Generating biodegradable molecular composites with MolGPT : A transformer based approach

Abstract

This work presents the development of biodegradable polymer composites using the MolGPT generative transformer model. MolGPT was trained on the GuacaMol dataset and fine-tuned on the COCONUT datasets to produce valid, unique, and novel molecules. The model achieved 98.7%, 96.4%, and 94.1% in validity, uniqueness, and novelty, respectively. confirming its capability to generate chemically diverse structures. A Random Forest classifier trained on a QSAR biodegradation dataset was used to classify candidates as readily or non-readily biodegradable. Readily biodegradable molecules were selected for further evaluation and validation. AutoDock Vina was employed to dock these candidates onto a polyethylene (PE) fragment, with the lowest-energy mode subjected to DFT calculations at the B3LYP/6-31G(d) level. The docked PE–biodegradable complex exhibited HOMO–LUMO gaps of 2.1 eV, together with a binding energy of –17.6 kcal/mol. These results demonstrate that MolGPT can generate novel biodegradable candidates and that their interactions with polyethylene enhance electronic reactivity, providing a foundation for understanding how biodegradable molecules can promote polymer degradation and a basis for future laboratory validation and material design.