency thresholds, which were subsequently validated through static docking against β-tubulin (PDB: 4O2B) and Acetylcholinesterase (PDB: 1EVE). In total, 100 ns MD simulations and MM-GBSA calculations demonstrated superior binding stability and energetically favorable profiles for both targets, while DCCM analysis confirmed the functional synchrony of the protein-ligand complexes. <b>Conclusions:</b> The results provide a validated structural hypothesis for dual-target inhibition. The identified leads, <b>15</b> and <b>16</b>, demonstrate strong predictive potential and are prioritized for chemical synthesis and in vitro biological evaluation.