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Meewan I, Panmanee J, Petchyam N, Lertvilai P. HBCVTr: an end-to-end transformer with a deep neural network hybrid model for anti-HBV and HCV activity predictor from SMILES. Sci Rep 2024; 14:9262. [PMID: 38649402 PMCID: PMC11035669 DOI: 10.1038/s41598-024-59933-4] [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: 01/09/2024] [Accepted: 04/16/2024] [Indexed: 04/25/2024] Open
Abstract
Hepatitis B and C viruses (HBV and HCV) are significant causes of chronic liver diseases, with approximately 350 million infections globally. To accelerate the finding of effective treatment options, we introduce HBCVTr, a novel ligand-based drug design (LBDD) method for predicting the inhibitory activity of small molecules against HBV and HCV. HBCVTr employs a hybrid model consisting of double encoders of transformers and a deep neural network to learn the relationship between small molecules' simplified molecular-input line-entry system (SMILES) and their antiviral activity against HBV or HCV. The prediction accuracy of HBCVTr has surpassed baseline machine learning models and existing methods, with R-squared values of 0.641 and 0.721 for the HBV and HCV test sets, respectively. The trained models were successfully applied to virtual screening against 10 million compounds within 240 h, leading to the discovery of the top novel inhibitor candidates, including IJN04 for HBV and IJN12 and IJN19 for HCV. Molecular docking and dynamics simulations identified IJN04, IJN12, and IJN19 target proteins as the HBV core antigen, HCV NS5B RNA-dependent RNA polymerase, and HCV NS3/4A serine protease, respectively. Overall, HBCVTr offers a new and rapid drug discovery and development screening method targeting HBV and HCV.
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Affiliation(s)
- Ittipat Meewan
- Center for Advanced Therapeutics, Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom, 73170, Thailand.
| | - Jiraporn Panmanee
- Research Center for Neuroscience, Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom, 73170, Thailand
| | - Nopphon Petchyam
- Center for Advanced Therapeutics, Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom, 73170, Thailand
| | - Pichaya Lertvilai
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, 92037, USA
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2
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A R N, G K R. A deep learning and docking simulation-based virtual screening strategy enables the rapid identification of HIF-1α pathway activators from a marine natural product database. J Biomol Struct Dyn 2024; 42:629-651. [PMID: 37038705 DOI: 10.1080/07391102.2023.2194997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 03/17/2023] [Indexed: 04/12/2023]
Abstract
Artificial Intelligence is hailed as a cutting-edge technology for accelerating drug discovery efforts, and our goal was to validate its potential in predicting pharmacological inhibitors of EGLN1 using a deep learning-based architecture, one of its subsidiaries. Egl nine homolog 1 (EGLN1) inhibition prevents poly ubiquitination-mediated proteosomal destruction HIF-1α. The pharmacological interventions aimed at stabilizing HIF-1α have the potential to be a promising treatment option for a range of human diseases, including ischemic stroke. To unveil a novel EGLN1 inhibitor from marine natural products, a custom-based virtual screening was carried out using a Deep Convolutional Neural Network (DCNN) architecture, docking, and molecular dynamics simulation. The custom DCNN model was optimized and further employed to screen marine natural products from the CMNPD database. The docking was performed as a secondary strategy for screened hits. Molecular dynamics (MD) and molecular mechanics/generalized Born surface area (MM-GBSA) were used to analyze inhibitor binding and identify key interactions. The findings support the claim that deep learning-based virtual screening is a rapid, reliable and accurate method of identifying highly contributing drug candidates (EGLN1 inhibitors). This study demonstrates that deep learning architecture can significantly accelerate drug discovery and development, and provides a solid foundation for using (Z)-2-ethylhex-2-enedioic acid [(Z)-2-ethylhex-2-enedioic acid] as a potential EGLN1 inhibitor for treating various health complications.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Neelakandan A R
- School of Biotechnology, National Institute of Technology Calicut, Calicut, Kerala, India
| | - Rajanikant G K
- School of Biotechnology, National Institute of Technology Calicut, Calicut, Kerala, India
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3
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Andrianov AM, Shuldau MA, Furs KV, Yushkevich AM, Tuzikov AV. AI-Driven De Novo Design and Molecular Modeling for Discovery of Small-Molecule Compounds as Potential Drug Candidates Targeting SARS-CoV-2 Main Protease. Int J Mol Sci 2023; 24:ijms24098083. [PMID: 37175788 PMCID: PMC10178971 DOI: 10.3390/ijms24098083] [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: 03/14/2023] [Revised: 04/21/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
Over the past three years, significant progress has been made in the development of novel promising drug candidates against COVID-19. However, SARS-CoV-2 mutations resulting in the emergence of new viral strains that can be resistant to the drugs used currently in the clinic necessitate the development of novel potent and broad therapeutic agents targeting different vulnerable spots of the viral proteins. In this study, two deep learning generative models were developed and used in combination with molecular modeling tools for de novo design of small molecule compounds that can inhibit the catalytic activity of SARS-CoV-2 main protease (Mpro), an enzyme critically important for mediating viral replication and transcription. As a result, the seven best scoring compounds that exhibited low values of binding free energy comparable with those calculated for two potent inhibitors of Mpro, via the same computational protocol, were selected as the most probable inhibitors of the enzyme catalytic site. In light of the data obtained, the identified compounds are assumed to present promising scaffolds for the development of new potent and broad-spectrum drugs inhibiting SARS-CoV-2 Mpro, an attractive therapeutic target for anti-COVID-19 agents.
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Affiliation(s)
- Alexander M Andrianov
- Institute of Bioorganic Chemistry, National Academy of Sciences of Belarus, 220141 Minsk, Belarus
| | - Mikita A Shuldau
- United Institute of Informatics Problems, National Academy of Sciences of Belarus, 220012 Minsk, Belarus
| | - Konstantin V Furs
- United Institute of Informatics Problems, National Academy of Sciences of Belarus, 220012 Minsk, Belarus
| | - Artsemi M Yushkevich
- United Institute of Informatics Problems, National Academy of Sciences of Belarus, 220012 Minsk, Belarus
| | - Alexander V Tuzikov
- United Institute of Informatics Problems, National Academy of Sciences of Belarus, 220012 Minsk, Belarus
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4
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Juhas M. Artificial Intelligence in Microbiology. BRIEF LESSONS IN MICROBIOLOGY 2023:93-109. [DOI: 10.1007/978-3-031-29544-7_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Juhas M. Into a Brighter Future. BRIEF LESSONS IN MICROBIOLOGY 2023:143-149. [DOI: 10.1007/978-3-031-29544-7_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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6
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Diakou I, Papakonstantinou E, Papageorgiou L, Pierouli K, Dragoumani K, Spandidos DA, Bacopoulou F, Chrousos GP, Eliopoulos E, Vlachakis D. Novel computational pipelines in antiviral structure‑based drug design (Review). Biomed Rep 2022; 17:97. [PMID: 36382260 PMCID: PMC9634337 DOI: 10.3892/br.2022.1580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 10/05/2022] [Indexed: 11/22/2022] Open
Abstract
Viral infections constitute a fundamental and continuous challenge for the global scientific and medical community, as highlighted by the ongoing COVID-19 pandemic. In combination with prophylactic vaccines, the development of safe and effective antiviral drugs remains a pressing need for the effective management of rare and common pathogenic viruses. The design of potent antivirals can be informed by the study of the three-dimensional structure of viral protein targets. Structure-based design of antivirals in silico provides a solution to the arduous and costly process of conventional drug development pipelines. Furthermore, rapid advances in high-throughput computing, along with the growth of available biomolecular and biochemical data, enable the development of novel computational pipelines in the hunt of antivirals. The incorporation of modern methods, such as deep-learning and artificial intelligence, has the potential to revolutionize the structure-based design and repurposing of antiviral compounds, with minimal side effects and high efficacy. The present review aims to provide an outline of both traditional computational drug design and emerging, high-level computing strategies.
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Affiliation(s)
- Io Diakou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Eleni Papakonstantinou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Louis Papageorgiou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Katerina Pierouli
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Konstantina Dragoumani
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Demetrios A. Spandidos
- Laboratory of Clinical Virology, School of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Flora Bacopoulou
- University Research Institute of Maternal and Child Health and Precision Medicine, and UNESCO Chair on Adolescent Health Care, National and Kapodistrian University of Athens, ‘Aghia Sophia’ Children's Hospital, 11527 Athens, Greece
| | - George P. Chrousos
- University Research Institute of Maternal and Child Health and Precision Medicine, and UNESCO Chair on Adolescent Health Care, National and Kapodistrian University of Athens, ‘Aghia Sophia’ Children's Hospital, 11527 Athens, Greece
| | - Elias Eliopoulos
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Dimitrios Vlachakis
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
- University Research Institute of Maternal and Child Health and Precision Medicine, and UNESCO Chair on Adolescent Health Care, National and Kapodistrian University of Athens, ‘Aghia Sophia’ Children's Hospital, 11527 Athens, Greece
- Division of Endocrinology and Metabolism, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of The Academy of Athens, 11527 Athens, Greece
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Sun Y, Jiao Y, Shi C, Zhang Y. Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2. Comput Struct Biotechnol J 2022; 20:5014-5027. [PMID: 36091720 PMCID: PMC9448712 DOI: 10.1016/j.csbj.2022.09.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 08/03/2022] [Accepted: 09/03/2022] [Indexed: 11/26/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.
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Affiliation(s)
- Yao Sun
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Yanqi Jiao
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Chengcheng Shi
- State Key Lab of Urban Water Resource and Environment, School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Yang Zhang
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
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8
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Debnath S, Seth D, Pramanik S, Adhikari S, Mondal P, Sherpa D, Sen D, Mukherjee D, Mukerjee N. A comprehensive review and meta-analysis of recent advances in biotechnology for plant virus research and significant accomplishments in human health and the pharmaceutical industry. Biotechnol Genet Eng Rev 2022:1-33. [PMID: 36063068 DOI: 10.1080/02648725.2022.2116309] [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: 04/28/2022] [Accepted: 07/29/2022] [Indexed: 02/03/2023]
Abstract
Secondary metabolites made by plants and used through their metabolic routes are today's most reliable and cost-effective way to make pharmaceuticals and improve health. The concept of genetic engineering is used for molecular pharming. As more people use plants as sources of nanotechnology systems, they are adding to this. These systems are made up of viruses-like particles (VLPs) and virus nanoparticles (VNPs). Due to their superior ability to be used as plant virus expression vectors, plant viruses are becoming more popular in pharmaceuticals. This has opened the door for them to be used in research, such as the production of medicinal peptides, antibodies, and other heterologous protein complexes. This is because biotechnological approaches have been linked with new bioinformatics tools. Because of the rise of high-throughput sequencing (HTS) and next-generation sequencing (NGS) techniques, it has become easier to use metagenomic studies to look for plant virus genomes that could be used in pharmaceutical research. A look at how bioinformatics can be used in pharmaceutical research is also covered in this article. It also talks about plant viruses and how new biotechnological tools and procedures have made progress in the field.
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Affiliation(s)
- Sandip Debnath
- Department of Genetics and Plant Breeding, Palli Siksha Bhavana (Institute of Agriculture), Visva-Bharati University, Sriniketan, West Bengal, India
| | - Dibyendu Seth
- Department of Genetics and Plant Breeding, Palli Siksha Bhavana (Institute of Agriculture), Visva-Bharati University, Sriniketan, West Bengal, India
| | - Sourish Pramanik
- Department of Genetics and Plant Breeding, Palli Siksha Bhavana (Institute of Agriculture), Visva-Bharati University, Sriniketan, West Bengal, India
| | - Sanchari Adhikari
- Department of Genetics and Plant Breeding, Palli Siksha Bhavana (Institute of Agriculture), Visva-Bharati University, Sriniketan, West Bengal, India
| | - Parimita Mondal
- Department of Genetics and Plant Breeding, Palli Siksha Bhavana (Institute of Agriculture), Visva-Bharati University, Sriniketan, West Bengal, India
| | - Dechen Sherpa
- Department of Genetics and Plant Breeding, Palli Siksha Bhavana (Institute of Agriculture), Visva-Bharati University, Sriniketan, West Bengal, India
| | - Deepjyoti Sen
- Department of Genetics and Plant Breeding, Palli Siksha Bhavana (Institute of Agriculture), Visva-Bharati University, Sriniketan, West Bengal, India
| | | | - Nobendu Mukerjee
- Department of Microbiology, Ramakrishna Mission Vivekananda Centenary College, Kolkata, India
- Department of Health Sciences, Novel Global Community Educational Foundation, Hebarsham, Australia
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9
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Pi J, Jiao P, Zhang Y, Li J. MDGNN: Microbial Drug Prediction Based on Heterogeneous Multi-Attention Graph Neural Network. Front Microbiol 2022; 13:819046. [PMID: 35464940 PMCID: PMC9021438 DOI: 10.3389/fmicb.2022.819046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 03/07/2022] [Indexed: 11/14/2022] Open
Abstract
Human beings are now facing one of the largest public health crises in history with the outbreak of COVID-19. Traditional drug discovery could not keep peace with newly discovered infectious diseases. The prediction of drug-virus associations not only provides insights into the mechanism of drug–virus interactions, but also guides the screening of potential antiviral drugs. We develop a deep learning algorithm based on the graph convolutional networks (MDGNN) to predict potential antiviral drugs. MDGNN is consisted of new node-level attention and feature-level attention mechanism and shows its effectiveness compared with other comparative algorithms. MDGNN integrates the global information of the graph in the process of information aggregation by introducing the attention at node and feature level to graph convolution. Comparative experiments show that MDGNN achieves state-of-the-art performance with an area under the curve (AUC) of 0.9726 and an area under the PR curve (AUPR) of 0.9112. In this case study, two drugs related to SARS-CoV-2 were successfully predicted and verified by the relevant literature. The data and code are open source and can be accessed from https://github.com/Pijiangsheng/MDGNN.
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Affiliation(s)
- Jiangsheng Pi
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Peishun Jiao
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Yang Zhang
- College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, China
- *Correspondence: Yang Zhang,
| | - Junyi Li
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China
- Junyi Li,
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David L, Brata AM, Mogosan C, Pop C, Czako Z, Muresan L, Ismaiel A, Dumitrascu DI, Leucuta DC, Stanculete MF, Iaru I, Popa SL. Artificial Intelligence and Antibiotic Discovery. Antibiotics (Basel) 2021; 10:antibiotics10111376. [PMID: 34827314 PMCID: PMC8614913 DOI: 10.3390/antibiotics10111376] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/01/2021] [Accepted: 11/08/2021] [Indexed: 01/13/2023] Open
Abstract
Over recent decades, a new antibiotic crisis has been unfolding due to a decreased research in this domain, a low return of investment for the companies that developed the drug, a lengthy and difficult research process, a low success rate for candidate molecules, an increased use of antibiotics in farms and an overall inappropriate use of antibiotics. This has led to a series of pathogens developing antibiotic resistance, which poses severe threats to public health systems while also driving up the costs of hospitalization and treatment. Moreover, without proper action and collaboration between academic and health institutions, a catastrophic trend might develop, with the possibility of returning to a pre-antibiotic era. Nevertheless, new emerging AI-based technologies have started to enter the field of antibiotic and drug development, offering a new perspective to an ever-growing problem. Cheaper and faster research can be achieved through algorithms that identify hit compounds, thereby further accelerating the development of new antibiotics, which represents a vital step in solving the current antibiotic crisis. The aim of this review is to provide an extended overview of the current artificial intelligence-based technologies that are used for antibiotic discovery, together with their technological and economic impact on the industrial sector.
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Affiliation(s)
- Liliana David
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (L.D.); (A.I.); (S.L.P.)
| | - Anca Monica Brata
- Faculty of Environmental Protection, University of Oradea, 410048 Oradea, Romania
- Correspondence:
| | - Cristina Mogosan
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (C.M.); (C.P.); (I.I.)
| | - Cristina Pop
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (C.M.); (C.P.); (I.I.)
| | - Zoltan Czako
- Department of Computer Science, Technical University of Cluj-Napoca, 400027 Cluj-Napoca, Romania;
| | - Lucian Muresan
- Department of Cardiology, “Emile Muller” Hospital, 68200 Mulhouse, France;
| | - Abdulrahman Ismaiel
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (L.D.); (A.I.); (S.L.P.)
| | - Dinu Iuliu Dumitrascu
- Department of Anatomy, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania;
| | - Daniel Corneliu Leucuta
- Department of Medical Informatics and Biostatistics, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania;
| | - Mihaela Fadygas Stanculete
- Department of Neurosciences, Discipline of Psychiatry and Pediatric Psychiatry, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania;
| | - Irina Iaru
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (C.M.); (C.P.); (I.I.)
| | - Stefan Lucian Popa
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (L.D.); (A.I.); (S.L.P.)
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