Deep Learning for Green Chemistry: An AI-Enabled Pathway for Biodegradability Prediction and Organic Material Discovery

Abstract

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 artificial 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 simplified molecular-input line-entry system (SMILES) notation and molecular images to develop an effective 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.