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- Conflict of Interest
- In relation to this article, we declare that there is no conflict of interest.
- Publication history
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Received February 29, 2024
Accepted May 29, 2024
- This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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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 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.