The increasing global demand for eco-friendly products is driving innovation in sustainable chemical synthesis, particularly

the development of biodegradable substances. Herein, a novel method utilizing artifi cial intelligence (AI) to predict the

biodegradability of organic compounds is presented, overcoming the limitations of traditional prediction methods that rely

on laborious and costly density functional theory (DFT) calculations. We propose leveraging readily available molecular

formulas and structures represented by simplifi ed molecular-input line-entry system (SMILES) notation and molecular

images to develop an eff ective AI-based prediction model using state-of-the-art machine learning techniques, including

deep convolutional neural networks (CNN) and long-short term memory (LSTM) learning algorithms, capable of extracting

meaningful molecular features and spatiotemporal relationships. The model is further enhanced with reinforcement

learning (RL) to better predict and discover new biodegradable materials by rewarding the system for identifying unique

and biodegradable compounds. The combined CNN-LSTM model achieved an 87.2% prediction accuracy, outperforming

CNN- (75.4%) and LSTM-only (79.3%) models. The RL-assisted generator model produced approximately 60% valid

SMILES structures, with over 80% being unique to the training dataset, demonstrating the model’s capability to generate

novel compounds with potential for practical application in sustainable chemistry. The model was extended to develop novel

electrolytes with desired molecular weight distribution.