Accelerating Biopolymer Discovery with Machine Learning and High-Throughput Molecular Dynamics

How Nextmol modeled 546 biopolymers computationally to predict glass transition temperature with AI and accelerate sustainable cosmetic formulation.

Learning Points

• How a hybrid computational approach, combining high-throughput Molecular Dynamics simulations with machine learning, encompasses the combinatorial complexity of biopolymer design.
  How to build a validated and reproducible workflow and dataset of 2,184 molecular dynamics simulations of 546 biopolymers.
  How to train and validate a machine learning model using molecular descriptors, and how to deploy it as a web tool for instant glass transition temperature prediction by lab scientists.
  The practical implications for sustainable cosmetic formulation: how this approach enables formulators to identify bio-based alternatives to synthetic polymers with targeted mechanical properties, faster and at a fraction of the experimental cost.