Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges

Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been...

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Bibliographic Details
Published in:Polymers Vol. 12; no. 1; p. 163
Main Authors: Chen, Guang, Shen, Zhiqiang, Iyer, Akshay, Ghumman, Umar Farooq, Tang, Shan, Bi, Jinbo, Chen, Wei, Li, Ying
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 08.01.2020
MDPI
Subjects:
Artificial intelligence
Biomedical materials
Case studies
Design
Genomes
Informatics
Machine learning
Machining
Market entry
Material properties
Materials science
Methods
Molecular structure
Organic chemistry
Polymers
Review
Source code
organic molecules
data-driven algorithm
de novo materials design
materials database
polymers
machine learning
ISSN:2073-4360, 2073-4360
Online Access:Get full text
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Summary:Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been successfully applied to discover many important materials, these methods are facing significant challenges due to the tremendous demand of new materials and vast design space of organic molecules and polymers. Accelerated and inverse materials design is an ideal solution to these challenges. With advancements in high-throughput computation, artificial intelligence (especially machining learning, ML), and the growth of materials databases, ML-assisted materials design is emerging as a promising tool to flourish breakthroughs in many areas of materials science and engineering. To date, using ML-assisted approaches, the quantitative structure property/activity relation for material property prediction can be established more accurately and efficiently. In addition, materials design can be revolutionized and accelerated much faster than ever, through ML-enabled molecular generation and inverse molecular design. In this perspective, we review the recent progresses in ML-guided design of organic molecules and polymers, highlight several successful examples, and examine future opportunities in biomedical, chemical, and materials science fields. We further discuss the relevant challenges to solve in order to fully realize the potential of ML-assisted materials design for organic molecules and polymers. In particular, this study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications. Last but not least, it draws insights into the current limitations of ML-guided design of organic molecules and polymers. We anticipate that ML-assisted materials design for organic molecules and polymers will be the driving force in the near future, to meet the tremendous demand of new materials with tailored properties in different fields.
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ISSN:2073-4360
2073-4360
DOI:10.3390/polym12010163