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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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Zhang Z, Lin J, Owens G, Chen Z. Deciphering silver nanoparticles perturbation effects and risks for soil enzymes worldwide: Insights from machine learning and soil property integration. JOURNAL OF HAZARDOUS MATERIALS 2024; 469:134052. [PMID: 38493625 DOI: 10.1016/j.jhazmat.2024.134052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 02/15/2024] [Accepted: 03/14/2024] [Indexed: 03/19/2024]
Abstract
Globally extensive research into how silver nanoparticles (AgNPs) affect enzyme activity in soils with differing properties has been limited by cost-prohibitive sampling. In this study, customized machine learning (ML) was used to extract data patterns from complex research, with a hit rate of Random Forest > Multiple Imputation by Chained Equations > Decision Tree > K-Nearest Neighbors. Results showed that soil properties played a pivotal role in determining AgNPs' effect on soil enzymes, with the order being pH > organic matter (OM) > soil texture ≈ cation exchange capacity (CEC). Notably, soil enzyme activity was more sensitive to AgNPs in acidic soil (pH < 5.5), while elevated OM content (>1.9 %) attenuated AgNPs toxicity. Compared to soil acidification, reducing soil OM content is more detrimental in exacerbating AgNPs' toxicity and it emerged that clay particles were deemed effective in curbing their toxicity. Meanwhile sand particles played a very different role, and a sandy soil sample at > 40 % of the water holding capacity (WHC), amplified the toxicity of AgNPs. Perturbation mapping of how soil texture alters enzyme activity under AgNPs exposure was generated, where soils with sand (45-65 %), silt (< 22 %), and clay (35-55 %) exhibited even higher probability of positive effects of AgNPs. The average calculation results indicate the sandy clay loam (75.6 %), clay (74.8 %), silt clay (65.8 %), and sandy clay (55.9 %) texture soil demonstrate less AgNPs inhibition effect. The results herein advance the prediction of the effect of AgNPs on soil enzymes globally and determine the soil types that are more sensitive to AgNPs worldwide.
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Affiliation(s)
- Zhenjun Zhang
- Fujian Key Laboratory of Pollution Control and Resource Reuse, College of Environmental and Resource Sciences, Fujian Normal University, Fuzhou 350117, Fujian Province, China
| | - Jiajiang Lin
- Fujian Key Laboratory of Pollution Control and Resource Reuse, College of Environmental and Resource Sciences, Fujian Normal University, Fuzhou 350117, Fujian Province, China.
| | - Gary Owens
- Environmental Contaminants Group, Future Industries Institute, University of South Australian, Mawson Lakes, SA 5095, Australia
| | - Zuliang Chen
- Fujian Key Laboratory of Pollution Control and Resource Reuse, College of Environmental and Resource Sciences, Fujian Normal University, Fuzhou 350117, Fujian Province, China.
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Gohari G, Jiang M, Manganaris GA, Zhou J, Fotopoulos V. Next generation chemical priming: with a little help from our nanocarrier friends. TRENDS IN PLANT SCIENCE 2024; 29:150-166. [PMID: 38233253 DOI: 10.1016/j.tplants.2023.11.024] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/28/2023] [Accepted: 11/30/2023] [Indexed: 01/19/2024]
Abstract
Plants are exposed to multiple threats linked to climate change which can cause critical yield losses. Therefore, designing novel crop management tools is crucial. Chemical priming has recently emerged as an effective technology for improving tolerance to stress factors. Several compounds such as phytohormones, reactive species, and synthetic chimeras have been identified as promising priming agents. Following remarkable developments in nanotechnology, several unique nanocarriers (NCs) have been engineered that can act as smart delivery systems. These provide an eco-friendly, next-generation method for chemical priming, leading to increased efficiency and reduced overall chemical usage. We review novel engineered NCs (NENCs) as vehicles for chemical agents in advanced priming strategies, and address challenges and opportunities to be met towards achieving sustainable agriculture.
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Affiliation(s)
- Gholamreza Gohari
- Department of Agricultural Sciences Biotechnology and Food Science, Cyprus University of Technology, Lemesos, Cyprus; Department of Horticulture, Faculty of Horticulture, University of Maragheh, Maragheh, Iran
| | - Meng Jiang
- Hainan Institute, Zhejiang University, Yazhou Bay Sci-Tech City, Sanya, PR China
| | - George A Manganaris
- Department of Agricultural Sciences Biotechnology and Food Science, Cyprus University of Technology, Lemesos, Cyprus
| | - Jie Zhou
- Hainan Institute, Zhejiang University, Yazhou Bay Sci-Tech City, Sanya, PR China; Department of Horticulture, Zhejiang Provincial Key Laboratory of Horticultural Plant Integrative Biology, Zhejiang University, Hangzhou, PR China
| | - Vasileios Fotopoulos
- Department of Agricultural Sciences Biotechnology and Food Science, Cyprus University of Technology, Lemesos, Cyprus.
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Huang W, Liu Y, Hu P, Ding S, Gao S, Zhang M. What influence farmers' relative poverty in China: A global analysis based on statistical and interpretable machine learning methods. Heliyon 2023; 9:e19525. [PMID: 37809468 PMCID: PMC10558733 DOI: 10.1016/j.heliyon.2023.e19525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 08/24/2023] [Accepted: 08/25/2023] [Indexed: 10/10/2023] Open
Abstract
Poverty eradication has always been a major challenge to global development and governance, which received widespread attention from each country. With the completion poverty alleviation task in 2020, relative poverty governance becomes an important issue to be solved in China urgently. Because of a large population, poor infrastructures, insufficient resources, and long-term uneven development raising the living standard of farmers in rural areas is critical to China's success in realizing moderate prosperity. Therefore, identifying the poor farmers, exploring the influence factors to relative poverty, and clarifying its effect mechanism in rural areas are significant for the subsequent poverty governance. Most of the previous studies adopted the method of apriori assuming the factor system and verifying the hypothesis. We innovatively constructed a relative poverty index system consistent with China's actual conditions, selecting all the possible variables that could affect relative poverty based on the existing literature, including individual characteristics, psychological endowment, and geographical environment, and rebuilt an experimental database. Then, through data processing and data analysis, the main factors influencing the relative poverty of farmers were systematically sorted out based on the machine learning method. Finally, 25 chosen influencing factors were discussed in detail. Research findings show that: 1) Machine learning algorithm is proved it could be well applied in relative poverty fields, especially XGBoost, which achieves 81.9% accuracy and the score of ROC_AUC reaches 0.819. 2) This study sheds light on many new research directions in applying machine learning for relative poverty research, besides, the paper offers an integral framework and beneficial reference for target identification using machine learning algorithms. 3) In addition, by utilizing the interpretable tools, the "black-box" of ML become transparent through PDP and SHAP explanation, it also reveals that machine learning models can readily handle the non-linear association relationship.
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Affiliation(s)
- Wei Huang
- School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
| | - Yinke Liu
- School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
| | - Peiqi Hu
- School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
| | - Shiyu Ding
- School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
| | - Shuhui Gao
- School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
| | - Ming Zhang
- School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
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Yu H, Tang S, Li SFY, Cheng F. Averaging Strategy for Interpretable Machine Learning on Small Datasets to Understand Element Uptake after Seed Nanotreatment. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:12760-12770. [PMID: 37594125 DOI: 10.1021/acs.est.3c01878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
Understanding plant uptake and translocation of nanomaterials is crucial for ensuring the successful and sustainable applications of seed nanotreatment. Here, we collect a dataset with 280 instances from experiments for predicting the relative metal/metalloid concentration (RMC) in maize seedlings after seed priming by various metal and metalloid oxide nanoparticles. To obtain unbiased predictions and explanations on small datasets, we present an averaging strategy and add a dimension for interpretable machine learning. The findings in post-hoc interpretations of sophisticated LightGBM models demonstrate that solubility is highly correlated with model performance. Surface area, concentration, zeta potential, and hydrodynamic diameter of nanoparticles and seedling part and relative weight of plants are dominant factors affecting RMC, and their effects and interactions are explained. Furthermore, self-interpretable models using the RuleFit algorithm are established to successfully predict RMC only based on six important features identified by post-hoc explanations. We then develop a visualization tool called RuleGrid to depict feature effects and interactions in numerous generated rules. Consistent parameter-RMC relationships are obtained by different methods. This study offers a promising interpretable data-driven approach to expand the knowledge of nanoparticle fate in plants and may profoundly contribute to the safety-by-design of nanomaterials in agricultural and environmental applications.
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Affiliation(s)
- Hengjie Yu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Shiyu Tang
- Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore
| | - Sam Fong Yau Li
- Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore
| | - Fang Cheng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
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Yu H, Luo D, Li SFY, Qu M, Liu D, He Y, Cheng F. Interpretable machine learning-accelerated seed treatment using nanomaterials for environmental stress alleviation. NANOSCALE 2023; 15:13437-13449. [PMID: 37548042 DOI: 10.1039/d3nr02322b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Crops are constantly challenged by different environmental conditions. Seed treatment using nanomaterials is a cost-effective and environmentally friendly solution for environmental stress mitigation in crop plants. Here, 56 seed nanopriming treatments are used to alleviate environmental stresses in maize. Seven selected nanopriming treatments significantly increase the stress resistance index (SRI) by 13.9% and 12.6% under salinity stress and combined heat-drought stress, respectively. Metabolomics data reveal that ZnO nanopriming treatment, with the highest SRI value, mainly regulates the pathways of amino acid metabolism, secondary metabolite synthesis, carbohydrate metabolism, and translation. Understanding the mechanism of seed nanopriming is still difficult due to the variety of nanomaterials and the complexity of interactions between nanomaterials and plants. Using the nanopriming data, we present an interpretable structure-activity relationship (ISAR) approach based on interpretable machine learning for predicting and understanding its stress mitigation effects. The post hoc and model-based interpretation approaches of machine learning are integrated to provide complementary advantages and may yield more illuminating or trustworthy results for researchers or policymakers. The concentration, size, and zeta potential of nanoparticles are identified as dominant factors for correlating root dry weight under salinity stress, and their effects and interactions are explained. Additionally, a web-based interactive tool is developed for offering prediction-level interpretation and gathering more details about a specific nanopriming treatment. This work offers a promising framework for accelerating the agricultural applications of nanomaterials and may contribute to nanosafety assessment.
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Affiliation(s)
- Hengjie Yu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Dan Luo
- Department of Biological and Environmental Engineering, Cornell University, Ithaca, New York 14853, USA
| | - Sam Fong Yau Li
- Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore
| | - Maozhen Qu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Da Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Yingchao He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Fang Cheng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
- Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
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