Hybrid in silico screening platform for predicting compound interactions and ingredient functionality
A hybrid modeling platform combining molecular dynamics and environment dynamics to predict ingredient functionality in complex cosmetic and detergent environments. It creates digital twins of skin, hair, and textiles and integrates MD and ED to simulate physical interactions, diffusion into hair, and chemical interactions. The approach aims to improve prediction accuracy and robustness across formulation and scenario testing.
Overview
The in silico screening modeling platform offers a solution for predicting the interactions and functionalities of ingredients in complex environments such as cosmetics and laundry detergents. By creating digital twins of skin, hair, and textiles, the platform combines molecular dynamics (MD) with environment dynamics (ED) modeling to deliver predictions and simulations.
Technical specifications
The platform is composed of three main building blocks:
- Molecular Dynamics (MD) Module: Simulates molecular interactions by calculating the dynamics and spatial motions of molecules based on their potential and force fields.
- Environment Dynamics (ED) Module: Models the macroscopic dynamics of environments like skin and hair, incorporating factors such as peeling, microbiota, and mechanical constraints.
- Numerical Simulator Integration: Integrates MD and ED layers to predict physical interactions, diffusion into hair, and chemical interactions. Additional modules, such as AI trained on proprietary datasets, can be incorporated to enhance prediction accuracy.
Technology readiness level
The modeling platform is at Technology Readiness Level 9, indicating it is fully validated and ready for commercial application. Experimental datasets and various scenarios, such as shampoo formulations and heat treatments, have been tested to ensure robustness and accuracy.
About iMEAN
iMEAN is a deeptech company specializing in computational modeling and predictive biology. The company reconstructs digital organisms—mathematical representations of molecular networks at the genome scale—to simulate complex biological systems. Using an in-house platform that combines proprietary databases, algorithms, and expert curation, iMEAN generates high-quality predictive models from DNA sequences and omics datasets. These tools help researchers and biotechnology companies extract actionable biological insights, identify metabolic targets, and optimize bioprocesses across agriculture, food, environmental, and health sectors.
By providing these in silico services, iMEAN enables customers to accelerate R&D and improve the efficiency of their biological projects. The company has supported industrial and academic partners by resolving bottlenecks and delivering models that have reached industrial scale. Notable collaborations include work in sustainable crop protection and participation in the Ferments du Futur consortium to drive innovation in fermented foods and biopreservation. With a team of scientists focused on systems biology and biostatistics, iMEAN supports the transition toward more sustainable industrial practices through data-driven prediction and analysis.
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