Design and Optimization of Multifunctional Peptide Candidates for Cosmeceutical Applications: Combining Anti-Inflammatory and Collagen-Boosting Properties

Skin aging is multifactorial and involves inflammation, oxidative stress, collagen degradation, and impaired metal ion homeostasis, which requires holistic approaches for dermal rejuvenation. Conventional cosmeceutical peptides often target singular pathways, limiting their efficacy. In this study, we present a computational strategy for designing and evaluating multifunctional peptides that can simultaneously modulate collagen remodelling, exhibit anti-inflammatory responses, and maintain extracellular matrix (ECM) stability. We focused on three important skin-associated proteins: TGF- β receptor II, Integrin α 5 β 1, and MMP-1. and generated peptide sequences specific to them through a de novo sequence generation method that utilized protein-specific profiles alongside machine learning-based property screening. Over 15,000 peptides were generated and filtered using physicochemical and synthesis-based criteria (charge, hydrophobicity, pI, solubility, and synthesis rules). A multi-parametric composite score was developed to visualize peptide favourability in a 3D physicochemical space. The top peptides from each target set were subjected to structure prediction and docking, resulting in binding affinities that ranged from -19.8 to -34.5 kcal/mol. Cross-target docking analysis demonstrated a significant potential for dual or pan-target applications, indicating the broad-spectrum utility of the cosmeceutical. This study firmly establishes a foundation for multifunctional peptide candidates that combine collagen-boosting, anti-inflammatory, and MMP-inhibitory effects. These candidates are poised to lead the way for innovative skincare formulations, paving the path for future validation and experimental evidence that will undeniably strengthen our findings.