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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Vydáno v:Polymers Ročník 12; číslo 1; s. 163
Hlavní autoři: Chen, Guang, Shen, Zhiqiang, Iyer, Akshay, Ghumman, Umar Farooq, Tang, Shan, Bi, Jinbo, Chen, Wei, Li, Ying
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 08.01.2020
MDPI
Témata:
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
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Abstract 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.
AbstractList 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.
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.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.
Author Bi, Jinbo
Chen, Wei
Ghumman, Umar Farooq
Li, Ying
Chen, Guang
Tang, Shan
Shen, Zhiqiang
Iyer, Akshay
AuthorAffiliation 1 Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA; guang.chen@uconn.edu (G.C.); zhiqiang.shen@uconn.edu (Z.S.)
4 Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA; jinbo.bi@uconn.edu
2 Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA; akshayiyer2021@u.northwestern.edu (A.I.); UmarGhumman2018@u.northwestern.edu (U.F.G.)
3 State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, and International Research Center for Computational Mechanics, Dalian University of Technology, Dalian 116023, China; shantang@dlut.edu.cn
5 Polymer Program, Institute of Materials Science, University of Connecticut, Storrs, CT 06269, USA
AuthorAffiliation_xml – name: 3 State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, and International Research Center for Computational Mechanics, Dalian University of Technology, Dalian 116023, China; shantang@dlut.edu.cn
– name: 2 Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA; akshayiyer2021@u.northwestern.edu (A.I.); UmarGhumman2018@u.northwestern.edu (U.F.G.)
– name: 1 Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA; guang.chen@uconn.edu (G.C.); zhiqiang.shen@uconn.edu (Z.S.)
– name: 4 Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA; jinbo.bi@uconn.edu
– name: 5 Polymer Program, Institute of Materials Science, University of Connecticut, Storrs, CT 06269, USA
Author_xml – sequence: 1
  givenname: Guang
  orcidid: 0000-0002-6753-6745
  surname: Chen
  fullname: Chen, Guang
– sequence: 2
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  orcidid: 0000-0003-0804-2478
  surname: Shen
  fullname: Shen, Zhiqiang
– sequence: 3
  givenname: Akshay
  surname: Iyer
  fullname: Iyer, Akshay
– sequence: 4
  givenname: Umar Farooq
  surname: Ghumman
  fullname: Ghumman, Umar Farooq
– sequence: 5
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  fullname: Tang, Shan
– sequence: 6
  givenname: Jinbo
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– sequence: 7
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  surname: Chen
  fullname: Chen, Wei
– sequence: 8
  givenname: Ying
  orcidid: 0000-0002-1487-3350
  surname: Li
  fullname: Li, Ying
BackLink https://www.ncbi.nlm.nih.gov/pubmed/31936321$$D View this record in MEDLINE/PubMed
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2020 by the authors. 2020
Copyright_xml – notice: 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Issue 1
Keywords organic molecules
data-driven algorithm
de novo materials design
materials database
polymers
machine learning
Language English
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SubjectTerms Artificial intelligence
Biomedical materials
Case studies
Design
Genomes
Informatics
Machine learning
Machining
Market entry
Material properties
Materials science
Methods
Molecular structure
Organic chemistry
Polymers
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Title Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges
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