This concept highlights the use of in silico tools—AI/deep learning, molecular docking, and molecular dynamics simulations—in cosmetics research to optimize formulation design. These predictive methods enhance R&D by streamlining experiments from molecules to full products, supporting biological research, reducing costs, and saving time, making them essential alongside experimental approaches in modern formulation development.

The vast field of cosmetics is mainly explored through experimental methods, while computational tools find broader application in structuralbiology. The world of formulations remains relatively untouched or nondisclosed due to commercial interests. This work aims to better understand the applications of in silico methods in cosmetics research, at the molecular level, and how the recent work is advancing the state of the art. The integration of AI, molecular docking, and molecular dynamics simulations in cosmetics research enables the efficient discovery and optimization of ingredients, leading to innovative and effective cosmetic products. Importantly, the establishment of predictive computational protocols suitable for different cosmetic applications optimizes experimental processes and simplify testing, being economically important for the industry.