Computer aided formulation design based on molecular dynamics simulation: Detergents with fragrance

Abstract

Computer-aided formulation design is a methodology that utilizes domain knowledge and selected methods and tools suitable for computer-based applications to assist in formulation (product) design. In this paper, molecular dynamics simulation and Bayesian neural network algorithms are combined with well-known engineering models to help accelerate the development and optimization of formulation-based detergent products with a view to improve product quality and performance. In particular, the mechanism of the behavior of polymers (an active ingredient in the product) to improve the product quality in terms of the fragrance and its residence time is highlighted. Results from molecular dynamic simulation applied to study the molecular interaction mechanism show that the polymers have an attraction effect with fragrance molecules and could adsorb more to make them to stay on the surface of clothes. In addition, the polymer attenuates the diffusion of the fragrance molecules, lengthening the entire process of fragrance diffusion, which is the essence of the ability of the polymer to slow down the release of the fragrance. A Quantitative Structure-Property Relationship (QSPR) model between component proportions and fragrance diffusion is established through Bayesian Neural Network (BNN) and the product formulation is optimized based on this model. Keeping polymer and perfume ingredients unchanged, the surfactant amounts are optimized to provide improved product quality.