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Varsou DD, Kolokathis PD, Antoniou M, Sidiropoulos NK, Tsoumanis A, Papadiamantis AG, Melagraki G, Lynch I, Afantitis A. In silico assessment of nanoparticle toxicity powered by the Enalos Cloud Platform: Integrating automated machine learning and synthetic data for enhanced nanosafety evaluation. Comput Struct Biotechnol J 2024; 25:47-60. [PMID: 38646468 PMCID: PMC11026727 DOI: 10.1016/j.csbj.2024.03.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/22/2024] [Accepted: 03/23/2024] [Indexed: 04/23/2024] Open
Abstract
The rapid advance of nanotechnology has led to the development and widespread application of nanomaterials, raising concerns regarding their potential adverse effects on human health and the environment. Traditional (experimental) methods for assessing the nanoparticles (NPs) safety are time-consuming, expensive, and resource-intensive, and raise ethical concerns due to their reliance on animals. To address these challenges, we propose an in silico workflow that serves as an alternative or complementary approach to conventional hazard and risk assessment strategies, which incorporates state-of-the-art computational methodologies. In this study we present an automated machine learning (autoML) scheme that employs dose-response toxicity data for silver (Ag), titanium dioxide (TiO2), and copper oxide (CuO) NPs. This model is further enriched with atomistic descriptors to capture the NPs' underlying structural properties. To overcome the issue of limited data availability, synthetic data generation techniques are used. These techniques help in broadening the dataset, thus improving the representation of different NP classes. A key aspect of this approach is a novel three-step applicability domain method (which includes the development of a local similarity approach) that enhances user confidence in the results by evaluating the prediction's reliability. We anticipate that this approach will significantly expedite the nanosafety assessment process enabling regulation to keep pace with innovation, and will provide valuable insights for the design and development of safe and sustainable NPs. The ML model developed in this study is made available to the scientific community as an easy-to-use web-service through the Enalos Cloud Platform (www.enaloscloud.novamechanics.com/sabydoma/safenanoscope/), facilitating broader access and collaborative advancements in nanosafety.
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Affiliation(s)
- Dimitra-Danai Varsou
- NovaMechanics MIKE, Piraeus 18545, Greece
- Entelos Institute, Larnaca 6059, Cyprus
| | | | | | | | - Andreas Tsoumanis
- Entelos Institute, Larnaca 6059, Cyprus
- NovaMechanics Ltd, Nicosia 1070, Cyprus
| | - Anastasios G. Papadiamantis
- Entelos Institute, Larnaca 6059, Cyprus
- NovaMechanics Ltd, Nicosia 1070, Cyprus
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Georgia Melagraki
- Division of Physical Sciences and Applications, Hellenic Military Academy, Vari 16672, Greece
| | - Iseult Lynch
- Entelos Institute, Larnaca 6059, Cyprus
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Antreas Afantitis
- NovaMechanics MIKE, Piraeus 18545, Greece
- Entelos Institute, Larnaca 6059, Cyprus
- NovaMechanics Ltd, Nicosia 1070, Cyprus
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2
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Moncho S, Serrano-Candelas E, de Julián-Ortiz JV, Gozalbes R. A review on the structural characterization of nanomaterials for nano-QSAR models. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2024; 15:854-866. [PMID: 39015425 PMCID: PMC11250003 DOI: 10.3762/bjnano.15.71] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 06/28/2024] [Indexed: 07/18/2024]
Abstract
Quantitative structure-activity relationship (QSAR) models are routinely used to predict the properties and biological activity of chemicals to direct synthetic advances, perform massive screenings, and even to register new substances according to international regulations. Currently, nanoscale QSAR (nano-QSAR) models, adapting this methodology to predict the intrinsic features of nanomaterials (NMs) and quantitatively assess their risks, are blooming. One of the challenges is the characterization of the NMs. This cannot be done with a simple SMILES representation, as for organic molecules, because their chemical structure is complex, including several layers and many inorganic materials, and their size and geometry are key features. In this review, we survey the literature for existing predictive models for NMs and discuss the variety of calculated and experimental features used to define and describe NMs. In the light of this research, we propose a classification of the descriptors including those that directly describe a component of the nanoform (core, surface, or structure) and also experimental features (related to the nanomaterial's behavior, preparation, or test conditions) that indirectly reflect its structure.
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Affiliation(s)
- Salvador Moncho
- ProtoQSAR S.L., CEEI Valencia, Avda. Benjamin Franklin 12, 46980 Paterna, Spain
| | | | - Jesús Vicente de Julián-Ortiz
- Universitat de València, Facultad de Farmacia, Departamento de Química Física, Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Avda. Vicent Andrés Estellés 0, 46100 Burjassot, Spain
| | - Rafael Gozalbes
- ProtoQSAR S.L., CEEI Valencia, Avda. Benjamin Franklin 12, 46980 Paterna, Spain
- MolDrug AI Systems S.L., Olimpia Arozena Torres 45, 46108 Valencia, Spain
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3
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Dubourg G, Pavlović Z, Bajac B, Kukkar M, Finčur N, Novaković Z, Radović M. Advancement of metal oxide nanomaterials on agri-food fronts. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 928:172048. [PMID: 38580125 DOI: 10.1016/j.scitotenv.2024.172048] [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: 11/27/2023] [Revised: 03/03/2024] [Accepted: 03/26/2024] [Indexed: 04/07/2024]
Abstract
The application of metal oxide nanomaterials (MOx NMs) in the agrifood industry offers innovative solutions that can facilitate a paradigm shift in a sector that is currently facing challenges in meeting the growing requirements for food production, while safeguarding the environment from the impacts of current agriculture practices. This review comprehensively illustrates recent advancements and applications of MOx for sustainable practices in the food and agricultural industries and environmental preservation. Relevant published data point out that MOx NMs can be tailored for specific properties, enabling advanced design concepts with improved features for various applications in the agrifood industry. Applications include nano-agrochemical formulation, control of food quality through nanosensors, and smart food packaging. Furthermore, recent research suggests MOx's vital role in addressing environmental challenges by removing toxic elements from contaminated soil and water. This mitigates the environmental effects of widespread agrichemical use and creates a more favorable environment for plant growth. The review also discusses potential barriers, particularly regarding MOx toxicity and risk evaluation. Fundamental concerns about possible adverse effects on human health and the environment must be addressed to establish an appropriate regulatory framework for nano metal oxide-based food and agricultural products.
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Affiliation(s)
- Georges Dubourg
- University of Novi Sad, Center for Sensor Technologies, Biosense Institute, Dr Zorana Đinđića 1, 21000 Novi Sad, Serbia.
| | - Zoran Pavlović
- University of Novi Sad, Center for Sensor Technologies, Biosense Institute, Dr Zorana Đinđića 1, 21000 Novi Sad, Serbia
| | - Branimir Bajac
- University of Novi Sad, Center for Sensor Technologies, Biosense Institute, Dr Zorana Đinđića 1, 21000 Novi Sad, Serbia
| | - Manil Kukkar
- University of Novi Sad, Center for Sensor Technologies, Biosense Institute, Dr Zorana Đinđića 1, 21000 Novi Sad, Serbia
| | - Nina Finčur
- University of Novi Sad Faculty of Sciences, Department of Chemistry, Biochemistry and Environmental Protection, Trg Dositeja Obradovića 3, 21000 Novi Sad, Serbia
| | - Zorica Novaković
- University of Novi Sad, Center for Sensor Technologies, Biosense Institute, Dr Zorana Đinđića 1, 21000 Novi Sad, Serbia
| | - Marko Radović
- University of Novi Sad, Center for Sensor Technologies, Biosense Institute, Dr Zorana Đinđića 1, 21000 Novi Sad, Serbia
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4
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Toropova AP, Toropov AA. The coefficient of conformism of a correlative prediction (CCCP): Building up reliable nano-QSPRs/QSARs for endpoints of nanoparticles in different experimental conditions encoded via quasi-SMILES. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172119. [PMID: 38569951 DOI: 10.1016/j.scitotenv.2024.172119] [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: 01/26/2024] [Revised: 03/12/2024] [Accepted: 03/29/2024] [Indexed: 04/05/2024]
Abstract
Simulation of the physicochemical and biochemical behavior of nanomaterials has its own specifics. However, the main goal of modeling for both traditional substances and nanomaterials is the same. This is an ecologic risk assessment. The universal indicator of toxicity is the n-octanol/water partition coefficient. Mutagenicity indicates the possibility of future undesirable environmental effects, possibly greater than toxicity. Models have been proposed for the octanol/water distribution coefficient of gold nanoparticles and the mutagenicity of silver nanoparticles. Unlike the previous studies, here the models are built using an updated scheme, which includes two improvements. Firstly, the computing involves a new criterion for prediction potential, the so-called coefficient of conformism of a correlative prediction (CCCP); secondly, the Las Vegas algorithm is used to select the potentially most promising models from a group of models obtained by the Monte Carlo algorithm. Apparently, CCCP is a measure of the predictive potential (not only correlation). This can give an advantage in developing a model in comparison to using the classic determination coefficient. Likely, CCCP can be more informative than the classical determination coefficient. The Las Vegas algorithm is able to improve the model obtained by the Monte Carlo method.
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Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
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5
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Bahl A, Halappanavar S, Wohlleben W, Nymark P, Kohonen P, Wallin H, Vogel U, Haase A. Bioinformatics and machine learning to support nanomaterial grouping. Nanotoxicology 2024; 18:373-400. [PMID: 38949108 DOI: 10.1080/17435390.2024.2368005] [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: 12/28/2023] [Revised: 05/22/2024] [Accepted: 06/11/2024] [Indexed: 07/02/2024]
Abstract
Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. Hence, efficient hazard and risk assessment strategies building on New Approach Methodologies (NAMs) become indispensable. Indeed, the design, the development and implementation of NAMs has been a major topic in a substantial number of research projects. One of the promising strategies that can help to deal with the high number of NMs variants is grouping and read-across. Based on demonstrated structural and physicochemical similarity, NMs can be grouped and assessed together. Within an established NM group, read-across may be performed to fill in data gaps for data-poor variants using existing data for NMs within the group. Establishing a group requires a sound justification, usually based on a grouping hypothesis that links specific physicochemical properties to well-defined hazard endpoints. However, for NMs these interrelationships are only beginning to be understood. The aim of this review is to demonstrate the power of bioinformatics with a specific focus on Machine Learning (ML) approaches to unravel the NM Modes-of-Action (MoA) and identify the properties that are relevant to specific hazards, in support of grouping strategies. This review emphasizes the following messages: 1) ML supports identification of the most relevant properties contributing to specific hazards; 2) ML supports analysis of large omics datasets and identification of MoA patterns in support of hypothesis formulation in grouping approaches; 3) omics approaches are useful for shifting away from consideration of single endpoints towards a more mechanistic understanding across multiple endpoints gained from one experiment; and 4) approaches from other fields of Artificial Intelligence (AI) like Natural Language Processing or image analysis may support automated extraction and interlinkage of information related to NM toxicity. Here, existing ML models for predicting NM toxicity and for analyzing omics data in support of NM grouping are reviewed. Various challenges related to building robust models in the field of nanotoxicology exist and are also discussed.
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Affiliation(s)
- Aileen Bahl
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
- Department of Biological Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
- Freie Universität Berlin, Institute of Pharmacy, Berlin, Germany
| | - Sabina Halappanavar
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Canada
| | - Wendel Wohlleben
- BASF SE, Department Analytical and Material Science and Department Experimental Toxicology and Ecology, Ludwigshafen, Germany
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Pekka Kohonen
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Håkan Wallin
- Department of Chemical and Biological Risk Factors, National Institute of Occupational Health, Oslo, Norway
- Department of Public Health, Copenhagen University, Copenhagen, Denmark
| | - Ulla Vogel
- National Research Centre for the Working Environment, Copenhagen, Denmark
| | - Andrea Haase
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
- Freie Universität Berlin, Institute of Pharmacy, Berlin, Germany
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6
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Banaye Yazdipour A, Masoorian H, Ahmadi M, Mohammadzadeh N, Ayyoubzadeh SM. Predicting the toxicity of nanoparticles using artificial intelligence tools: a systematic review. Nanotoxicology 2023; 17:62-77. [PMID: 36883698 DOI: 10.1080/17435390.2023.2186279] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Nanoparticles have been used extensively in different scientific fields. Due to the possible destructive effects of nanoparticles on the environment or the biological systems, their toxicity evaluation is a crucial phase for studying nanomaterial safety. In the meantime, experimental approaches for toxicity assessment of various nanoparticles are expensive and time-consuming. Thus, an alternative technique, such as artificial intelligence (AI), could be valuable for predicting nanoparticle toxicity. Therefore, in this review, the AI tools were investigated for the toxicity assessment of nanomaterials. To this end, a systematic search was performed on PubMed, Web of Science, and Scopus databases. Articles were included or excluded based on pre-defined inclusion and exclusion criteria, and duplicate studies were excluded. Finally, twenty-six studies were included. The majority of the studies were conducted on metal oxide and metallic nanoparticles. In addition, Random Forest (RF) and Support Vector Machine (SVM) had the most frequency in the included studies. Most of the models demonstrated acceptable performance. Overall, AI could provide a robust, fast, and low-cost tool for the evaluation of nanoparticle toxicity.
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Affiliation(s)
- Alireza Banaye Yazdipour
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.,Students' Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran
| | - Hoorie Masoorian
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahnaz Ahmadi
- Department of Pharmaceutics and Pharmaceutical Nanotechnology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Niloofar Mohammadzadeh
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Mohammad Ayyoubzadeh
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
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7
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Scott-Fordsmand JJ, Amorim MJB. Using Machine Learning to make nanomaterials sustainable. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 859:160303. [PMID: 36410486 DOI: 10.1016/j.scitotenv.2022.160303] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/06/2022] [Accepted: 11/15/2022] [Indexed: 06/16/2023]
Abstract
Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.
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Affiliation(s)
| | - Mónica J B Amorim
- Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
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8
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Li J, Wang C, Yue L, Chen F, Cao X, Wang Z. Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 243:113955. [PMID: 35961199 DOI: 10.1016/j.ecoenv.2022.113955] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 07/11/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
Given the rapid development of nanotechnology, it is crucial to understand the effects of nanoparticles on living organisms. However, it is laborious to perform toxicological tests on a case-by-case basis. Quantitative structure-activity relationship (QSAR) is an effective computational technique because it saves time, costs, and animal sacrifice. Therefore, this review presents general procedures for the construction and application of nano-QSAR models of metal-based and metal-oxide nanoparticles (MBNPs and MONPs). We also provide an overview of available databases and common algorithms. The molecular descriptors and their roles in the toxicological interpretation of MBNPs and MONPs are systematically reviewed and the future of nano-QSAR is discussed. Finally, we address the growing demand for novel nano-specific descriptors, new computational strategies to address the data shortage, in situ data for regulatory concerns, a better understanding of the physicochemical properties of NPs with bioactivity, and, most importantly, the design of nano-QSAR for real-life environmental predictions rather than laboratory simulations.
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Affiliation(s)
- Jing Li
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Chuanxi Wang
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Le Yue
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Feiran Chen
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Xuesong Cao
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Zhenyu Wang
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China.
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9
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Furxhi I. Health and environmental safety of nanomaterials: O Data, Where Art Thou? NANOIMPACT 2022; 25:100378. [PMID: 35559884 DOI: 10.1016/j.impact.2021.100378] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/15/2021] [Accepted: 12/17/2021] [Indexed: 06/15/2023]
Abstract
Nanotechnology keeps drawing attention due to the great tunable properties of nanomaterials in comparison to their bulk conventional materials. The growth of nanotechnology in combination with the digitization era has led to an increased need of safety related data. In addition to safety, new data-driven paradigms on safe and sustainable by design materials are stressing the necessity of data even more. Data is a fundamental asset to the scientific community in studying and analysing the entire life-cycle of nanomaterials. Unfortunately, data exist in a scattered fashion, in different sources and formats. To our knowledge, there is no study focusing on aspects of actual data-structure knowledge that exists in literature and databases. The purpose of this review research is to transparently and comprehensively, display to the nanoscience community the datasets readily available for machine learning purposes making it convenient and more efficient for the next users such as modellers or data curators to retrieve information. We systematically recorded the features and descriptors available in the datasets and provide synopsised information on their ranges, forms and metrics in the supplementary material.
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Affiliation(s)
- Irini Furxhi
- Transgero Limited, Cullinagh, Newcastle West, Co. Limerick, Ireland; Dept. of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93, Ireland.
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10
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Huang R, Ma C, Ma J, Huangfu X, He Q. Machine learning in natural and engineered water systems. WATER RESEARCH 2021; 205:117666. [PMID: 34560616 DOI: 10.1016/j.watres.2021.117666] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/01/2021] [Accepted: 09/11/2021] [Indexed: 06/13/2023]
Abstract
Water resources of desired quality and quantity are the foundation for human survival and sustainable development. To better protect the water environment and conserve water resources, efficient water management, purification, and transportation are of critical importance. In recent years, machine learning (ML) has exhibited its practicability, reliability, and high efficiency in numerous applications; furthermore, it has solved conventional and emerging problems in both natural and engineered water systems. For example, ML can predict various water quality indicators in situ and real-time by considering the complex interactions among water-related variables. ML approaches can also solve emerging pollution problems with proven rules or universal mechanisms summarized from the related research. Moreover, by applying image recognition technology to analyze the relationships between image information and physicochemical properties of the research object, ML can effectively identify and characterize specific contaminants. In view of the bright prospects of ML, this review comprehensively summarizes the development of ML applications in natural and engineered water systems. First, the concept and modeling steps of ML are briefly introduced, including data preparation, algorithm selection and model evaluation. In addition, comprehensive applications of ML in recent studies, including predicting water quality, mapping groundwater contaminants, classifying water resources, tracing contaminant sources, and evaluating pollutant toxicity in natural water systems, as well as modeling treatment techniques, assisting characterization analysis, purifying and distributing drinking water, and collecting and treating sewage water in engineered water systems, are summarized. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of ML algorithms for different studies, as well as prospects on the application and development of ML in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.
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Affiliation(s)
- Ruixing Huang
- Key Laboratory of Eco-environments in the Three Gorges Reservoir Region, Ministry of Education, College of Environmental and Ecology, Chongqing University, Chongqing 400044, China; State Key Laboratory of Urban Water Resource and Environment, School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China
| | - Chengxue Ma
- Key Laboratory of Eco-environments in the Three Gorges Reservoir Region, Ministry of Education, College of Environmental and Ecology, Chongqing University, Chongqing 400044, China; State Key Laboratory of Urban Water Resource and Environment, School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China
| | - Jun Ma
- State Key Laboratory of Urban Water Resource and Environment, School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China
| | - Xiaoliu Huangfu
- Key Laboratory of Eco-environments in the Three Gorges Reservoir Region, Ministry of Education, College of Environmental and Ecology, Chongqing University, Chongqing 400044, China.
| | - Qiang He
- Key Laboratory of Eco-environments in the Three Gorges Reservoir Region, Ministry of Education, College of Environmental and Ecology, Chongqing University, Chongqing 400044, China
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11
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Halder AK, Dias Soeiro Cordeiro MN. QSAR-Co-X: an open source toolkit for multitarget QSAR modelling. J Cheminform 2021; 13:29. [PMID: 33858509 PMCID: PMC8048082 DOI: 10.1186/s13321-021-00508-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Accepted: 03/31/2021] [Indexed: 12/02/2022] Open
Abstract
Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling is that models are set up based on a limited number of experimental and/or theoretical conditions. To overcome this, the so-called multitasking or multitarget QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a single model equation, thus extending and improving the reliability of this type of modelling. We have developed QSAR-Co-X, an open source python–based toolkit (available to download at https://github.com/ncordeirfcup/QSAR-Co-X) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters and graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, four case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable. ![]()
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Affiliation(s)
- Amit Kumar Halder
- LAQV@REQUIMTE/Faculty of Sciences, University of Porto, 4169-007, Porto, Portugal.
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12
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Abdelsattar AS, Dawoud A, Helal MA. Interaction of nanoparticles with biological macromolecules: a review of molecular docking studies. Nanotoxicology 2020; 15:66-95. [PMID: 33283572 DOI: 10.1080/17435390.2020.1842537] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The high frequency of using engineered nanoparticles in various medical applications entails a deep understanding of their interaction with biological macromolecules. Molecular docking simulation is now widely used to study the binding of different types of nanoparticles with proteins and nucleic acids. This helps not only in understanding the mechanism of their biological action but also in predicting any potential toxicity. In this review, the computational techniques used in studying the nanoparticles interaction with biological macromolecules are covered. Then, a comprehensive overview of the docking studies performed on various types of nanoparticles will be offered. The implication of these predicted interactions in the biological activity and/or toxicity is also discussed for each type of nanoparticles.
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Affiliation(s)
- Abdallah S Abdelsattar
- Center for X-Ray and Determination of Structure of Matter, Zewail City of Science and Technology, Giza, Egypt
| | - Alyaa Dawoud
- Faculty of Pharmacy and Biotechnology, German University in Cairo, Cairo, Egypt
| | - Mohamed A Helal
- Biomedical Sciences Program, University of Science and Technology, Zewail City of Science and Technology, Giza, Egypt.,Medicinal Chemistry Department, Faculty of Pharmacy, Suez Canal University, Ismailia, Egypt
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Motta AGC, do Amaral DF, Benvindo-Souza M, Rocha TL, de Melo e Silva D. Genotoxic and mutagenic effects of zinc oxide nanoparticles and zinc chloride on tadpoles of Lithobates catesbeianus (Anura: Ranidae). ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.enmm.2020.100356] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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