1
|
Xu Z, Ye Y, Liu Y, Liu H, Jiang S. Design and assembly of porous organic cages. Chem Commun (Camb) 2024; 60:2261-2282. [PMID: 38318641 DOI: 10.1039/d3cc05091b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
Porous organic cages (POCs) represent a notable category of porous materials, showing remarkable material properties due to their inherent porosity. Unlike extended frameworks which are constructed by strong covalent or coordination bonds, POCs are composed of discrete molecular units held together by weak intermolecular forces. Their structure and chemical traits can be systematically tailored, making them suitable for a range of applications including gas storage and separation, molecular separation and recognition, catalysis, and proton and ion conduction. This review provides a comprehensive overview of POCs, covering their synthesis methods, structure and properties, computational approaches, and applications, serving as a primer for those who are new to the domain. A special emphasis is placed on the growing role of computational methods, highlighting how advanced data-driven techniques and automation are increasingly aiding the rapid exploration and understanding of POCs. We conclude by addressing the prevailing challenges and future prospects in the field.
Collapse
Affiliation(s)
- Zezhao Xu
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China.
| | - Yangzhi Ye
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China.
| | - Yilan Liu
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China.
| | - Huiyu Liu
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China.
| | - Shan Jiang
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China.
| |
Collapse
|
2
|
Xie Z, Evangelopoulos X, Omar ÖH, Troisi A, Cooper AI, Chen L. Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules. Chem Sci 2024; 15:500-510. [PMID: 38179524 PMCID: PMC10762956 DOI: 10.1039/d3sc04610a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/04/2023] [Indexed: 01/06/2024] Open
Abstract
We evaluate the effectiveness of fine-tuning GPT-3 for the prediction of electronic and functional properties of organic molecules. Our findings show that fine-tuned GPT-3 can successfully identify and distinguish between chemically meaningful patterns, and discern subtle differences among them, exhibiting robust predictive performance for the prediction of molecular properties. We focus on assessing the fine-tuned models' resilience to information loss, resulting from the absence of atoms or chemical groups, and to noise that we introduce via random alterations in atomic identities. We discuss the challenges and limitations inherent to the use of GPT-3 in molecular machine-learning tasks and suggest potential directions for future research and improvements to address these issues.
Collapse
Affiliation(s)
- Zikai Xie
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool Liverpool L7 3NY UK
| | - Xenophon Evangelopoulos
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool Liverpool L7 3NY UK
| | - Ömer H Omar
- Department of Chemistry, University of Liverpool Liverpool L69 3BX UK
| | - Alessandro Troisi
- Department of Chemistry, University of Liverpool Liverpool L69 3BX UK
| | - Andrew I Cooper
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool Liverpool L7 3NY UK
| | - Linjiang Chen
- School of Chemistry, School of Computer Science, University of Birmingham Birmingham B15 2TT UK
| |
Collapse
|
3
|
Zhou J, Mroz A, Jelfs KE. Deep generative design of porous organic cages via a variational autoencoder. DIGITAL DISCOVERY 2023; 2:1925-1936. [PMID: 38054102 PMCID: PMC10695006 DOI: 10.1039/d3dd00154g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 10/26/2023] [Indexed: 12/07/2023]
Abstract
Porous organic cages (POCs) are a class of porous molecular materials characterised by their tunable, intrinsic porosity; this functional property makes them candidates for applications including guest storage and separation. Typically formed via dynamic covalent chemistry reactions from multifunctionalised molecular precursors, there is an enormous potential chemical space for POCs due to the fact they can be formed by combining two relatively small organic molecules, which themselves have an enormous chemical space. However, identifying suitable molecular precursors for POC formation is challenging, as POCs often lack shape persistence (the cage collapses upon solvent removal with loss of its cavity), thus losing a key functional property (porosity). Generative machine learning models have potential for targeted computational design of large functional molecular systems such as POCs. Here, we present a deep-learning-enabled generative model, Cage-VAE, for the targeted generation of shape-persistent POCs. We demonstrate the capacity of Cage-VAE to propose novel, shape-persistent POCs, via integration with multiple efficient sampling methods, including Bayesian optimisation and spherical linear interpolation.
Collapse
Affiliation(s)
- Jiajun Zhou
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London White City Campus, Wood Lane London W12 0BZ UK
| | - Austin Mroz
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London White City Campus, Wood Lane London W12 0BZ UK
| | - Kim E Jelfs
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London White City Campus, Wood Lane London W12 0BZ UK
| |
Collapse
|
4
|
Yokogawa D, Suda K. Interpretable Attribution Assignment for Octanol-Water Partition Coefficient. J Phys Chem B 2023; 127:7004-7010. [PMID: 37498912 DOI: 10.1021/acs.jpcb.3c02740] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
With the increasing development of machine learning models, their credibility has become an important issue. In chemistry, attribution assignment is gaining relevance when it comes to designing molecules and debugging models. However, attention has only been paid to which atoms are important in the prediction and not to whether the attribution is reasonable. In this study, we developed a graph neural network model, a highly interpretable attribution model in chemistry, and modified the integrated gradients method. The credibility of our approach was confirmed by predicting the octanol-water partition coefficient (logP) and evaluating the three metrics (accuracy, consistency, and stability) in the attribution assignment.
Collapse
Affiliation(s)
- Daisuke Yokogawa
- Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba Meguro-ku, Tokyo 153-8902, Japan
| | - Kayo Suda
- Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba Meguro-ku, Tokyo 153-8902, Japan
| |
Collapse
|
5
|
Jelfs KE. Computational modeling to assist in the discovery of supramolecular materials. Ann N Y Acad Sci 2022; 1518:106-119. [PMID: 36251351 PMCID: PMC10091946 DOI: 10.1111/nyas.14913] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Computational modeling is increasingly used to assist in the discovery of supramolecular materials. Supramolecular materials are typically primarily built from organic components that are self-assembled through noncovalent bonding and have potential applications, including in selective binding, sorption, molecular separations, catalysis, optoelectronics, sensing, and as molecular machines. In this review, the key areas where computational prediction can assist in the discovery of supramolecular materials, including in structure prediction, property prediction, and the prediction of how to synthesize a hypothetical material are discussed, before exploring the potential impact of artificial intelligence techniques on the field. Throughout, the importance of close integration with experimental materials discovery programs will be highlighted. A series of case studies from the author's work across some different supramolecular material classes will be discussed, before finishing with a discussion of the outlook for the field.
Collapse
Affiliation(s)
- Kim E Jelfs
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, London, UK
| |
Collapse
|
6
|
Reiser P, Neubert M, Eberhard A, Torresi L, Zhou C, Shao C, Metni H, van Hoesel C, Schopmans H, Sommer T, Friederich P. Graph neural networks for materials science and chemistry. COMMUNICATIONS MATERIALS 2022; 3:93. [PMID: 36468086 PMCID: PMC9702700 DOI: 10.1038/s43246-022-00315-6] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 11/07/2022] [Indexed: 05/14/2023]
Abstract
Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.
Collapse
Affiliation(s)
- Patrick Reiser
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Marlen Neubert
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
| | - André Eberhard
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
| | - Luca Torresi
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
| | - Chen Zhou
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
| | - Chen Shao
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- Present Address: Institute for Applied Informatics and Formal Description Systems, Karlsruhe Institute of Technology, Kaiserstr. 89, 76133 Karlsruhe, Germany
| | - Houssam Metni
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- ECPM, Université de Strasbourg, 25 Rue Becquerel, 67087 Strasbourg, France
| | - Clint van Hoesel
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- Department of Applied Physics, Eindhoven University of Technology, Groene Loper 19, 5612 AP Eindhoven, The Netherlands
| | - Henrik Schopmans
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Timo Sommer
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- Institute for Theory of Condensed Matter, Karlsruhe Institute of Technology, Wolfgang-Gaede-Str. 1, 76131 Karlsruhe, Germany
- Present Address: School of Chemistry, Trinity College Dublin, College Green, Dublin 2, Ireland
| | - Pascal Friederich
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| |
Collapse
|