Discovering New Color Developers for Thermochromic Microcapsules Using a Variational Autoencoder

Abstract

Abstract In our pursuit of safer alternatives to Bisphenol A (BPA) for use as color developers in thermochromic microcapsules, our goal was to mitigate endocrine disruption without compromising the properties of BPA-based microcapsules. We began by scrutinizing the effect of 11 BPA derivatives on temperatures at which microcapsules change color (color-changing temperatures). The color-changing temperatures were determined using sigmoid fitting of the color density versus temperature plot, leading to four regression models connecting these temperatures to the color developer structures. To assess endocrine-disrupting potential and toxicity, we adopted machine learning models from the Open QSAR Application (OPERA). Concurrently, using atom-wise tokenization, we trained a variational autoencoder on SMILES data of drug-like molecules. With this approach, we have achieved a six-fold speed increase in training with 20% fewer parameters than conventional character-wise tokenization. After transfer learning with potential color developer data, this model generated new SMILES data, which were subsequently evaluated for their properties. In the end, we have obtained a compilation of SMILES predicted to be effective and safer replacements for BPA.