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A Mohamed W, Chakraborty J, Bourda L, Lavendomme R, Liu C, Morent R, De Geyter N, Van Hecke K, Kaczmarek AM, Van Der Voort P. Engineering Porosity and Functionality in a Robust Twofold Interpenetrated Bismuth-Based MOF: Toward a Porous, Stable, and Photoactive Material. J Am Chem Soc 2024; 146:13113-13125. [PMID: 38700843 DOI: 10.1021/jacs.3c14739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Defect engineering in metal-organic frameworks (MOFs) has gained worldwide research traction, as it offers tools to tune the properties of MOFs. Herein, we report a novel 2-fold interpenetrated Bi-based MOF made of a tritopic flexible organic linker, followed by missing-linker defect engineering. This procedure creates a gradually augmented micro- and mesoporosity in the parent (originally nonporous) network. The resulting MOFs can tolerate a remarkable extent of linker vacancy (with absence of up to 60% of linkers per Bi node) created by altering the crystal-growth rate as a function of synthesis temperature and duration. Owing to the enhanced porosity and availability of the uncoordinated Lewis acidic Bi sites, the defect-engineered MOFs manifested improved surface areas, augmented CO2 and water vapor uptake, and catalytic activity. Parallel to this, the impact of defect engineering on the optoelectronic properties of these MOFs has also been studied, offering avenues for new applications.
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Affiliation(s)
- Wafaa A Mohamed
- Center for Ordered Materials, Organometallics and Catalysis (COMOC), Department of Chemistry, Ghent University, Krijgslaan 281-S3, Ghent 9000, Belgium
- Department of Chemistry, Faculty of Science, South Valley University, Qena 83523, Egypt
| | - Jeet Chakraborty
- Center for Ordered Materials, Organometallics and Catalysis (COMOC), Department of Chemistry, Ghent University, Krijgslaan 281-S3, Ghent 9000, Belgium
| | - Laurens Bourda
- Center for Ordered Materials, Organometallics and Catalysis (COMOC), Department of Chemistry, Ghent University, Krijgslaan 281-S3, Ghent 9000, Belgium
- XStruct, Department of Chemistry, Ghent University, Krijgslaan 281-S3, Ghent 9000, Belgium
| | - Roy Lavendomme
- Center for Ordered Materials, Organometallics and Catalysis (COMOC), Department of Chemistry, Ghent University, Krijgslaan 281-S3, Ghent 9000, Belgium
- Laboratoire de Chimie Organique (LCO), Université libre de Bruxelles (ULB), Avenue F. D. Roosevelt 50, CP160/06, Brussels B-1050, Belgium
| | - Chunhui Liu
- Center for Ordered Materials, Organometallics and Catalysis (COMOC), Department of Chemistry, Ghent University, Krijgslaan 281-S3, Ghent 9000, Belgium
- NanoSensing, Department of Chemistry, Ghent University, Krijgslaan 281-S3, Ghent 9000, Belgium
| | - Rino Morent
- RUPT-Research Unit Plasma Technology, Department of Applied Physics, Ghent University, Sint-Pietersnieuwstraat 41-B4, Ghent 9000, Belgium
| | - Nathalie De Geyter
- RUPT-Research Unit Plasma Technology, Department of Applied Physics, Ghent University, Sint-Pietersnieuwstraat 41-B4, Ghent 9000, Belgium
| | - Kristof Van Hecke
- XStruct, Department of Chemistry, Ghent University, Krijgslaan 281-S3, Ghent 9000, Belgium
| | - Anna M Kaczmarek
- NanoSensing, Department of Chemistry, Ghent University, Krijgslaan 281-S3, Ghent 9000, Belgium
| | - Pascal Van Der Voort
- Center for Ordered Materials, Organometallics and Catalysis (COMOC), Department of Chemistry, Ghent University, Krijgslaan 281-S3, Ghent 9000, Belgium
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Wang Y, He L, Wang M, Yuan J, Wu S, Li X, Lin T, Huang Z, Li A, Yang Y, Liu X, He Y. The drug loading capacity prediction and cytotoxicity analysis of metal-organic frameworks using stacking algorithms of machine learning. Int J Pharm 2024; 656:124128. [PMID: 38621612 DOI: 10.1016/j.ijpharm.2024.124128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/24/2024] [Accepted: 04/13/2024] [Indexed: 04/17/2024]
Abstract
Metal-organic frameworks (MOFs) have shown excellent performance in the field of drug delivery. Despite the synthesis of a vast array of MOFs exceeding 100,000 varieties, certain formulations have exhibited suboptimal performance characteristics. Therefore, there is a pressing need to enhance their efficacy by identifying MOFs with superior drug loading capacities and minimal cytotoxicity, which can be achieved through machine learning (ML). In this study, a stacking regression model was developed to predict drug loading capacity and cytotoxicity of MOFs using datasets compiled from various literature sources. The model exhibited exceptional predictive capabilities, achieving R2 values of 0.907 for drug loading capacity and 0.856 for cytotoxicity. Furthermore, various model interpretation methods including partial dependence plots, individual conditional expectation, Shapley additive explanation, decision tree, random forest, CatBoost Regressor, and light gradient-boosting machine were employed for feature importance analysis. The results revealed that specific metal atoms such as Zn, Cr, Fe, Zr, and Cu significantly influenced the drug loading capacity and cytotoxicity of MOFs. Through model validation encompassing experimental validation and computational verification, the reliability of the model was thoroughly established. In general, it is a good practice to use ML methods for predicting drug loading capacity and cytotoxicity analysis of MOFs, guiding the development of future property prediction methods for MOFs.
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Affiliation(s)
- Yang Wang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Liqiang He
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Meijing Wang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Jiongpeng Yuan
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Siwei Wu
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Xiaojing Li
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Tong Lin
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Zihui Huang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Andi Li
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Yuhang Yang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China
| | - Xujie Liu
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China.
| | - Yan He
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, PR China.
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Li Z, Huang R, Xia M, Patterson TA, Hong H. Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery. Biomolecules 2024; 14:72. [PMID: 38254672 PMCID: PMC10813698 DOI: 10.3390/biom14010072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/26/2023] [Accepted: 12/28/2023] [Indexed: 01/24/2024] Open
Abstract
Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure-activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure-activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein-ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.
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Affiliation(s)
- Zoe Li
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA; (Z.L.); (T.A.P.)
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD 20892, USA; (R.H.); (M.X.)
| | - Menghang Xia
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD 20892, USA; (R.H.); (M.X.)
| | - Tucker A. Patterson
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA; (Z.L.); (T.A.P.)
| | - Huixiao Hong
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA; (Z.L.); (T.A.P.)
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Khan MKH, Guo W, Liu J, Dong F, Li Z, Patterson TA, Hong H. Machine learning and deep learning for brain tumor MRI image segmentation. Exp Biol Med (Maywood) 2023; 248:1974-1992. [PMID: 38102956 PMCID: PMC10798183 DOI: 10.1177/15353702231214259] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2023] Open
Abstract
Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic resonance imaging (MRI) is a commonly used imaging technique for capturing brain images. Both machine learning and deep learning techniques are popular in analyzing MRI images. This article reviews some commonly used machine learning and deep learning techniques for brain tumor MRI image segmentation. The limitations and advantages of the reviewed machine learning and deep learning methods are discussed. Even though each of these methods has a well-established status in their individual domains, the combination of two or more techniques is currently an emerging trend.
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Affiliation(s)
- Md Kamrul Hasan Khan
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| | - Wenjing Guo
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| | - Jie Liu
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| | - Fan Dong
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| | - Zoe Li
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| | - Tucker A Patterson
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
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Singh SK, Sose AT, Wang F, Bejagam KK, Deshmukh SA. Data Driven Discovery of MOFs for Hydrogen Gas Adsorption. J Chem Theory Comput 2023; 19:6686-6703. [PMID: 37756641 DOI: 10.1021/acs.jctc.3c00081] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Hydrogen gas (H2) is a clean and renewable energy source, but the lack of efficient and cost-effective storage materials is a challenge to its widespread use. Metal-organic frameworks (MOFs), a class of porous materials, have been extensively studied for H2 storage due to their tunable structural and chemical features. However, the large design space offered by MOFs makes it challenging to select or design appropriate MOFs with a high H2 storage capacity. To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H2 storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H2 uptake. This automated, data driven framework adds appropriate functional groups to IRMOF-10 to improve its H2 adsorption capacity. A detailed analysis of the top selected MOFs, their adsorption isotherms, and MOF design rules to enhance H2 adsorption are presented. We found a functionalized IRMOF-10 with an enhanced H2 adsorption increased by ∼6 times compared to that of pure IRMOF-10 at 1 bar and 77 K. Furthermore, this study also utilizes machine learning and deep learning techniques to analyze a large data set of MOF structures and properties, in order to identify the key factors that influence hydrogen adsorption. The proof-of-concept that uses a machine learning/deep learning approach to predict hydrogen adsorption based on the identified structural and chemical properties of the MOF is demonstrated.
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Affiliation(s)
- Samrendra K Singh
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Abhishek T Sose
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Fangxi Wang
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Karteek K Bejagam
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Sanket A Deshmukh
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
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Goeminne R, Vanduyfhuys L, Van Speybroeck V, Verstraelen T. DFT-Quality Adsorption Simulations in Metal-Organic Frameworks Enabled by Machine Learning Potentials. J Chem Theory Comput 2023; 19:6313-6325. [PMID: 37642314 DOI: 10.1021/acs.jctc.3c00495] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Nanoporous materials such as metal-organic frameworks (MOFs) have been extensively studied for their potential for adsorption and separation applications. In this respect, grand canonical Monte Carlo (GCMC) simulations have become a well-established tool for computational screenings of the adsorption properties of large sets of MOFs. However, their reliance on empirical force field potentials has limited the accuracy with which this tool can be applied to MOFs with challenging chemical environments such as open-metal sites. On the other hand, density-functional theory (DFT) is too computationally demanding to be routinely employed in GCMC simulations due to the excessive number of required function evaluations. Therefore, we propose in this paper a protocol for training machine learning potentials (MLPs) on a limited set of DFT intermolecular interaction energies (and forces) of CO2 in ZIF-8 and the open-metal site containing Mg-MOF-74, and use the MLPs to derive adsorption isotherms from first principles. We make use of the equivariant NequIP model which has demonstrated excellent data efficiency, and as such an error on the interaction energies below 0.2 kJ mol-1 per adsorbate in ZIF-8 was attained. Its use in GCMC simulations results in highly accurate adsorption isotherms and heats of adsorption. For Mg-MOF-74, a large dependence of the obtained results on the used dispersion correction was observed, where PBE-MBD performs the best. Lastly, to test the transferability of the MLP trained on ZIF-8, it was applied to ZIF-3, ZIF-4, and ZIF-6, which resulted in large deviations in the predicted adsorption isotherms and heats of adsorption. Only when explicitly training on data for all ZIFs, accurate adsorption properties were obtained. As the proposed methodology is widely applicable to guest adsorption in nanoporous materials, it opens up the possibility for training general-purpose MLPs to perform highly accurate investigations of guest adsorption.
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Affiliation(s)
- Ruben Goeminne
- Center for Molecular Modeling (CMM), Ghent Univeristy, Technologiepark 46, 9052 Zwijnaarde, Belgium
| | - Louis Vanduyfhuys
- Center for Molecular Modeling (CMM), Ghent Univeristy, Technologiepark 46, 9052 Zwijnaarde, Belgium
| | - Veronique Van Speybroeck
- Center for Molecular Modeling (CMM), Ghent Univeristy, Technologiepark 46, 9052 Zwijnaarde, Belgium
| | - Toon Verstraelen
- Center for Molecular Modeling (CMM), Ghent Univeristy, Technologiepark 46, 9052 Zwijnaarde, Belgium
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Huang G, Guo Y, Chen Y, Nie Z. Application of Machine Learning in Material Synthesis and Property Prediction. MATERIALS (BASEL, SWITZERLAND) 2023; 16:5977. [PMID: 37687675 PMCID: PMC10488794 DOI: 10.3390/ma16175977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/22/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023]
Abstract
Material innovation plays a very important role in technological progress and industrial development. Traditional experimental exploration and numerical simulation often require considerable time and resources. A new approach is urgently needed to accelerate the discovery and exploration of new materials. Machine learning can greatly reduce computational costs, shorten the development cycle, and improve computational accuracy. It has become one of the most promising research approaches in the process of novel material screening and material property prediction. In recent years, machine learning has been widely used in many fields of research, such as superconductivity, thermoelectrics, photovoltaics, catalysis, and high-entropy alloys. In this review, the basic principles of machine learning are briefly outlined. Several commonly used algorithms in machine learning models and their primary applications are then introduced. The research progress of machine learning in predicting material properties and guiding material synthesis is discussed. Finally, a future outlook on machine learning in the materials science field is presented.
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Affiliation(s)
| | | | | | - Zhengwei Nie
- School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China; (G.H.); (Y.G.); (Y.C.)
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