Background: 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. Methods: 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. Results: Following ADMET and drug-likeness filtering, Lead Candidates 15 and 16 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. Conclusions: The results provide a validated structural hypothesis for dual-target inhibition. The identified leads, 15 and 16, demonstrate strong predictive potential and are prioritized for chemical synthesis and in vitro biological evaluation.

Alzheimer’s disease (AD) represents an escalating global neuropharmacological crisis, with prevalence in high-growth demographic regions such as India projected to exceed 14 million by 2040. This study addresses the urgent need for high-potency, dual-site acetylcholinesterase (AChE) inhibitors through an integrated computational pipeline. We address the failure of mono-target paradigms by designing scaffolds capable of simultaneously anchoring the Catalytic Active Site (CAS) and the Peripheral Anionic Site (PAS). A robust GA-MLR QSAR model was developed from 115 quinoline analogs using 11,135 descriptors. Lead candidates were prioritized via cavity directed molecular docking (7XN1) and 100 ns molecular dynamics (MD) simulations. The five-descriptor model (R² = 0.7569, $Q_{LOO}^2$ = 0.7244) was validated by an external set of 8 experimental compounds ($R_{ext}^2$ = 0.8620). Lead Compound 19 emerged as a superior candidate (ΔG = −11.1 kcal/mol), exhibiting a stable MD trajectory (PL-RMSD ≈ 2.4 Å) and preserving essential Gly121-His447 catalytic anti-correlations. This study provides a statistically validated scaffold and computational mechanistic foundation for future in vitro experimental validation, advancing the high throughput screening of neuroprotective agents on a global scale.

The human metapneumovirus (HMPV) Fusion (F) glycoprotein is a high-priority target for “fusion-locking” agents that stabilize its metastable prefusion state. While monomeric catechins like EGCG are known antivirals, the molecular basis for the superior activity of structurally complex dimeric catechins remains poorly understood. We employed an advanced biophysical workflow, integrating 100 ns all-atom molecular dynamics (MD), free energy landscape (FEL) analysis, and MM/GBSA thermodynamic integration to decode the Structure–Dynamics Relationship (SDR) of 210 Camellia sinensis (Green tea) phytochemicals. The results reveal a “Galloylation-Driven Anchoring” mechanism: the galloyl moiety of prodelphinidin A2 3′-gallate provides critical electrostatic complementarity to the Asp325-Asp336 acidic ridge. FEL analysis quantitatively demonstrates that this anchoring leads to pronounced stabilization of the F protein in a deep, kinetically favored global minimum (ΔG = 9.357 kJ/mol), effectively raising the energy barrier for the fusogenic conformational shift. This study provides a comparative and mechanistically informed computational proof-of-concept for the use of dimeric natural scaffolds as precision fusion-locking agents, offering a roadmap for experimental biophysical validation. In this workflow, molecular docking was employed exclusively as a qualitative structure-based filtering step, while all quantitative conclusions regarding stabilization and binding energetics were derived from post-docking MD, FEL, and MM/GBSA analyses.

Background/Objectives: The EGFR T790M gatekeeper mutation drives resistance in non-small cell lung cancer by sterically hindering inhibitors and restoring ATP affinity. As emerging C797S mutations render covalent inhibitors obsolete, novel non-covalent strategies are critical. This study identifies inhibitors that redefine the mutant methionine sulfur atom as a primary stabilizing anchor rather than a liability. Methods: A reinforcement learning-based generative AI framework (DrugEx) sampled 100,000 molecules, prioritized through QSAR-based classification (mean ROC-AUC: 0.91 ± 0.01) and physicochemical filtering. Four lead candidates underwent 200 ns all-atom molecular dynamics (MD) simulations. Mechanistic stability and energetic convergence were quantified via Free Energy Landscape (FEL) analysis, post-simulation Ramachandran validation, and ensemble-averaged Molecular Mechanics Generalized Born Surface Area (MM-GBSA) binding free energy calculations. Results: Candidate 106 demonstrated high mutation tolerance by redistributing interactions toward the Met790 sulfur atom, contrasting with reference scaffolds 14 and 88 which suffered significant affinity loss. MD analysis revealed that potency is fundamentally dictated by successful recruitment of the thioether environment, locking the complex within a narrow, well-defined thermodynamic basin. Candidate 106 maintained stable binding (−11.0 kcal/mol by docking) corroborated by a robust MM-GBSA ΔGbind of −50.51 kcal/mol, primarily driven by persistent π-sulfur contacts (85% occupancy). Ramachandran analysis confirmed over 90% favored region occupancy, ensuring interactions occur without non-physical protein strain. Conclusions: Successful T790M/C797S resistance bypass is achievable by exploiting the gatekeeper methionine’s electronic environment. This mutation-aware blueprint provides a non-covalent strategy that avoids the metabolic and resistance-prone liabilities of covalent warheads.