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Saifi I, Bhat BA, Hamdani SS, Bhat UY, Lobato-Tapia CA, Mir MA, Dar TUH, Ganie SA. Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science. J Biomol Struct Dyn 2024; 42:6523-6541. [PMID: 37434311 DOI: 10.1080/07391102.2023.2234039] [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: 02/12/2023] [Accepted: 07/03/2023] [Indexed: 07/13/2023]
Abstract
In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science and chemistry, is used to extract chemical information and search compound databases, while the application of AI and ML allows for the identification of potential hit compounds, optimization of synthesis routes, and prediction of drug efficacy and toxicity. This collaborative approach has led to the discovery, preclinical evaluations and approval of over 70 drugs in recent years. To aid researchers in the pursuit of new drugs, this article presents a comprehensive list of databases, datasets, predictive and generative models, scoring functions and web platforms that have been launched between 2021 and 2022. These resources provide a wealth of information and tools for computer-assisted drug development, and are a valuable asset for those working in the field of cheminformatics. Overall, the integration of AI, ML and cheminformatics has greatly advanced the drug discovery process and continues to hold great potential for the future. As new resources and technologies become available, we can expect to see even more groundbreaking discoveries and advancements in these fields.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ifra Saifi
- Chaudhary Charan Singh University, Meerut, Uttar Pradesh, India
| | - Basharat Ahmad Bhat
- Department of Bioresources, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
| | - Syed Suhail Hamdani
- Department of Bioresources, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
| | - Umar Yousuf Bhat
- Department of Zoology, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
| | | | - Mushtaq Ahmad Mir
- Department of Clinical Laboratory Sciences, College of Applied Medical Science, King Khalid University, KSA, Saudi Arabia
| | - Tanvir Ul Hasan Dar
- Department of Biotechnology, School of Biosciences and Biotechnology, BGSB University, Rajouri, India
| | - Showkat Ahmad Ganie
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
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2
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Smith DA, Burton LM, Smith SA. Through a computer monitor darkly: artificial intelligence in absorption, distribution, metabolism and excretion science. Xenobiotica 2024; 54:359-367. [PMID: 38095217 DOI: 10.1080/00498254.2023.2295361] [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] [Received: 10/06/2023] [Accepted: 12/12/2023] [Indexed: 08/22/2024]
Abstract
Artificial Intelligence (AI) is poised or has already begun to influence absorption, distribution, metabolism and excretion (ADME) science. It is not in the area expected - that of superior modelling of ADME data to increase its predictive power. It is influencing traditional exhaustive and careful literature research by providing almost perfect summaries of existing information. This will highly influence how people study, graduate and progress in the ADME sciences. The literature contains many flaws (protein binding influence on unbound drug concentration is one of the examples cited) and without direction AI may help to popularise them.ADME science has a relatively small number of key assays and values, but these are produced under widely varying conditions so large data sets, the best substrate for artificial intelligence, are not readily available to produce new more predictive systems. The use of AI to enrich the databases may be a near term goal.AI is already contributing in other areas such as technical skill assimilation, maintenance of complex instruments (combined with virtual reality) and the processing of pharmacovigilance.
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Zhu J, Meng H, Li X, Jia L, Xu L, Cai Y, Chen Y, Jin J, Yu L. Optimization of virtual screening against phosphoinositide 3-kinase delta: Integration of common feature pharmacophore and multicomplex-based molecular docking. Comput Biol Chem 2024; 109:108011. [PMID: 38198965 DOI: 10.1016/j.compbiolchem.2023.108011] [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: 11/23/2023] [Revised: 12/29/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024]
Abstract
Extensive research has accumulated which suggests that phosphatidylinositol 3-kinase delta (PI3Kδ) is closely related to the occurrence and development of various human diseases, making PI3Kδ a highly promising drug target. However, PI3Kδ exhibits high homology with other members of the PI3K family, which poses significant challenges to the development of PI3Kδ inhibitors. Therefore, in the present study, a hybrid virtual screening (VS) approach based on a ligand-based pharmacophore model and multicomplex-based molecular docking was developed to find novel PI3Kδ inhibitors. 13 crystal structures of the human PI3Kδ-inhibitor complex were collected to establish models. The inhibitors were extracted from the crystal structures to generate the common feature pharmacophore. The crystallographic protein structures were used to construct a naïve Bayesian classification model that integrates molecular docking based on multiple PI3Kδ conformations. Subsequently, three VS protocols involving sequential or parallel molecular docking and pharmacophore approaches were employed. External predictions demonstrated that the protocol combining molecular docking and pharmacophore resulted in a significant improvement in the enrichment of active PI3Kδ inhibitors. Finally, the optimal VS method was utilized for virtual screening against a large chemical database, and some potential hit compounds were identified. We hope that the developed VS strategy will provide valuable guidance for the discovery of novel PI3Kδ inhibitors.
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Affiliation(s)
- Jingyu Zhu
- School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China.
| | - Huiqin Meng
- School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Xintong Li
- School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Lei Jia
- School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Yanfei Cai
- School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Yun Chen
- School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Jian Jin
- School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Li Yu
- School of Inspection and Testing Certification, Changzhou Vocational Institute of Engineering, Changzhou, Jiangsu 213164, China.
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Nandi S, Bhaduri S, Das D, Ghosh P, Mandal M, Mitra P. Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence. Mol Pharm 2024; 21:1563-1590. [PMID: 38466810 DOI: 10.1021/acs.molpharmaceut.3c01161] [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] [Indexed: 03/13/2024]
Abstract
Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.
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Affiliation(s)
- Suvendu Nandi
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Soumyadeep Bhaduri
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Debraj Das
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Priya Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Mahitosh Mandal
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
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Wang Y, Chen S, Shi W, Liu S, Chen X, Pan N, Wang X, Su Y, Liu Z. Targeted Affinity Purification and Mechanism of Action of Angiotensin-Converting Enzyme (ACE) Inhibitory Peptides from Sea Cucumber Gonads. Mar Drugs 2024; 22:90. [PMID: 38393061 PMCID: PMC10890666 DOI: 10.3390/md22020090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 02/25/2024] Open
Abstract
Protein hydrolysates from sea cucumber (Apostichopus japonicus) gonads are rich in active materials with remarkable angiotensin-converting enzyme (ACE) inhibitory activity. Alcalase was used to hydrolyze sea cucumber gonads, and the hydrolysate was separated by the ultrafiltration membrane to produce a low-molecular-weight peptide component (less than 3 kDa) with good ACE inhibitory activity. The peptide component (less than 3 kDa) was isolated and purified using a combination method of ACE gel affinity chromatography and reverse high-performance liquid chromatography. The purified fractions were identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS), and the resulting products were filtered using structure-based virtual screening (SBVS) to obtain 20 peptides. Of those, three noncompetitive inhibitory peptides (DDQIHIF with an IC50 value of 333.5 μmol·L-1, HDWWKER with an IC50 value of 583.6 μmol·L-1, and THDWWKER with an IC50 value of 1291.8 μmol·L-1) were further investigated based on their favorable pharmacochemical properties and ACE inhibitory activity. Molecular docking studies indicated that the three peptides were entirely enclosed within the ACE protein cavity, improving the overall stability of the complex through interaction forces with the ACE active site. The total free binding energies (ΔGtotal) for DDQIHIF, HDWWKER, and THDWWKER were -21.9 Kcal·mol-1, -71.6 Kcal·mol-1, and -69.1 Kcal·mol-1, respectively. Furthermore, a short-term assay of antihypertensive activity in spontaneously hypertensive rats (SHRs) revealed that HDWWKER could significantly decrease the systolic blood pressure (SBP) of SHRs after intravenous administration. The results showed that based on the better antihypertensive activity of the peptide in SHRs, the feasibility of targeted affinity purification and computer-aided drug discovery (CADD) for the efficient screening and preparation of ACE inhibitory peptide was verified, which provided a new idea of modern drug development method for clinical use.
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Affiliation(s)
- Yangduo Wang
- College of Food Sciences and Technology, Shanghai Ocean University, Shanghai 202206, China; (Y.W.); (W.S.)
- Key Laboratory of Cultivation and High-Value Utilization of Marine Organisms, Fisheries Research Institute of Fujian, Xiamen 361013, China; (S.L.); (X.C.); (N.P.); (X.W.)
| | - Shicheng Chen
- Medical Laboratory Sciences Program, College of Health and Human Sciences, Northern Illinois University, DeKalb, IL 60015, USA;
| | - Wenzheng Shi
- College of Food Sciences and Technology, Shanghai Ocean University, Shanghai 202206, China; (Y.W.); (W.S.)
| | - Shuji Liu
- Key Laboratory of Cultivation and High-Value Utilization of Marine Organisms, Fisheries Research Institute of Fujian, Xiamen 361013, China; (S.L.); (X.C.); (N.P.); (X.W.)
| | - Xiaoting Chen
- Key Laboratory of Cultivation and High-Value Utilization of Marine Organisms, Fisheries Research Institute of Fujian, Xiamen 361013, China; (S.L.); (X.C.); (N.P.); (X.W.)
| | - Nan Pan
- Key Laboratory of Cultivation and High-Value Utilization of Marine Organisms, Fisheries Research Institute of Fujian, Xiamen 361013, China; (S.L.); (X.C.); (N.P.); (X.W.)
| | - Xiaoyan Wang
- Key Laboratory of Cultivation and High-Value Utilization of Marine Organisms, Fisheries Research Institute of Fujian, Xiamen 361013, China; (S.L.); (X.C.); (N.P.); (X.W.)
| | - Yongchang Su
- Key Laboratory of Cultivation and High-Value Utilization of Marine Organisms, Fisheries Research Institute of Fujian, Xiamen 361013, China; (S.L.); (X.C.); (N.P.); (X.W.)
| | - Zhiyu Liu
- Key Laboratory of Cultivation and High-Value Utilization of Marine Organisms, Fisheries Research Institute of Fujian, Xiamen 361013, China; (S.L.); (X.C.); (N.P.); (X.W.)
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Wu X, Li W, Tu H. Big data and artificial intelligence in cancer research. Trends Cancer 2024; 10:147-160. [PMID: 37977902 DOI: 10.1016/j.trecan.2023.10.006] [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] [Received: 08/31/2023] [Revised: 10/17/2023] [Accepted: 10/20/2023] [Indexed: 11/19/2023]
Abstract
The field of oncology has witnessed an extraordinary surge in the application of big data and artificial intelligence (AI). AI development has made multiscale and multimodal data fusion and analysis possible. A new era of extracting information from complex big data is rapidly evolving. However, challenges related to efficient data curation, in-depth analysis, and utilization remain. We provide a comprehensive overview of the current state of the art in big data and computational analysis, highlighting key applications, challenges, and future opportunities in cancer research. By sketching the current landscape, we seek to foster a deeper understanding and facilitate the advancement of big data utilization in oncology, call for interdisciplinary collaborations, ultimately contributing to improved patient outcomes and a profound understanding of cancer.
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Affiliation(s)
- Xifeng Wu
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Wenyuan Li
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Huakang Tu
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China
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7
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Seo S, Lee JW. Applications of Big Data and AI-Driven Technologies in CADD (Computer-Aided Drug Design). Methods Mol Biol 2024; 2714:295-305. [PMID: 37676605 DOI: 10.1007/978-1-0716-3441-7_16] [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] [Indexed: 09/08/2023]
Abstract
In the field of computer-aided drug design (CADD), there has been dramatic progress in the development of big data and AI-driven methodologies. The expensive and time-consuming process of drug design is related to biomedical complexity. CADD can be used to apply effective and efficient strategies to overcome obstacles in the field of drug design in order to properly design and develop a new medicine. To prepare the raw data for consistent and repeatable applications of big data and AI methodologies, data pre-processing methods are introduced. Big data and AI technologies can be used to develop drugs in areas including predicting absorption, distribution, metabolism, excretion, and toxicity properties as well as finding binding sites in target proteins and conducting structure-based virtual screenings. The accurate and thorough analysis of large amounts of biomedical data as well as the design of prediction models in the area of drug design is made possible by data pre-processing and applications of big data and AI skills. In the biomedical big data era, knowledge on the biological, chemical, or pharmacological structures of biomedical entities relevant to drug design should be analyzed with significant big data and AI approaches.
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Affiliation(s)
- Seongmin Seo
- Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Jai Woo Lee
- Department of Big Data Science, College of Public Policy, Korea University, Sejong, Republic of Korea.
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8
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da Fonseca AM, Cabongo SQ, Caluaco BJ, Colares RP, Fernandes CFC, Dos Santos HS, de Lima-Neto P, Marinho ES. The search for new efficient inhibitors of SARS-COV-2 through the De novo drug design developed by artificial intelligence. J Biomol Struct Dyn 2023; 41:9890-9906. [PMID: 36420665 DOI: 10.1080/07391102.2022.2148128] [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: 08/11/2022] [Accepted: 11/10/2022] [Indexed: 11/25/2022]
Abstract
The pandemic caused by Sars-CoV-2 is a viral infection that has generated one of the most significant health problems worldwide. Previous studies report the main protease (Mpro) as a potential target for this virus, as it is considered a crucial enzyme in mediating replication and viral transcription. This work presented the construction of new bioactive compounds for possible inhibition. The De novo molecular design of drugs method in the incremental construction of a ligant model within a receptor model was used, producing new structures with the help of artificial intelligence. The research algorithm and the scoring function responsible for predicting orientation and affinity in the molecular target at the time of coupling showed, as a result of the simulation, the compound with the highest bioaffinity value, Hit 998, with the energy of -17.62 kcal/mol, and synthetic viability close to 50%. While hit 1103 presented better synthetic viability (80%), its affinity energy of -10.28 kcal/mol. Both were compared with the reference linker N3, with a binding affinity of -7.5 kcal/mol. ADMET tests demonstrated that simulated compounds have a low risk of metabolic activation and do not exert effective distribution in the CNS, suggesting a pharmacokinetic mechanism based on local action, even with high topological polarity, which resulted in low oral bioavailability. In conclusion, MMGBSA, H-bonds, RMSD, SASA, and RMSF values were also obtained through molecular dynamics to verify the stability of the receptor-ligant complex within the active protein site to seek new therapeutic propositions in the fight against the pandemic.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Aluísio Marques da Fonseca
- Mestrado Acadêmico em Sociobiodiversidades e Tecnologias Sustentáveis - MASTS, Instituto de Engenharias e Desenvolvimento Sustentável, Universidade da Integração Internacional da Lusofonia Afro-Brasileira, Acarape, CE, Brazil
| | - Sadrack Queque Cabongo
- Instituto de Ciências Exatas e da Natureza, Universidade da Integração Internacional da Lusofonia Afro-Brasileira, Acarape, CE, Brazil
| | - Bernardino Joaquim Caluaco
- Instituto de Ciências Exatas e da Natureza, Universidade da Integração Internacional da Lusofonia Afro-Brasileira, Acarape, CE, Brazil
| | - Regilany Paulo Colares
- Instituto de Ciências Exatas e da Natureza, Universidade da Integração Internacional da Lusofonia Afro-Brasileira, Acarape, CE, Brazil
| | | | | | - Pedro de Lima-Neto
- Department of Analytical Chemistry and Physical Chemistry, Science Center, Federal University of Ceara, Fortaleza, CE, Brazil
| | - Emmanuel Silva Marinho
- Grupo de química Teorica e Eletroquimica-GQTE, Universidade Estadual do Ceará, Limoeiro do Norte, CE, Brazil
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9
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de Oliveira Viana J, Silva E Souza E, Sbaraini N, Vainstein MH, Gomes JNS, de Moura RO, Barbosa EG. Scaffold repositioning of spiro-acridine derivatives as fungi chitinase inhibitor by target fishing and in vitro studies. Sci Rep 2023; 13:7320. [PMID: 37147323 PMCID: PMC10163251 DOI: 10.1038/s41598-023-33279-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 04/11/2023] [Indexed: 05/07/2023] Open
Abstract
The concept of "one target, one drug, one disease" is not always true, as compounds with previously described therapeutic applications can be useful to treat other maladies. For example, acridine derivatives have several potential therapeutic applications. In this way, identifying new potential targets for available drugs is crucial for the rational management of diseases. Computational methodologies are interesting tools in this field, as they use rational and direct methods. Thus, this study focused on identifying other rational targets for acridine derivatives by employing inverse virtual screening (IVS). This analysis revealed that chitinase enzymes can be potential targets for these compounds. Subsequently, we coupled molecular docking consensus analysis to screen the best chitinase inhibitor among acridine derivatives. We observed that 3 compounds displayed potential enhanced activity as fungal chitinase inhibitors, showing that compound 5 is the most active molecule, with an IC50 of 0.6 ng/µL. In addition, this compound demonstrated a good interaction with the active site of chitinases from Aspergillus fumigatus and Trichoderma harzianum. Additionally, molecular dynamics and free energy demonstrated complex stability for compound 5. Therefore, this study recommends IVS as a powerful tool for drug development. The potential applications are highlighted as this is the first report of spiro-acridine derivatives acting as chitinase inhibitors that can be potentially used as antifungal and antibacterial candidates.
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Affiliation(s)
- Jéssika de Oliveira Viana
- Post-Graduate Program in Bioinformatics, Bioinformatics Multidisciplinary Environment, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Eden Silva E Souza
- School of Biomolecular and Biomedical Science & BiOrbic-Bioeconomy Research Center, University College Dublin, Dublin, Ireland
| | - Nicolau Sbaraini
- Biotechnology Center, Postgraduate Program in Cellular and Molecular Biology, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Marilene Henning Vainstein
- Biotechnology Center, Postgraduate Program in Cellular and Molecular Biology, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | | | | | - Euzébio Guimarães Barbosa
- Post-Graduate Program in Bioinformatics, Bioinformatics Multidisciplinary Environment, Federal University of Rio Grande do Norte, Natal, Brazil.
- Post-Graduate Program in Pharmaceutical Sciences, Faculty of Pharmacy, Federal University of Rio Grande do Norte, Natal, Brazil.
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10
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Carneiro J, Magalhães RP, de la Oliva Roque VM, Simões M, Pratas D, Sousa SF. TargIDe: a machine-learning workflow for target identification of molecules with antibiofilm activity against Pseudomonas aeruginosa. J Comput Aided Mol Des 2023; 37:265-278. [PMID: 37085636 DOI: 10.1007/s10822-023-00505-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 04/12/2023] [Indexed: 04/23/2023]
Abstract
Bacterial biofilms are a source of infectious human diseases and are heavily linked to antibiotic resistance. Pseudomonas aeruginosa is a multidrug-resistant bacterium widely present and implicated in several hospital-acquired infections. Over the last years, the development of new drugs able to inhibit Pseudomonas aeruginosa by interfering with its ability to form biofilms has become a promising strategy in drug discovery. Identifying molecules able to interfere with biofilm formation is difficult, but further developing these molecules by rationally improving their activity is particularly challenging, as it requires knowledge of the specific protein target that is inhibited. This work describes the development of a machine learning multitechnique consensus workflow to predict the protein targets of molecules with confirmed inhibitory activity against biofilm formation by Pseudomonas aeruginosa. It uses a specialized database containing all the known targets implicated in biofilm formation by Pseudomonas aeruginosa. The experimentally confirmed inhibitors available on ChEMBL, together with chemical descriptors, were used as the input features for a combination of nine different classification models, yielding a consensus method to predict the most likely target of a ligand. The implemented algorithm is freely available at https://github.com/BioSIM-Research-Group/TargIDe under licence GNU General Public Licence (GPL) version 3 and can easily be improved as more data become available.
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Affiliation(s)
- João Carneiro
- Interdisciplinary Centre of Marine and Environmental Research, CIIMAR, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, Porto, 4450-208, Portugal.
| | - Rita P Magalhães
- Faculty of Medicine, Associate Laboratory i4HB-Institute for Health and Bioeconomy, University of Porto, 4200-319, Porto, Portugal
- Department of Biomedicine, Faculty of Medicine, UCIBIO-Applied Molecular Biosciences Unit, University of Porto, BioSIM, Porto, 4200-319, Portugal
| | - Victor M de la Oliva Roque
- Faculty of Medicine, Associate Laboratory i4HB-Institute for Health and Bioeconomy, University of Porto, 4200-319, Porto, Portugal
- Department of Biomedicine, Faculty of Medicine, UCIBIO-Applied Molecular Biosciences Unit, University of Porto, BioSIM, Porto, 4200-319, Portugal
| | - Manuel Simões
- Faculty of Engineering, LEPABE Laboratory for Process Engineering, Environment, Biotechnology and Energy, University of Porto, Rua Dr. Roberto Frias, s/n, Porto, 4200-465, Portugal
- Faculty of Engineering, ALiCE-Associate Laboratory in Chemical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal
| | - Diogo Pratas
- Institute of Electronics and Informatics Engineering of Aveiro, IEETA, University of Aveiro, Aveiro, Portugal
- Department of Electronics, Telecommunications and Informatics, DETI, University of Aveiro, Aveiro, Portugal
- Department of Virology, DoV, University of Helsinki, Helsinki, Finland
| | - Sérgio F Sousa
- Faculty of Medicine, Associate Laboratory i4HB-Institute for Health and Bioeconomy, University of Porto, 4200-319, Porto, Portugal
- Department of Biomedicine, Faculty of Medicine, UCIBIO-Applied Molecular Biosciences Unit, University of Porto, BioSIM, Porto, 4200-319, Portugal
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11
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Tysinger EP, Rai BK, Sinitskiy AV. Can We Quickly Learn to "Translate" Bioactive Molecules with Transformer Models? J Chem Inf Model 2023; 63:1734-1744. [PMID: 36914216 DOI: 10.1021/acs.jcim.2c01618] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
Meaningful exploration of the chemical space of druglike molecules in drug design is a highly challenging task due to a combinatorial explosion of possible modifications of molecules. In this work, we address this problem with transformer models, a type of machine learning (ML) model originally developed for machine translation. By training transformer models on pairs of similar bioactive molecules from the public ChEMBL data set, we enable them to learn medicinal-chemistry-meaningful, context-dependent transformations of molecules, including those absent from the training set. By retrospective analysis on the performance of transformer models on ChEMBL subsets of ligands binding to COX2, DRD2, or HERG protein targets, we demonstrate that the models can generate structures identical or highly similar to most active ligands, despite the models having not seen any ligands active against the corresponding protein target during training. Our work demonstrates that human experts working on hit expansion in drug design can easily and quickly employ transformer models, originally developed to translate texts from one natural language to another, to "translate" from known molecules active against a given protein target to novel molecules active against the same target.
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Affiliation(s)
- Emma P Tysinger
- Machine Learning and Computational Sciences, Pfizer Worldwide Research, Development, and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Brajesh K Rai
- Machine Learning and Computational Sciences, Pfizer Worldwide Research, Development, and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Anton V Sinitskiy
- Machine Learning and Computational Sciences, Pfizer Worldwide Research, Development, and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
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Ma Q, Wang G, Li N, Wang X, Kang X, Mao Y, Wang G. Insights into the Effects and Mechanism of Andrographolide-Mediated Recovery of Susceptibility of Methicillin-Resistant Staphylococcus aureus to β-Lactam Antibiotics. Microbiol Spectr 2023; 11:e0297822. [PMID: 36602386 PMCID: PMC9927479 DOI: 10.1128/spectrum.02978-22] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 12/13/2022] [Indexed: 01/06/2023] Open
Abstract
The frequent resistance associated with β-lactam antibiotics and the high frequency of mutations in β-lactamases constitute a major clinical challenge that can no longer be ignored. Andrographolide (AP), a natural active compound, has been shown to restore susceptibility to β-lactam antibiotics. Fluorescence quenching and molecular simulation showed that AP quenched the intrinsic fluorescence of β-lactamase BlaZ and stably bound to the residues in the catalytic cavity of BlaZ. Of note, AP was found to reduce the stability of the cell wall (CW) in methicillin-resistant Staphylococcus aureus (MRSA), and in combination with penicillin G (PEN), it significantly induced CW roughness and dispersion and even caused its disintegration, while the same concentration of PEN did not. In addition, transcriptome sequencing revealed that AP induced a significant stress response and increased peptidoglycan (PG) synthesis but disrupted its cross-linking, and it repressed the expression of critical genes such as mecA, blaZ, and sarA. We also validated these findings by quantitative reverse transcription-PCR (qRT-PCR). Association analysis using the GEO database showed that the alterations caused by AP were similar to those caused by mutations in the sarA gene. In summary, AP was able to restore the susceptibility of MRSA to β-lactam antibiotics, mainly by inhibiting the β-lactamase BlaZ, by downregulating the expression of critical resistance genes such as mecA and blaZ, and by disrupting CW homeostasis. In addition, restoration of susceptibility to antibiotics could be achieved by inhibiting the global regulator SarA, providing an effective solution to alleviate the problem of bacterial resistance. IMPORTANCE Increasingly, alternatives to antibiotics are being used to mitigate the rapid onset and development of bacterial resistance, and the combination of natural compounds with traditional antibiotics has become an effective therapeutic strategy. Therefore, we attempted to discover more mechanisms to restore susceptibility and effective dosing strategies. Andrographolide (AP), as a natural active ingredient, can mediate recovery of susceptibility of MRSA to β-lactam antibiotics. AP bound stably to the β-lactamase BlaZ and impaired its hydrolytic activity. Notably, AP was able to downregulate the expression of critical resistance genes such as mecA, blaZ, and sarA. Meanwhile, it disrupted the CW cross-linking and homeostasis, while the same concentration of penicillin could not. The multiple inhibitory effect of AP resensitizes intrinsically resistant bacteria to β-lactam antibiotics, effectively prolonging the use cycle of these antibiotics and providing an effective solution to reduce the dosage of antibiotics and providing a theoretical reference for the prevention and control of MRSA.
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Affiliation(s)
- Qiang Ma
- Veterinary Pharmacology Lab, College of Agriculture, Ningxia University, Yinchuan, Ningxia, China
| | - Guilai Wang
- Yinchuan Hospital of Traditional Chinese Medicine, Yinchuan, Ningxia, China
| | - Na Li
- Veterinary Pharmacology Lab, College of Agriculture, Ningxia University, Yinchuan, Ningxia, China
| | - Xin Wang
- Veterinary Pharmacology Lab, College of Agriculture, Ningxia University, Yinchuan, Ningxia, China
| | - Xinyun Kang
- Veterinary Pharmacology Lab, College of Agriculture, Ningxia University, Yinchuan, Ningxia, China
| | - Yanni Mao
- Veterinary Pharmacology Lab, College of Agriculture, Ningxia University, Yinchuan, Ningxia, China
| | - Guiqin Wang
- Veterinary Pharmacology Lab, College of Agriculture, Ningxia University, Yinchuan, Ningxia, China
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A New Anticancer Semisynthetic Theobromine Derivative Targeting EGFR Protein: CADDD Study. LIFE (BASEL, SWITZERLAND) 2023; 13:life13010191. [PMID: 36676140 PMCID: PMC9867533 DOI: 10.3390/life13010191] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/25/2022] [Accepted: 01/06/2023] [Indexed: 01/10/2023]
Abstract
A new lead compound has been designed as an antiangiogenic EGFR inhibitor that has the pharmacophoric characteristics to bind with the catalytic pocket of EGFR protein. The designed lead compound is a (para-chloro)acetamide derivative of the alkaloid, theobromine, (T-1-PCPA). At first, we started with deep density functional theory (DFT) calculations for T-1-PCPA to confirm and optimize its 3D structure. Additionally, the DFT studies identified the electrostatic potential, global reactive indices and total density of states expecting a high level of reactivity for T-1-PCPA. Secondly, the affinity of T-1-PCPA to bind and inhibit the EGFR protein was studied and confirmed through detailed structure-based computational studies including the molecular docking against EGFRWT and EGFRT790M, Molecular dynamics (MD) over 100 ns, MM-GPSA and PLIP experiments. Before the preparation, the computational ADME and toxicity profiles of T-1-PCPA have been investigated and its safety and the general drug-likeness predicted. Accordingly, T-1-PCPA was semi-synthesized to scrutinize the proposed design and the obtained in silico results. Interestingly, T-1-PCPA inhibited in vitro EGFRWT with an IC50 value of 25.35 nM, comparing that of erlotinib (5.90 nM). Additionally, T-1-PCPA inhibited the growth of A549 and HCT-116 malignant cell lines with IC50 values of 31.74 and 20.40 µM, respectively, comparing erlotinib that expressed IC50 values of 6.73 and 16.35 µM, respectively.
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Wang D, Deng H, Zhang T, Tian F, Wei D. Open access databases available for the pesticide lead discovery. PESTICIDE BIOCHEMISTRY AND PHYSIOLOGY 2022; 188:105267. [PMID: 36464372 DOI: 10.1016/j.pestbp.2022.105267] [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: 01/08/2022] [Revised: 10/04/2022] [Accepted: 10/11/2022] [Indexed: 06/17/2023]
Abstract
Pesticide research is a multi-disciplinary collaborative study, and big data analysis based on integrating information from databases benefits decision-making in pesticide research. In the last 40 years, dozens of pesticide-related databases have been built up to describe their biological activities, toxicity, modes of action, and environmental risks, etc. However, these data are scattered and overlapping in different databases in multiple inconsistent formats, which is not convenient for information analysis and comparison. In this study, the content of 26 open access databases related to pesticide research was illustrated according to the information provided for the ligand-based drug design (LBDD) and receptor-based (or structure-based drug design, SBDD), and was summarized into three categories:1) the correspondence between the chemical structures and functional properties (biological activity, resistance, toxicity, environmental adaptation); 2) action mode study (target identification, target structures, and biological pathways); 3) computational servers for pesticide design. To our knowledge, this is the first review about the open access databases for pesticide research. The data classification could facilitate the information accessibility for pesticide research, and speed up the decision-making process in pesticide discovery.
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Affiliation(s)
- Daozhong Wang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China; Interdisciplinary Sciences Institute, Huazhong Agricultural University, Wuhan 430070, China; College of Veterinary Medicine, National Reference Laboratory of Veterinary Drug Residues (HZAU) and MAO Key Laboratory for Detection of Veterinary Drug Residues, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Shenzhen Institute of Nutrition and Health,Huazhong Agricultural University, Shenzhen 518000, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China
| | - Hua Deng
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China
| | - Tao Zhang
- College of Science, Huazhong Agricultural University, Wuhan 430070, China
| | - Fang Tian
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Dengguo Wei
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China; Interdisciplinary Sciences Institute, Huazhong Agricultural University, Wuhan 430070, China; College of Veterinary Medicine, National Reference Laboratory of Veterinary Drug Residues (HZAU) and MAO Key Laboratory for Detection of Veterinary Drug Residues, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Shenzhen Institute of Nutrition and Health,Huazhong Agricultural University, Shenzhen 518000, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China.
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15
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Zhang J, Zhang L, Wang J, Ouyang L, Wang Y. Polo-like Kinase 1 Inhibitors in Human Cancer Therapy: Development and Therapeutic Potential. J Med Chem 2022; 65:10133-10160. [PMID: 35878418 DOI: 10.1021/acs.jmedchem.2c00614] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Polo-like kinase 1 (PLK1) plays an important role in a variety of cellular functions, including the regulation of mitosis, DNA replication, autophagy, and the epithelial-mesenchymal transition (EMT). PLK1 overexpression is often associated with cell proliferation and poor prognosis in cancer patients, making it a promising antitumor target. To date, at least 10 PLK1 inhibitors (PLK1i) have been entered into clinical trials, among which the typical kinase domain (KD) inhibitor BI 6727 (volasertib) was granted "breakthrough therapy designation" by the FDA in 2013. Unfortunately, many other KD inhibitors showed poor specificity, resulting in dose-limiting toxicity, which has greatly impeded their development. Researchers recently discovered many PLK1i with higher selectivity, stronger potency, and better absorption, distribution, metabolism, and elimination (ADME) characteristics. In this review, we emphasize the structure-activity relationships (SARs) of PLK1i, providing insights into new drugs targeting PLK1 for antitumor clinical practice.
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Affiliation(s)
- Jifa Zhang
- Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, Joint Research Institution of Altitude Health, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.,State Key Laboratory of Biotherapy and Cancer Center, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Lele Zhang
- Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, Joint Research Institution of Altitude Health, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.,State Key Laboratory of Biotherapy and Cancer Center, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Jiaxing Wang
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis 38163, Tennessee, United States
| | - Liang Ouyang
- Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, Joint Research Institution of Altitude Health, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.,State Key Laboratory of Biotherapy and Cancer Center, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Yuxi Wang
- Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, Joint Research Institution of Altitude Health, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.,State Key Laboratory of Biotherapy and Cancer Center, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
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Jukič M, Bren U. Machine Learning in Antibacterial Drug Design. Front Pharmacol 2022; 13:864412. [PMID: 35592425 PMCID: PMC9110924 DOI: 10.3389/fphar.2022.864412] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/28/2022] [Indexed: 12/17/2022] Open
Abstract
Advances in computer hardware and the availability of high-performance supercomputing platforms and parallel computing, along with artificial intelligence methods are successfully complementing traditional approaches in medicinal chemistry. In particular, machine learning is gaining importance with the growth of the available data collections. One of the critical areas where this methodology can be successfully applied is in the development of new antibacterial agents. The latter is essential because of the high attrition rates in new drug discovery, both in industry and in academic research programs. Scientific involvement in this area is even more urgent as antibacterial drug resistance becomes a public health concern worldwide and pushes us increasingly into the post-antibiotic era. In this review, we focus on the latest machine learning approaches used in the discovery of new antibacterial agents and targets, covering both small molecules and antibacterial peptides. For the benefit of the reader, we summarize all applied machine learning approaches and available databases useful for the design of new antibacterial agents and address the current shortcomings.
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Affiliation(s)
- Marko Jukič
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Maribor, Slovenia
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia
| | - Urban Bren
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Maribor, Slovenia
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia
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