Application of Molecular Descriptors and End-to-End Deep Learning in MOFs Design

Ying He; Fangchang Tan; Xiliang Yan

doi:10.7536/PC241104

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Prog Chem ›› 2025, Vol. 37 ›› Issue (8) : 1177-1187. DOI: 10.7536/PC241104

Review

Application of Molecular Descriptors and End-to-End Deep Learning in MOFs Design

Ying He ¹ ,
Fangchang Tan ² ,
Xiliang Yan ³^,^*

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Abstract

Metal-organic frameworks （MOFs） exhibit great promise in diverse applications such as gas storage，catalysis，and sensing due to their distinctive structures and physicochemical properties. However，traditional experimental approaches face challenges in quickly and efficiently designing MOFs withthe desired characteristics. In recent years，artificial intelligence （AI） techniques，particularly traditional machine learning and deep learning，have been extensively applied in materials science，yielding numerous noteworthy results. An essential requirement for successful modeling with these techniques is the ability to extract the structural features of MOFs and transform them into computer-readable formats. Therefore，we present a comprehensive review of two feature extraction approaches based on molecular descriptors and end-to-end deep learning. We summarize the fundamental concepts and principles of both methods，emphasizing their specific applications and recent advancements in MOFs design. Finally，we discuss the challenges and future directions for improving the comprehensiveness，interpretability，and reproducibility of structural feature extraction. This review aims to provide valuable insights and theoretical guidance for AI-driven MOFs design.

Contents

1 Introduction

2 Traditional machine learning and end-to-end deep learning

2.1 Basic concepts and historical development of artificial intelligence

2.2 Key steps in traditional machine learning and end-to-end deep Learning

2.3 Differences between traditional machine learning and end-to-end deep Learning

2.4 Overview of the MOF databases

3 Feature extraction based on molecular descriptors

3.1 Structural descriptors

3.2 Chemical characteristics

3.3 Thermodynamic properties

3.4 Feature selection and dimensionality reduction techniques

3.5 Effective strategies for handling missing features and noisy data

4 Application of end-to-end deep learning model to MOFs design

4.1 Convolutional neural networks

4.2 Recurrent neural networks

4.3 Graph neural networks

4.4 Generative adversarial networks

5 Conclusion and outlook

Key words

artificial intelligence / computer-aided material design / QSPR / molecular descriptors / feature extraction

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Ying He , Fangchang Tan , Xiliang Yan. Application of Molecular Descriptors and End-to-End Deep Learning in MOFs Design[J]. Progress in Chemistry. 2025, 37(8): 1177-1187 https://doi.org/10.7536/PC241104

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