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Daghighi A, Casanola-Martin GM, Iduoku K, Kusic H, González-Díaz H, Rasulev B. Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:10116-10127. [PMID: 38797941 DOI: 10.1021/acs.est.4c01017] [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: 05/29/2024]
Abstract
In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.
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Affiliation(s)
- Amirreza Daghighi
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Biomedical Engineering Program, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Gerardo M Casanola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Kweeni Iduoku
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Biomedical Engineering Program, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Hrvoje Kusic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev Trg 19, Zagreb 10000, Croatia
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa 48940, Spain
- BIOFISIKA, Basque Center for Biophysics CSIC-UPVEH, Leioa 48940, Spain
- IKERBASQUE, Basque Foundation for Science,Bilbao, Biscay 48011, Spain
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Biomedical Engineering Program, North Dakota State University, Fargo, North Dakota 58102, United States
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He S, Segura Abarrategi J, Bediaga H, Arrasate S, González-Díaz H. On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2024; 15:535-555. [PMID: 38774585 PMCID: PMC11106676 DOI: 10.3762/bjnano.15.47] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 04/23/2024] [Indexed: 05/24/2024]
Abstract
Neurodegenerative diseases are characterized by slowly progressing neuronal cell death. Conventional drug treatment strategies often fail because of poor solubility, low bioavailability, and the inability of the drugs to effectively cross the blood-brain barrier. Therefore, the development of new neurodegenerative disease drugs (NDDs) requires immediate attention. Nanoparticle (NP) systems are of increasing interest for transporting NDDs to the central nervous system. However, discovering effective nanoparticle neuronal disease drug delivery systems (N2D3Ss) is challenging because of the vast number of combinations of NP and NDD compounds, as well as the various assays involved. Artificial intelligence/machine learning (AI/ML) algorithms have the potential to accelerate this process by predicting the most promising NDD and NP candidates for assaying. Nevertheless, the relatively limited amount of reported data on N2D3S activity compared to assayed NDDs makes AI/ML analysis challenging. In this work, the IFPTML technique, which combines information fusion (IF), perturbation theory (PT), and machine learning (ML), was employed to address this challenge. Initially, we conducted the fusion into a unified dataset comprising 4403 NDD assays from ChEMBL and 260 NP cytotoxicity assays from journal articles. Through a resampling process, three new working datasets were generated, each containing 500,000 cases. We utilized linear discriminant analysis (LDA) along with artificial neural network (ANN) algorithms, such as multilayer perceptron (MLP) and deep learning networks (DLN), to construct linear and non-linear IFPTML models. The IFPTML-LDA models exhibited sensitivity (Sn) and specificity (Sp) values in the range of 70% to 73% (>375,000 training cases) and 70% to 80% (>125,000 validation cases), respectively. In contrast, the IFPTML-MLP and IFPTML-DLN achieved Sn and Sp values in the range of 85% to 86% for both training and validation series. Additionally, IFPTML-ANN models showed an area under the receiver operating curve (AUROC) of approximately 0.93 to 0.95. These results indicate that the IFPTML models could serve as valuable tools in the design of drug delivery systems for neurosciences.
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Affiliation(s)
- Shan He
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº6, 48940 Leioa, Greater Bilbao, Basque Country, Spain
| | - Julen Segura Abarrategi
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
| | - Harbil Bediaga
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº6, 48940 Leioa, Greater Bilbao, Basque Country, Spain
- Painting Department, Fine Arts Faculty, University of the Basque Country UPV/EHU, 48940, Leioa, Biscay, Basque Country, Spain
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- Instituto Biofisika (UPV/EHU-CSIC), 48940 Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
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Wu Y, Liu P, Mehrjou B, Chu PK. Interdisciplinary-Inspired Smart Antibacterial Materials and Their Biomedical Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2305940. [PMID: 37469232 DOI: 10.1002/adma.202305940] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 07/14/2023] [Accepted: 07/17/2023] [Indexed: 07/21/2023]
Abstract
The discovery of antibiotics has saved millions of lives, but the emergence of antibiotic-resistant bacteria has become another problem in modern medicine. To avoid or reduce the overuse of antibiotics in antibacterial treatments, stimuli-responsive materials, pathogen-targeting nanoparticles, immunogenic nano-toxoids, and biomimetic materials are being developed to make sterilization better and smarter than conventional therapies. The common goal of smart antibacterial materials (SAMs) is to increase the antibiotic efficacy or function via an antibacterial mechanism different from that of antibiotics in order to increase the antibacterial and biological properties while reducing the risk of drug resistance. The research and development of SAMs are increasingly interdisciplinary because new designs require the knowledge of different fields and input/collaboration from scientists in different fields. A good understanding of energy conversion in materials, physiological characteristics in cells and bacteria, and bactericidal structures and components in nature are expected to promote the development of SAMs. In this review, the importance of multidisciplinary insights for SAMs is emphasized, and the latest advances in SAMs are categorized and discussed according to the pertinent disciplines including materials science, physiology, and biomimicry.
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Affiliation(s)
- Yuzheng Wu
- Department of Physics, Department of Materials Science and Engineering and Department of Biomedical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, 999077, China
| | - Pei Liu
- Department of Physics, Department of Materials Science and Engineering and Department of Biomedical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, 999077, China
| | - Babak Mehrjou
- Department of Physics, Department of Materials Science and Engineering and Department of Biomedical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, 999077, China
| | - Paul K Chu
- Department of Physics, Department of Materials Science and Engineering and Department of Biomedical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, 999077, China
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Carracedo-Reboredo P, Aranzamendi E, He S, Arrasate S, Munteanu CR, Fernandez-Lozano C, Sotomayor N, Lete E, González-Díaz H. MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products. J Cheminform 2024; 16:9. [PMID: 38254200 PMCID: PMC10804835 DOI: 10.1186/s13321-024-00802-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 01/11/2024] [Indexed: 01/24/2024] Open
Abstract
The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts are versatile catalysts for this type of reactions. The selection and design of new CPA catalysts for different enantioselective reactions has a dual interest because new CPA catalysts (tools) and chiral drugs or materials (products) can be obtained. However, this process is difficult and time consuming if approached from an experimental trial and error perspective. In this work, an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm was used to seek a predictive model for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R2 = 0.96 overall for training and validation series. It involved a Monte Carlo sampling of > 100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived N-triflylphosphoramides as CPA catalysts is reported as a case of study. The model was implemented in a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization, available online at: https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo . This new user-friendly online computational tool would enable sustainable optimization of reaction conditions that could lead to the design of new CPA catalysts along with new organic synthesis products.
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Affiliation(s)
- Paula Carracedo-Reboredo
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain
- Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain
| | - Eider Aranzamendi
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain
| | - Shan He
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940, Leioa, Spain
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain
| | - Cristian R Munteanu
- Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain
| | - Carlos Fernandez-Lozano
- Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain
| | - Nuria Sotomayor
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain.
| | - Esther Lete
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain.
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain.
- IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain.
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Miao R, Bissoli M, Basagni A, Marotta E, Corni S, Amendola V. Data-Driven Predetermination of Cu Oxidation State in Copper Nanoparticles: Application to the Synthesis by Laser Ablation in Liquid. J Am Chem Soc 2023; 145:25737-25752. [PMID: 37907392 PMCID: PMC10690790 DOI: 10.1021/jacs.3c09158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/12/2023] [Accepted: 10/18/2023] [Indexed: 11/02/2023]
Abstract
Copper-based nanocrystals are reference nanomaterials for integration into emerging green technologies, with laser ablation in liquid (LAL) being a remarkable technique for their synthesis. However, the achievement of a specific type of nanocrystal, among the whole library of nanomaterials available using LAL, has been until now an empirical endeavor based on changing synthesis parameters and characterizing the products. Here, we started from the bibliographic analysis of LAL synthesis of Cu-based nanocrystals to identify the relevant physical and chemical features for the predetermination of copper oxidation state. First, single features and their combinations were screened by linear regression analysis, also using a genetic algorithm, to find the best correlation with experimental output and identify the equation giving the best prediction of the LAL results. Then, machine learning (ML) models were exploited to unravel cross-correlations between features that are hidden in the linear regression analysis. Although the LAL-generated Cu nanocrystals may be present in a range of oxidation states, from metallic copper to cuprous oxide (Cu2O) and cupric oxide (CuO), in addition to the formation of other materials such as Cu2S and CuCN, ML was able to guide the experiments toward the maximization of the compounds in the greatest demand for integration in sustainable processes. This approach is of general applicability to other nanomaterials and can help understand the origin of the chemical pathways of nanocrystals generated by LAL, providing a rational guideline for the conscious predetermination of laser-synthesis parameters toward the desired compounds.
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Affiliation(s)
- Runpeng Miao
- Department of Chemical Sciences, University of Padova, 35131 Padova, Italy
| | - Michael Bissoli
- Department of Chemical Sciences, University of Padova, 35131 Padova, Italy
| | - Andrea Basagni
- Department of Chemical Sciences, University of Padova, 35131 Padova, Italy
| | - Ester Marotta
- Department of Chemical Sciences, University of Padova, 35131 Padova, Italy
| | - Stefano Corni
- Department of Chemical Sciences, University of Padova, 35131 Padova, Italy
| | - Vincenzo Amendola
- Department of Chemical Sciences, University of Padova, 35131 Padova, Italy
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Mazón-Ortiz G, Cerda-Mejía G, Gutiérrez Morales E, Diéguez-Santana K, Ruso JM, González-Díaz H. Trends in Nanoparticles for Leishmania Treatment: A Bibliometric and Network Analysis. Diseases 2023; 11:153. [PMID: 37987264 PMCID: PMC10660713 DOI: 10.3390/diseases11040153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/02/2023] [Accepted: 10/24/2023] [Indexed: 11/22/2023] Open
Abstract
Leishmaniasis is a neglected tropical illness with a wide variety of clinical signs ranging from visceral to cutaneous symptoms, resulting in millions of new cases and thousands of fatalities reported annually. This article provides a bibliometric analysis of the main authors' contributions, institutions, and nations in terms of productivity, citations, and bibliographic linkages to the application of nanoparticles (NPs) for the treatment of leishmania. The study is based on a sample of 524 Scopus documents from 1991 to 2022. Utilising the Bibliometrix R-Tool version 4.0 and VOSviewer software, version 1.6.17 the analysis was developed. We identified crucial subjects associated with the application of NPs in the field of antileishmanial development (NPs and drug formulation for leishmaniasis treatment, animal models, and experiments). We selected research topics that were out of date and oversaturated. Simultaneously, we proposed developing subjects based on multiple analyses of the corpus of published scientific literature (title, abstract, and keywords). Finally, the technique used contributed to the development of a broader and more specific "big picture" of nanomedicine research in antileishmanial studies for future projects.
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Affiliation(s)
- Gabriel Mazón-Ortiz
- Facultad Ciencias de la Vida, Facultad Ciencias de la Tierra y Agua, Universidad Regional Amazónica Ikiam, Parroquia Muyuna km 7 vía Alto Tena, Tena 150150, Napo, Ecuador; (G.M.-O.); (G.C.-M.); (E.G.M.)
- Soft Matter and Molecular Biophysics Group, Department of Applied Physics and Institute of Materials (iMATUS), University of Santiago de Compostela, 15782 Santiago de Compostela, Spain;
| | - Galo Cerda-Mejía
- Facultad Ciencias de la Vida, Facultad Ciencias de la Tierra y Agua, Universidad Regional Amazónica Ikiam, Parroquia Muyuna km 7 vía Alto Tena, Tena 150150, Napo, Ecuador; (G.M.-O.); (G.C.-M.); (E.G.M.)
- Soft Matter and Molecular Biophysics Group, Department of Applied Physics and Institute of Materials (iMATUS), University of Santiago de Compostela, 15782 Santiago de Compostela, Spain;
| | - Eberto Gutiérrez Morales
- Facultad Ciencias de la Vida, Facultad Ciencias de la Tierra y Agua, Universidad Regional Amazónica Ikiam, Parroquia Muyuna km 7 vía Alto Tena, Tena 150150, Napo, Ecuador; (G.M.-O.); (G.C.-M.); (E.G.M.)
| | - Karel Diéguez-Santana
- Facultad Ciencias de la Vida, Facultad Ciencias de la Tierra y Agua, Universidad Regional Amazónica Ikiam, Parroquia Muyuna km 7 vía Alto Tena, Tena 150150, Napo, Ecuador; (G.M.-O.); (G.C.-M.); (E.G.M.)
- Wood Engineering Department, University of Bio-Bio, Concepcion 4030000, Chile
| | - Juan M. Ruso
- Soft Matter and Molecular Biophysics Group, Department of Applied Physics and Institute of Materials (iMATUS), University of Santiago de Compostela, 15782 Santiago de Compostela, Spain;
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, 48940 Leioa, Spain
- Basque Center for Biophysics CSIC-UPVEH, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
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Kleandrova VV, Cordeiro MNDS, Speck-Planche A. Optimizing drug discovery using multitasking models for quantitative structure-biological effect relationships: an update of the literature. Expert Opin Drug Discov 2023; 18:1231-1243. [PMID: 37639708 DOI: 10.1080/17460441.2023.2251385] [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: 06/08/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023]
Abstract
INTRODUCTION Drug discovery has provided modern societies with the means to fight against many diseases. In this sense, computational methods have been at the forefront, playing an important role in rationalizing the search for novel drugs. Yet, tackling phenomena such as the multi-genic nature of diseases and drug resistance are limitations of the current computational methods. Multi-tasking models for quantitative structure-biological effect relationships (mtk-QSBER) have emerged to overcome such limitations. AREAS COVERED The present review describes an update on the fundamentals and applications of the mtk-QSBER models as tools to accelerate multiple stages/substages of the drug discovery process. EXPERT OPINION Computational approaches are extremely important for the rationalization of the search for novel and efficacious therapeutic agents. However, they need to focus more on the multi-target drug discovery paradigm. In this sense, mtk-QSBER models are particularly suited for multi-target drug discovery, offering encouraging opportunities across multiple therapeutic areas and scientific disciplines associated with drug discovery.
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Affiliation(s)
- Valeria V Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Russian Biotechnological University, Moscow, Russian Federation
| | - M Natália D S Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Alejandro Speck-Planche
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
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Diéguez-Santana K, González-Díaz H. Machine learning in antibacterial discovery and development: A bibliometric and network analysis of research hotspots and trends. Comput Biol Med 2023; 155:106638. [PMID: 36764155 DOI: 10.1016/j.compbiomed.2023.106638] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/05/2023] [Accepted: 02/05/2023] [Indexed: 02/10/2023]
Abstract
Machine learning (ML) methods are used in cheminformatics processes to predict the activity of an unknown drug and thus discover new potential antibacterial drugs. This article conducts a bibliometric study to analyse the contributions of leading authors, universities/organisations and countries in terms of productivity, citations and bibliographic linkage. A sample of 1596 Scopus documents for the period 2006-2022 is the basis of the study. In order to develop the analysis, bibliometrix R-Tool and VOSviewer software were used. We determined essential topics related to the application of ML in the field of antibacterial development (Computer model in antibacterial drug design, and Learning algorithms and systems for forecasting). We identified obsolete and saturated areas of research. At the same time, we proposed emerging topics according to the various analyses carried out on the corpus of published scientific literature (Title, abstract and keywords). Finally, the applied methodology contributed to building a broader and more specific "big picture" of ML research in antibacterial studies for the focus of future projects.
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Affiliation(s)
- Karel Diéguez-Santana
- Universidad Regional Amazónica Ikiam, Parroquia Muyuna km 7 vía Alto Tena, 150150, Tena-Napo, Ecuador; Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940, Leioa, Spain.
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940, Leioa, Spain; Basque Center for Biophysics CSIC-UPVEH, University of Basque Country UPV/EHU, 48940, Leioa, Spain; IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Biscay, Spain.
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Palai D, Tahara H, Chikami S, Latag GV, Maeda S, Komura C, Kurioka H, Hayashi T. Prediction of Serum Adsorption onto Polymer Brush Films by Machine Learning. ACS Biomater Sci Eng 2022; 8:3765-3772. [PMID: 35905395 DOI: 10.1021/acsbiomaterials.2c00441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Using machine learning based on a random forest (RF) regression algorithm, we attempted to predict the amount of adsorbed serum protein on polymer brush films from the films' physicochemical information and the monomers' chemical structures constituting the films using a RF model. After the training of the RF model using the data of polymer brush films synthesized from five different types of monomers, the model became capable of predicting the amount of adsorbed protein from the chemical structure, physicochemical properties of monomer molecules, and structural parameters (density and thickness of the films). The analysis of the trained RF quantitatively provided the importance of each structural parameter and physicochemical properties of monomers toward serum protein adsorption (SPA). The ranking for the significance of the parameters agrees with our general understanding and perception. Based on the results, we discuss the correlation between brush film's physical properties (such as thickness and density) and SPA and attempt to provide a guideline for the design of antibiofouling polymer brush films.
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Affiliation(s)
- Debabrata Palai
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-Cho Midori-Ku, Yokohama, Kanagawa 226-8502, Japan
| | - Hiroyuki Tahara
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-Cho Midori-Ku, Yokohama, Kanagawa 226-8502, Japan
| | - Shunta Chikami
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-Cho Midori-Ku, Yokohama, Kanagawa 226-8502, Japan
| | - Glenn Villena Latag
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-Cho Midori-Ku, Yokohama, Kanagawa 226-8502, Japan
| | - Shoichi Maeda
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-Cho Midori-Ku, Yokohama, Kanagawa 226-8502, Japan
| | - Chisato Komura
- Research Institute for Advanced Materials and Devices, Kyocera Corporation, 3-5-3 Hikaridai, Seika-Cho, Soraku-gun, Kyoto 619-0237, Japan
| | - Hideharu Kurioka
- Research Institute for Advanced Materials and Devices, Kyocera Corporation, 3-5-3 Hikaridai, Seika-Cho, Soraku-gun, Kyoto 619-0237, Japan
| | - Tomohiro Hayashi
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-Cho Midori-Ku, Yokohama, Kanagawa 226-8502, Japan.,The Institute for Solid State Physics, the University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba, 277-0882, Japan
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Diéguez-Santana K, Casañola-Martin GM, Torres R, Rasulev B, Green JR, González-Díaz H. Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds. Mol Pharm 2022; 19:2151-2163. [PMID: 35671399 PMCID: PMC9986951 DOI: 10.1021/acs.molpharmaceut.2c00029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.
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Affiliation(s)
- Karel Diéguez-Santana
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain.,Universidad Regional Amazónica IKIAM, Tena, Napo 150150, Ecuador
| | - Gerardo M Casañola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States.,Department of Systems and Computer Engineering, Carleton University, K1S5B6 Ottawa, Ontario, Canada
| | - Roldan Torres
- Universidad Regional Amazónica IKIAM, Tena, Napo 150150, Ecuador
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - James R Green
- Department of Systems and Computer Engineering, Carleton University, K1S5B6 Ottawa, Ontario, Canada
| | - Humbert González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain.,BIOFISIKA, Basque Center for Biophysics CSIC-UPVEH, 48940 Leioa, Spain.,IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
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