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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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2
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Fernandes PO, Dias ALT, Dos Santos Júnior VS, Sá Magalhães Serafim M, Sousa YV, Monteiro GC, Coutinho ID, Valli M, Verzola MMSA, Ottoni FM, Pádua RMD, Oda FB, Dos Santos AG, Andricopulo AD, da Silva Bolzani V, Mota BEF, Alves RJ, de Oliveira RB, Kronenberger T, Maltarollo VG. Machine Learning-Based Virtual Screening of Antibacterial Agents against Methicillin-Susceptible and Resistant Staphylococcus aureus. J Chem Inf Model 2024; 64:1932-1944. [PMID: 38437501 DOI: 10.1021/acs.jcim.4c00087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
The application of computer-aided drug discovery (CADD) approaches has enabled the discovery of new antimicrobial therapeutic agents in the past. The high prevalence of methicillin-resistantStaphylococcus aureus(MRSA) strains promoted this pathogen to a high-priority pathogen for drug development. In this sense, modern CADD techniques can be valuable tools for the search for new antimicrobial agents. We employed a combination of a series of machine learning (ML) techniques to select and evaluate potential compounds with antibacterial activity against methicillin-susceptible S. aureus (MSSA) and MRSA strains. In the present study, we describe the antibacterial activity of six compounds against MSSA and MRSA reference (American Type Culture Collection (ATCC)) strains as well as two clinical strains of MRSA. These compounds showed minimal inhibitory concentrations (MIC) in the range from 12.5 to 200 μM against the different bacterial strains evaluated. Our results constitute relevant proven ML-workflow models to distinctively screen for novel MRSA antibiotics.
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Affiliation(s)
- Philipe Oliveira Fernandes
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Anna Letícia Teotonio Dias
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Valtair Severino Dos Santos Júnior
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Mateus Sá Magalhães Serafim
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Yamara Viana Sousa
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Gustavo Claro Monteiro
- Departamento de Química Orgânica, Instituto de Química, Universidade Estadual Paulista (UNESP), Araraquara, São Paulo 14.800-900, Brazil
| | - Isabel Duarte Coutinho
- Departamento de Química Orgânica, Instituto de Química, Universidade Estadual Paulista (UNESP), Araraquara, São Paulo 14.800-900, Brazil
| | - Marilia Valli
- Departamento de Física e Ciência Interdisciplinar, Instituto de Física, Universidade de São Paulo (USP), São Carlos, São Paulo 13.563-120, Brazil
| | - Marina Mol Sena Andrade Verzola
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Flaviano Melo Ottoni
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Rodrigo Maia de Pádua
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Fernando Bombarda Oda
- Departamento de Fármacos e Medicamentos, Faculdade de Ciências Farmacêuticas, Universidade Estadual Paulista (UNESP), Araraquara 14.800-903, Brazil
| | - André Gonzaga Dos Santos
- Departamento de Fármacos e Medicamentos, Faculdade de Ciências Farmacêuticas, Universidade Estadual Paulista (UNESP), Araraquara 14.800-903, Brazil
| | - Adriano Defini Andricopulo
- Departamento de Física e Ciência Interdisciplinar, Instituto de Física, Universidade de São Paulo (USP), São Carlos, São Paulo 13.563-120, Brazil
| | - Vanderlan da Silva Bolzani
- Departamento de Química Orgânica, Instituto de Química, Universidade Estadual Paulista (UNESP), Araraquara, São Paulo 14.800-900, Brazil
| | - Bruno Eduardo Fernandes Mota
- Departamento de Análises Clínicas e Toxicológicas, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Ricardo José Alves
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Renata Barbosa de Oliveira
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Thales Kronenberger
- Institute of Pharmacy, Pharmaceutical/Medicinal Chemistry and Tübingen Center for Academic Drug Discovery, Eberhard Karls University Tübingen, 72076 Tübingen, Germany
- School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Vinícius Gonçalves Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
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3
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Yamauchi K, Nakatsuji H, Kamishima T, Koseki Y, Kubo M, Kasai H. Combined substituent number utilized machine learning for the development of antimicrobial agent. Sci Rep 2024; 14:4106. [PMID: 38374237 PMCID: PMC10876936 DOI: 10.1038/s41598-024-53888-2] [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: 05/02/2023] [Accepted: 02/06/2024] [Indexed: 02/21/2024] Open
Abstract
The utilization of machine learning has a potential to improve the environment of the development of antimicrobial agents. For practical use of machine learning, it is important that the conversion of molecules information to an appropriate descriptor because too informative descriptor requires enormous computation time and experiments for gathering data, whereas a less informative descriptor has problems in validity. In this study, we utilized a descriptor only focused on substituent. The type and the position of substituents on the molecules that have a 4-quinolone structure (11,879 compounds) were converted to the combined substituent number (CSN). While the CSN does not include information on the detailed structure, physical properties, and quantum chemistry of molecules, the prediction model constructed by machine learning of CSN indicated a sufficient coefficient of determination (0.719 for the training dataset and 0.519 for the validation dataset). In addition, this CSN can easily construct the unknown molecules library which has a relatively consistent structure by recombination of substituents (32,079,318 compounds) and screening of them. The validity of the prediction model was also confirmed by growth inhibition experiments for E. coli using the model-suggested molecules and commercially available antimicrobial agents.
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Affiliation(s)
- Keitaro Yamauchi
- Institute of Multidisciplinary Research for Advance Materials (IMRAM), Tohoku University, Aoba-Ku, Sendai, Miyagi, 980-8577, Japan
| | - Hirotaka Nakatsuji
- Institute of Multidisciplinary Research for Advance Materials (IMRAM), Tohoku University, Aoba-Ku, Sendai, Miyagi, 980-8577, Japan.
- East Tokyo Laboratory, Genesis Research Institute, Inc., 717-86 Futamata, Ichikawa, Chiba, 272-0001, Japan.
| | - Takaaki Kamishima
- East Tokyo Laboratory, Genesis Research Institute, Inc., 717-86 Futamata, Ichikawa, Chiba, 272-0001, Japan
| | - Yoshitaka Koseki
- Institute of Multidisciplinary Research for Advance Materials (IMRAM), Tohoku University, Aoba-Ku, Sendai, Miyagi, 980-8577, Japan
| | - Masaki Kubo
- Department of Chemical Engineering, Graduate School of Engineering, Tohoku University, Aoba-Ku, Sendai, Miyagi, 980-8579, Japan
| | - Hitoshi Kasai
- Institute of Multidisciplinary Research for Advance Materials (IMRAM), Tohoku University, Aoba-Ku, Sendai, Miyagi, 980-8577, Japan.
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Oselusi SO, Dube P, Odugbemi AI, Akinyede KA, Ilori TL, Egieyeh E, Sibuyi NR, Meyer M, Madiehe AM, Wyckoff GJ, Egieyeh SA. The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials. Comput Biol Med 2024; 169:107927. [PMID: 38184864 DOI: 10.1016/j.compbiomed.2024.107927] [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: 09/06/2023] [Revised: 12/25/2023] [Accepted: 01/01/2024] [Indexed: 01/09/2024]
Abstract
Antimicrobial resistance (AMR) has become more of a concern in recent decades, particularly in infections associated with global public health threats. The development of new antibiotics is crucial to ensuring infection control and eradicating AMR. Although drug discovery and development are essential processes in the transformation of a drug candidate from the laboratory to the bedside, they are often very complicated, expensive, and time-consuming. The pharmaceutical sector is continuously innovating strategies to reduce research costs and accelerate the development of new drug candidates. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology that renews the hope of researchers for the faster identification, design, and development of cheaper, less resource-intensive, and more efficient drug candidates. In this review, we discuss an overview of AMR, the potential, and limitations of CADD in AMR drug discovery, and case studies of the successful application of this technique in the rapid identification of various drug candidates. This review will aid in achieving a better understanding of available CADD techniques in the discovery of novel drug candidates against resistant pathogens and other infectious agents.
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Affiliation(s)
- Samson O Oselusi
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Phumuzile Dube
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Adeshina I Odugbemi
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Cape Town, 7535, South Africa
| | - Kolajo A Akinyede
- Department of Science Technology, Biochemistry Unit, The Federal Polytechnic P.M.B.5351, Ado Ekiti, 360231, Nigeria
| | - Tosin L Ilori
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa
| | - Elizabeth Egieyeh
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa
| | - Nicole Rs Sibuyi
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Mervin Meyer
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Abram M Madiehe
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Gerald J Wyckoff
- School of Pharmacy, Division of Pharmacology and Pharmaceutical Sciences, University of Missouri, Kansas City, MO, 64110-2446, United States
| | - Samuel A Egieyeh
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa.
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5
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Chen LY, Li YP. Enhancing chemical synthesis: a two-stage deep neural network for predicting feasible reaction conditions. J Cheminform 2024; 16:11. [PMID: 38268009 PMCID: PMC11301986 DOI: 10.1186/s13321-024-00805-4] [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: 10/22/2023] [Accepted: 01/14/2024] [Indexed: 01/26/2024] Open
Abstract
In the field of chemical synthesis planning, the accurate recommendation of reaction conditions is essential for achieving successful outcomes. This work introduces an innovative deep learning approach designed to address the complex task of predicting appropriate reagents, solvents, and reaction temperatures for chemical reactions. Our proposed methodology combines a multi-label classification model with a ranking model to offer tailored reaction condition recommendations based on relevance scores derived from anticipated product yields. To tackle the challenge of limited data for unfavorable reaction contexts, we employed the technique of hard negative sampling to generate reaction conditions that might be mistakenly classified as suitable, forcing the model to refine its decision boundaries, especially in challenging cases. Our developed model excels in proposing conditions where an exact match to the recorded solvents and reagents is found within the top-10 predictions 73% of the time. It also predicts temperatures within ± 20 [Formula: see text] of the recorded temperature in 89% of test cases. Notably, the model demonstrates its capacity to recommend multiple viable reaction conditions, with accuracy varying based on the availability of condition records associated with each reaction. What sets this model apart is its ability to suggest alternative reaction conditions beyond the constraints of the dataset. This underscores its potential to inspire innovative approaches in chemical research, presenting a compelling opportunity for advancing chemical synthesis planning and elevating the field of reaction engineering. Scientific contribution: The combination of multi-label classification and ranking models provides tailored recommendations for reaction conditions based on the reaction yields. A novel approach is presented to address the issue of data scarcity in negative reaction conditions through data augmentation.
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Affiliation(s)
- Lung-Yi Chen
- Department of Chemical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan
| | - Yi-Pei Li
- Department of Chemical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan.
- Taiwan International Graduate Program on Sustainable Chemical Science and Technology (TIGP-SCST), No. 128, Sec. 2, Academia Road, Taipei, 11529, Taiwan.
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6
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Randall JR, Wang X, Groover KE, O'Donnell AC, Davies BW. Using display technologies to identify macrocyclic peptide antibiotics. BIOCHIMICA ET BIOPHYSICA ACTA. MOLECULAR CELL RESEARCH 2023; 1870:119473. [PMID: 37011732 PMCID: PMC10198949 DOI: 10.1016/j.bbamcr.2023.119473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 02/09/2023] [Accepted: 02/19/2023] [Indexed: 04/03/2023]
Abstract
Antibiotic resistant bacterial infections are now a leading cause of global mortality. While drug resistance continues to spread, the clinical antibiotic pipeline has become bare. This discord has focused attention on developing new strategies for antimicrobial discovery. Natural macrocyclic peptide-based products have provided novel antibiotics and antibiotic scaffolds targeting several essential bacterial cell envelope processes, but discovery of such natural products remains a slow and inefficient process. Synthetic strategies employing peptide display technologies can quickly screen large libraries of macrocyclic sequences for specific target binding and general antibacterial potential providing alternative approaches for new antibiotic discovery. Here we review cell envelope processes that can be targeted with macrocyclic peptide therapeutics, outline important macrocyclic peptide display technologies, and discuss future strategies for both library design and screening.
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Affiliation(s)
- Justin R Randall
- University of Texas at Austin, Department of Molecular Biosciences, Austin, TX, USA.
| | - Xun Wang
- University of Texas at Austin, Department of Molecular Biosciences, Austin, TX, USA
| | - Kyra E Groover
- University of Texas at Austin, Department of Molecular Biosciences, Austin, TX, USA
| | - Angela C O'Donnell
- University of Texas at Austin, Department of Molecular Biosciences, Austin, TX, USA
| | - Bryan W Davies
- University of Texas at Austin, Department of Molecular Biosciences, Austin, TX, USA.
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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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Singh DP, Kaushik B. A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques. Chem Biol Drug Des 2023; 101:175-194. [PMID: 36303299 DOI: 10.1111/cbdd.14164] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/13/2022] [Accepted: 10/24/2022] [Indexed: 12/24/2022]
Abstract
Computational methods have gained prominence in healthcare research. The accessibility of healthcare data has greatly incited academicians and researchers to develop executions that help in prognosis of cancer drug response. Among various computational methods, machine-learning (ML) and deep-learning (DL) methods provide the most consistent and effectual approaches to handle the serious aftermaths of the deadly disease and drug administered to the patients. Hence, this systematic literature review has reviewed researches that have investigated drug discovery and prognosis of anticancer drug response using ML and DL algorithms. Fot this purpose, PRISMA guidelines have been followed to choose research papers from Google Scholar, PubMed, and Sciencedirect websites. A total count of 105 papers that align with the context of this review were chosen. Further, the review also presents accuracy of the existing ML and DL methods in the prediction of anticancer drug response. It has been found from the review that, amidst the availability of various studies, there are certain challenges associated with each method. Thus, future researchers can consider these limitations and challenges to develop a prominent anticancer drug response prediction method, and it would be greatly beneficial to the medical professionals in administering non-invasive treatment to the patients.
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Affiliation(s)
- Davinder Paul Singh
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
| | - Baijnath Kaushik
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
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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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10
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Cantrell JM, Chung CH, Chandrasekaran S. Machine learning to design antimicrobial combination therapies: promises and pitfalls. Drug Discov Today 2022; 27:1639-1651. [DOI: 10.1016/j.drudis.2022.04.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/20/2022] [Accepted: 04/04/2022] [Indexed: 01/13/2023]
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Li WX, Tong X, Yang PP, Zheng Y, Liang JH, Li GH, Liu D, Guan DG, Dai SX. Screening of antibacterial compounds with novel structure from the FDA approved drugs using machine learning methods. Aging (Albany NY) 2022; 14:1448-1472. [PMID: 35150482 PMCID: PMC8876917 DOI: 10.18632/aging.203887] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 01/28/2022] [Indexed: 11/25/2022]
Abstract
Bacterial infection is one of the most important factors affecting the human life span. Elderly people are more harmed by bacterial infections due to their deficits in immunity. Because of the lack of new antibiotics in recent years, bacterial resistance has increasingly become a serious problem globally. In this study, an antibacterial compound predictor was constructed using the support vector machines and random forest methods and the data of the active and inactive antibacterial compounds from the ChEMBL database. The results showed that both models have excellent prediction performance (mean accuracy >0.9 and mean AUC >0.9 for the two models). We used the predictor to screen potential antibacterial compounds from FDA-approved drugs in the DrugBank database. The screening results showed that 1087 small-molecule drugs have potential antibacterial activity and 154 of them are FDA-approved antibacterial drugs, which accounts for 76.2% of the approved antibacterial drugs collected in this study. Through molecular fingerprint similarity analysis and common substructure analysis, we screened 8 predicted antibacterial small-molecule compounds with novel structures compared with known antibacterial drugs, and 5 of them are widely used in the treatment of various tumors. This study provides a new insight for predicting antibacterial compounds by using approved drugs, the predicted compounds might be used to treat bacterial infections and extend lifespan.
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Affiliation(s)
- Wen-Xing Li
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, Guangdong, China.,Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Xin Tong
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| | - Peng-Peng Yang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| | - Yang Zheng
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| | - Ji-Hao Liang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| | - Gong-Hua Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, Yunnan, China
| | - Dahai Liu
- School of Medicine, Foshan University, Foshan 528000, Guangdong, China
| | - Dao-Gang Guan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, Guangdong, China.,Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Shao-Xing Dai
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
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12
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A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria. J Antibiot (Tokyo) 2021; 74:838-849. [PMID: 34522024 DOI: 10.1038/s41429-021-00471-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 05/27/2021] [Accepted: 07/16/2021] [Indexed: 02/08/2023]
Abstract
Antimicrobials have paved the way for medical and social development over the last century and are indispensable for treating infections in humans and animals. The dramatic spread and diversity of antibiotic-resistant pathogens have significantly reduced the efficacy of essentially all antibiotic classes and is a global problem affecting human and animal health. Antimicrobial resistance is influenced by complex factors such as resistance genes and dosing, which are highly nonlinear, time-lagged and multivariate coupled, and the amount of resistance data is large and redundant, making it difficult to predict and analyze. Based on machine learning methods and data mining techniques, this paper reviews (1) antimicrobial resistance data storage and analysis techniques, (2) antimicrobial resistance assessment methods and the associated risk assessment methods for antimicrobial resistance, and (3) antimicrobial resistance prediction methods. Finally, the current research results on antimicrobial resistance and the development trend are summarized to provide a systematic and comprehensive reference for the research on antimicrobial resistance.
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13
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Diéguez-Santana K, González-Díaz H. Towards machine learning discovery of dual antibacterial drug-nanoparticle systems. NANOSCALE 2021; 13:17854-17870. [PMID: 34671801 DOI: 10.1039/d1nr04178a] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Artificial Intelligence/Machine Learning (AI/ML) algorithms may speed up the design of DADNP systems formed by Antibacterial Drugs (AD) and Nanoparticles (NP). In this work, we used IFPTML = Information Fusion (IF) + Perturbation-Theory (PT) + Machine Learning (ML) algorithm for the first time to study of a large dataset of putative DADNP systems composed by >165 000 ChEMBL AD assays and 300 NP assays vs. multiple bacteria species. We trained alternative models with Linear Discriminant Analysis (LDA), Artificial Neural Networks (ANN), Bayesian Networks (BNN), K-Nearest Neighbour (KNN) and other algorithms. IFPTML-LDA model was simpler with values of Sp ≈ 90% and Sn ≈ 74% in both training (>124 K cases) and validation (>41 K cases) series. IFPTML-ANN and KNN models are notably more complicated even when they are more balanced Sn ≈ Sp ≈ 88.5%-99.0% and AUROC ≈ 0.94-0.99 in both series. We also carried out a simulation (>1900 calculations) of the expected behavior for putative DADNPs in 72 different biological assays. The putative DADNPs studied are formed by 27 different drugs with multiple classes of NP and types of coats. In addition, we tested the validity of our additive model with 80 DADNP complexes experimentally synthetized and biologically tested (reported in >45 papers). All these DADNPs show values of MIC < 50 μg mL-1 (cutoff used) better that MIC of AD and NP alone (synergistic or additive effect). The assays involve DADNP complexes with 10 types of NP, 6 coating materials, NP size range 5-100 nm vs. 15 different antibiotics, and 12 bacteria species. The IFPTML-LDA model classified correctly 100% (80 out of 80) DADNP complexes as biologically active. IFPMTL additive strategy may become a useful tool to assist the design of DADNP systems for antibacterial therapy taking into consideration only information about AD and NP components by separate.
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Affiliation(s)
- Karel Diéguez-Santana
- 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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14
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Randall JR, Davies BW. Mining for novel antibiotics. Curr Opin Microbiol 2021; 63:66-69. [PMID: 34217916 PMCID: PMC8463434 DOI: 10.1016/j.mib.2021.06.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 06/10/2021] [Accepted: 06/11/2021] [Indexed: 10/20/2022]
Affiliation(s)
- Justin R Randall
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA
| | - Bryan W Davies
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA.
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15
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Melo MCR, Maasch JRMA, de la Fuente-Nunez C. Accelerating antibiotic discovery through artificial intelligence. Commun Biol 2021; 4:1050. [PMID: 34504303 PMCID: PMC8429579 DOI: 10.1038/s42003-021-02586-0] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 07/16/2021] [Indexed: 02/07/2023] Open
Abstract
By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.
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Affiliation(s)
- Marcelo C R Melo
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Jacqueline R M A Maasch
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
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16
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Miethke M, Pieroni M, Weber T, Brönstrup M, Hammann P, Halby L, Arimondo PB, Glaser P, Aigle B, Bode HB, Moreira R, Li Y, Luzhetskyy A, Medema MH, Pernodet JL, Stadler M, Tormo JR, Genilloud O, Truman AW, Weissman KJ, Takano E, Sabatini S, Stegmann E, Brötz-Oesterhelt H, Wohlleben W, Seemann M, Empting M, Hirsch AKH, Loretz B, Lehr CM, Titz A, Herrmann J, Jaeger T, Alt S, Hesterkamp T, Winterhalter M, Schiefer A, Pfarr K, Hoerauf A, Graz H, Graz M, Lindvall M, Ramurthy S, Karlén A, van Dongen M, Petkovic H, Keller A, Peyrane F, Donadio S, Fraisse L, Piddock LJV, Gilbert IH, Moser HE, Müller R. Towards the sustainable discovery and development of new antibiotics. Nat Rev Chem 2021; 5:726-749. [PMID: 37118182 PMCID: PMC8374425 DOI: 10.1038/s41570-021-00313-1] [Citation(s) in RCA: 393] [Impact Index Per Article: 131.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/01/2021] [Indexed: 02/08/2023]
Abstract
An ever-increasing demand for novel antimicrobials to treat life-threatening infections caused by the global spread of multidrug-resistant bacterial pathogens stands in stark contrast to the current level of investment in their development, particularly in the fields of natural-product-derived and synthetic small molecules. New agents displaying innovative chemistry and modes of action are desperately needed worldwide to tackle the public health menace posed by antimicrobial resistance. Here, our consortium presents a strategic blueprint to substantially improve our ability to discover and develop new antibiotics. We propose both short-term and long-term solutions to overcome the most urgent limitations in the various sectors of research and funding, aiming to bridge the gap between academic, industrial and political stakeholders, and to unite interdisciplinary expertise in order to efficiently fuel the translational pipeline for the benefit of future generations. ![]()
Antimicrobial resistance is an increasing threat to public health and encouraging the development of new antimicrobials is one of the most important ways to address the problem. This Roadmap article aims to bring together industrial, academic and political partners, and proposes both short-term and long-term solutions to this challenge.
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Affiliation(s)
- Marcus Miethke
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI), and Department of Pharmacy, Saarland University Campus E8.1, Saarbrücken, Germany.,German Center for Infection Research (DZIF), Braunschweig, Germany
| | - Marco Pieroni
- Food and Drug Department, University of Parma, Parma, Italy
| | - Tilmann Weber
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Mark Brönstrup
- German Center for Infection Research (DZIF), Braunschweig, Germany.,Department of Chemical Biology (CBIO), Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany
| | - Peter Hammann
- Infectious Diseases & Natural Product Research at EVOTEC, and Justus Liebig University Giessen, Giessen, Germany
| | - Ludovic Halby
- Epigenetic Chemical Biology, Department of Structural Biology and Chemistry, Institut Pasteur, UMR n°3523, CNRS, Paris, France
| | - Paola B Arimondo
- Epigenetic Chemical Biology, Department of Structural Biology and Chemistry, Institut Pasteur, UMR n°3523, CNRS, Paris, France
| | - Philippe Glaser
- Ecology and Evolution of Antibiotic Resistance Unit, Microbiology Department, Institut Pasteur, CNRS UMR3525, Paris, France
| | | | - Helge B Bode
- Department of Biosciences, Goethe University Frankfurt, Frankfurt, Germany.,Max Planck Institute for Terrestrial Microbiology, Department of Natural Products in Organismic Interactions, Marburg, Germany
| | - Rui Moreira
- Faculty of Pharmacy, University of Lisbon, Lisbon, Portugal
| | - Yanyan Li
- Unit MCAM, CNRS, National Museum of Natural History (MNHN), Paris, France
| | - Andriy Luzhetskyy
- Pharmaceutical Biotechnology, Saarland University, Saarbrücken, Germany
| | - Marnix H Medema
- Bioinformatics Group, Wageningen University and Research, Wageningen, Netherlands
| | - Jean-Luc Pernodet
- Institute for Integrative Biology of the Cell (I2BC) & Microbiology Department, University of Paris-Saclay, Gif-sur-Yvette, France
| | - Marc Stadler
- German Center for Infection Research (DZIF), Braunschweig, Germany.,Microbial Drugs (MWIS), Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany
| | | | | | - Andrew W Truman
- Department of Molecular Microbiology, John Innes Centre, Norwich, United Kingdom
| | - Kira J Weissman
- Molecular and Structural Enzymology Group, Université de Lorraine, CNRS, IMoPA, Nancy, France
| | - Eriko Takano
- Manchester Institute of Biotechnology, Department of Chemistry, School of Natural Sciences, Faculty of Science and Engineering, University of Manchester, Manchester, United Kingdom
| | - Stefano Sabatini
- Department of Pharmaceutical Sciences, University of Perugia, Perugia, Italy
| | - Evi Stegmann
- German Center for Infection Research (DZIF), Braunschweig, Germany.,Department of Microbial Bioactive Compounds, Interfaculty Institute of Microbiology and Infection Medicine, University of Tübingen, Tübingen, Germany
| | - Heike Brötz-Oesterhelt
- German Center for Infection Research (DZIF), Braunschweig, Germany.,Department of Microbial Bioactive Compounds, Interfaculty Institute of Microbiology and Infection Medicine, University of Tübingen, Tübingen, Germany
| | - Wolfgang Wohlleben
- German Center for Infection Research (DZIF), Braunschweig, Germany.,Department of Microbiology/Biotechnology, Interfaculty Institute of Microbiology and Infection Medicine, University of Tübingen, Tübingen, Germany
| | - Myriam Seemann
- Institute for Chemistry UMR 7177, University of Strasbourg/CNRS, ITI InnoVec, Strasbourg, France
| | - Martin Empting
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI), and Department of Pharmacy, Saarland University Campus E8.1, Saarbrücken, Germany.,German Center for Infection Research (DZIF), Braunschweig, Germany
| | - Anna K H Hirsch
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI), and Department of Pharmacy, Saarland University Campus E8.1, Saarbrücken, Germany.,German Center for Infection Research (DZIF), Braunschweig, Germany
| | - Brigitta Loretz
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI), and Department of Pharmacy, Saarland University Campus E8.1, Saarbrücken, Germany
| | - Claus-Michael Lehr
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI), and Department of Pharmacy, Saarland University Campus E8.1, Saarbrücken, Germany
| | - Alexander Titz
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI), and Department of Pharmacy, Saarland University Campus E8.1, Saarbrücken, Germany.,German Center for Infection Research (DZIF), Braunschweig, Germany
| | - Jennifer Herrmann
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI), and Department of Pharmacy, Saarland University Campus E8.1, Saarbrücken, Germany.,German Center for Infection Research (DZIF), Braunschweig, Germany
| | - Timo Jaeger
- German Center for Infection Research (DZIF), Braunschweig, Germany
| | - Silke Alt
- German Center for Infection Research (DZIF), Braunschweig, Germany
| | | | | | - Andrea Schiefer
- German Center for Infection Research (DZIF), Braunschweig, Germany.,Institute of Medical Microbiology, Immunology and Parasitology (IMMIP), University Hospital Bonn, Bonn, Germany
| | - Kenneth Pfarr
- German Center for Infection Research (DZIF), Braunschweig, Germany.,Institute of Medical Microbiology, Immunology and Parasitology (IMMIP), University Hospital Bonn, Bonn, Germany
| | - Achim Hoerauf
- German Center for Infection Research (DZIF), Braunschweig, Germany.,Institute of Medical Microbiology, Immunology and Parasitology (IMMIP), University Hospital Bonn, Bonn, Germany
| | - Heather Graz
- Biophys Ltd., Usk, Monmouthshire, United Kingdom
| | - Michael Graz
- School of Law, University of Bristol, Bristol, United Kingdom
| | | | | | - Anders Karlén
- Department of Medicinal Chemistry, Uppsala University, Uppsala, Sweden
| | | | - Hrvoje Petkovic
- Department of Food Science and Technology, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Andreas Keller
- Chair for Clinical Bioinformatics, Saarland University, University Hospital, Saarbrücken, Germany
| | | | | | - Laurent Fraisse
- Drugs for Neglected Diseases initiative (DNDi), Geneva, Switzerland
| | - Laura J V Piddock
- The Global Antibiotic Research and Development Partnership (GARDP), Geneva, Switzerland
| | - Ian H Gilbert
- Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, United Kingdom
| | - Heinz E Moser
- Novartis Institutes for BioMedical Research (NIBR), Emeryville, CA USA
| | - Rolf Müller
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI), and Department of Pharmacy, Saarland University Campus E8.1, Saarbrücken, Germany.,German Center for Infection Research (DZIF), Braunschweig, Germany
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17
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Mirzaei M, Furxhi I, Murphy F, Mullins M. A Machine Learning Tool to Predict the Antibacterial Capacity of Nanoparticles. NANOMATERIALS (BASEL, SWITZERLAND) 2021; 11:1774. [PMID: 34361160 PMCID: PMC8308172 DOI: 10.3390/nano11071774] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/13/2021] [Accepted: 07/06/2021] [Indexed: 12/22/2022]
Abstract
The emergence and rapid spread of multidrug-resistant bacteria strains are a public health concern. This emergence is caused by the overuse and misuse of antibiotics leading to the evolution of antibiotic-resistant strains. Nanoparticles (NPs) are objects with all three external dimensions in the nanoscale that varies from 1 to 100 nm. Research on NPs with enhanced antimicrobial activity as alternatives to antibiotics has grown due to the increased incidence of nosocomial and community acquired infections caused by pathogens. Machine learning (ML) tools have been used in the field of nanoinformatics with promising results. As a consequence of evident achievements on a wide range of predictive tasks, ML techniques are attracting significant interest across a variety of stakeholders. In this article, we present an ML tool that successfully predicts the antibacterial capacity of NPs while the model's validation demonstrates encouraging results (R2 = 0.78). The data were compiled after a literature review of 60 articles and consist of key physico-chemical (p-chem) properties and experimental conditions (exposure variables and bacterial clustering) from in vitro studies. Following data homogenization and pre-processing, we trained various regression algorithms and we validated them using diverse performance metrics. Finally, an important attribute evaluation, which ranks the attributes that are most important in predicting the outcome, was performed. The attribute importance revealed that NP core size, the exposure dose, and the species of bacterium are key variables in predicting the antibacterial effect of NPs. This tool assists various stakeholders and scientists in predicting the antibacterial effects of NPs based on their p-chem properties and diverse exposure settings. This concept also aids the safe-by-design paradigm by incorporating functionality tools.
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Affiliation(s)
- Mahsa Mirzaei
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (M.M.); (F.M.); (M.M.)
| | - Irini Furxhi
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (M.M.); (F.M.); (M.M.)
- Transgero Limited, Cullinagh, Newcastle West, V42V384 Limerick, Ireland
| | - Finbarr Murphy
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (M.M.); (F.M.); (M.M.)
- Transgero Limited, Cullinagh, Newcastle West, V42V384 Limerick, Ireland
| | - Martin Mullins
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (M.M.); (F.M.); (M.M.)
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18
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Nguyen LP, Park CS, Pinto NA, Lee H, Seo HS, Vu TN, Mai H, Pham AHT, Jang E, Cho YL, Goglin K, Nguyen K, White R, D’Souza R, Fouts DE, Yong D. In Vitro Activity of a Novel Siderophore-Cephalosporin LCB10-0200 (GT-1), and LCB10-0200/Avibactam, against Carbapenem-Resistant Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa Strains at a Tertiary Hospital in Korea. Pharmaceuticals (Basel) 2021; 14:370. [PMID: 33923801 PMCID: PMC8072773 DOI: 10.3390/ph14040370] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/12/2021] [Accepted: 04/14/2021] [Indexed: 12/17/2022] Open
Abstract
The siderophore-antibiotic conjugate LCB10-0200 (a.k.a. GT-1) has been developed to combat multidrug-resistant Gram-negative bacteria. In this study, the in vitro activity of LCB10-0200 and LCB10-0200/avibactam (AVI) has been investigated against carbapenem-resistant Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa. Minimal inhibitory concentrations (MICs) of LCB10-0200, LCB10-0200/AVI, aztreonam, aztreonam/AVI, ceftazidime, ceftazidime/AVI, and meropenem were measured using the agar dilution method. Whole genome sequencing was performed using Illumina and the resistome was analyzed. LCB10-0200 displayed stronger activity than the comparator drugs in meropenem-resistant E. coli and K. pneumoniae, and the addition of AVI enhanced the LCB10-0200 activity to MIC ≤ 0.12 mg/L for 90.5% of isolates. In contrast, whereas LCB10-0200 alone showed potent activity against meropenem-resistant A. baumannii and P. aeruginosa at MIC ≤ 4 mg/L for 84.3% of isolates, the combination with AVI did not improve its activity. LCB10-0200/AVI was active against CTX-M-, SHV-, CMY-, and KPC- producing E. coli and K. pneumoniae, while LCB10-0200 alone was active against ADC-, OXA-, and VIM- producing A. baumannii and P. aeruginosa. Both LCB10-0200 and LCB10-0200/AVI displayed low activity against IMP- and NDM- producing strains. LCB10-0200 alone exhibited strong activity against selected strains. The addition of AVI significantly increased LCB10-0200 activity against carbapenem-resistant E. coli, K. pneumoniae.
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Affiliation(s)
- Le Phuong Nguyen
- Department of Laboratory Medicine and Research Institute of Bacterial Resistance, Yonsei University College of Medicine, Seoul 03722, Korea; (L.P.N.); (C.S.P.); (N.A.P.); (H.L.); (H.S.S.); (T.N.V.); (E.J.)
- Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul 03722, Korea
| | - Chul Soon Park
- Department of Laboratory Medicine and Research Institute of Bacterial Resistance, Yonsei University College of Medicine, Seoul 03722, Korea; (L.P.N.); (C.S.P.); (N.A.P.); (H.L.); (H.S.S.); (T.N.V.); (E.J.)
| | - Naina Adren Pinto
- Department of Laboratory Medicine and Research Institute of Bacterial Resistance, Yonsei University College of Medicine, Seoul 03722, Korea; (L.P.N.); (C.S.P.); (N.A.P.); (H.L.); (H.S.S.); (T.N.V.); (E.J.)
- Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul 03722, Korea
| | - Hyunsook Lee
- Department of Laboratory Medicine and Research Institute of Bacterial Resistance, Yonsei University College of Medicine, Seoul 03722, Korea; (L.P.N.); (C.S.P.); (N.A.P.); (H.L.); (H.S.S.); (T.N.V.); (E.J.)
- Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul 03722, Korea
| | - Hyun Soo Seo
- Department of Laboratory Medicine and Research Institute of Bacterial Resistance, Yonsei University College of Medicine, Seoul 03722, Korea; (L.P.N.); (C.S.P.); (N.A.P.); (H.L.); (H.S.S.); (T.N.V.); (E.J.)
| | - Thao Nguyen Vu
- Department of Laboratory Medicine and Research Institute of Bacterial Resistance, Yonsei University College of Medicine, Seoul 03722, Korea; (L.P.N.); (C.S.P.); (N.A.P.); (H.L.); (H.S.S.); (T.N.V.); (E.J.)
- Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul 03722, Korea
| | - Hung Mai
- School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - An H. T. Pham
- UCI School of Biological Sciences, University of California, Irvine, CA 92617, USA;
| | - Eris Jang
- Department of Laboratory Medicine and Research Institute of Bacterial Resistance, Yonsei University College of Medicine, Seoul 03722, Korea; (L.P.N.); (C.S.P.); (N.A.P.); (H.L.); (H.S.S.); (T.N.V.); (E.J.)
| | | | | | - Kevin Nguyen
- J. Craig Venter Institute, Rockville, MD 20850, USA; (K.N.); (R.W.); (R.D.); (D.E.F.)
| | - Richard White
- J. Craig Venter Institute, Rockville, MD 20850, USA; (K.N.); (R.W.); (R.D.); (D.E.F.)
| | - Roshan D’Souza
- J. Craig Venter Institute, Rockville, MD 20850, USA; (K.N.); (R.W.); (R.D.); (D.E.F.)
| | - Derrick E. Fouts
- J. Craig Venter Institute, Rockville, MD 20850, USA; (K.N.); (R.W.); (R.D.); (D.E.F.)
| | - Dongeun Yong
- Department of Laboratory Medicine and Research Institute of Bacterial Resistance, Yonsei University College of Medicine, Seoul 03722, Korea; (L.P.N.); (C.S.P.); (N.A.P.); (H.L.); (H.S.S.); (T.N.V.); (E.J.)
- Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul 03722, Korea
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19
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Taguchi AT, Boyd J, Diehnelt CW, Legutki JB, Zhao ZG, Woodbury NW. Comprehensive Prediction of Molecular Recognition in a Combinatorial Chemical Space Using Machine Learning. ACS COMBINATORIAL SCIENCE 2020; 22:500-508. [PMID: 32786325 DOI: 10.1021/acscombsci.0c00003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
In combinatorial chemical approaches, optimizing the composition and arrangement of building blocks toward a particular function has been done using a number of methods, including high throughput molecular screening, molecular evolution, and computational prescreening. Here, a different approach is considered that uses sparse measurements of library molecules as the input to a machine learning algorithm which generates a comprehensive, quantitative relationship between covalent molecular structure and function that can then be used to predict the function of any molecule in the possible combinatorial space. To test the feasibility of the approach, a defined combinatorial chemical space consisting of ∼1012 possible linear combinations of 16 different amino acids was used. The binding of a very sparse, but nearly random, sampling of this amino acid sequence space to 9 different protein targets is measured and used to generate a general relationship between peptide sequence and binding for each target. Surprisingly, measuring as little as a few hundred to a few thousand of the ∼1012 possible molecules provides sufficient training to be highly predictive of the binding of the remaining molecules in the combinatorial space. Furthermore, measuring only amino acid sequences that bind weakly to a target allows the accurate prediction of which sequences will bind 10-100 times more strongly. Thus, the molecular recognition information contained in a tiny fraction of molecules in this combinatorial space is sufficient to characterize any set of molecules randomly selected from the entire space, a fact that potentially has significant implications for the design of new chemical function using combinatorial chemical libraries.
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Affiliation(s)
| | - James Boyd
- HealthTell, Inc., 145 S 79th Street, Chandler, Arizona 85226, United States
| | - Chris W. Diehnelt
- Center for Innovations in Medicine at the Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
| | - Joseph B. Legutki
- HealthTell, Inc., 145 S 79th Street, Chandler, Arizona 85226, United States
| | - Zhan-Gong Zhao
- Center for Innovations in Medicine at the Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
| | - Neal W. Woodbury
- Center for Innovations in Medicine at the Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
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20
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Serafim MSM, Kronenberger T, Oliveira PR, Poso A, Honório KM, Mota BEF, Maltarollo VG. The application of machine learning techniques to innovative antibacterial discovery and development. Expert Opin Drug Discov 2020; 15:1165-1180. [PMID: 32552005 DOI: 10.1080/17460441.2020.1776696] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION After the initial wave of antibiotic discovery, few novel classes of antibiotics have emerged, with the latest dating back to the 1980's. Furthermore, the pace of antibiotic drug discovery is unable to keep up with the increasing prevalence of antibiotic drug resistance. However, the increasing amount of available data promotes the use of machine learning techniques (MLT) in drug discovery projects (e.g. construction of regression/classification models and ranking/virtual screening of compounds). AREAS COVERED In this review, the authors cover some of the applications of MLT in medicinal chemistry, focusing on the development of new antibiotics, the prediction of resistance and its mechanisms. The aim of this review is to illustrate the main advantages and disadvantages and the major trends from studies over the past 5 years. EXPERT OPINION The application of MLT to antibacterial drug discovery can aid the selection of new and potent lead compounds, with desirable pharmacokinetic and toxic profiles for further optimization. The increasing volume of available data along with the constant improvement in computational power and algorithms has meant that we are experiencing a transition in the way we face modern issues such as drug resistance, where our decisions are data-driven and experiments can be focused by data-suggested hypotheses.
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Affiliation(s)
- Mateus Sá Magalhães Serafim
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG) , Belo Horizonte, Brazil
| | - Thales Kronenberger
- Department of Internal Medicine VIII, University Hospital of Tübingen , Tübingen, Germany
| | | | - Antti Poso
- Department of Internal Medicine VIII, University Hospital of Tübingen , Tübingen, Germany.,School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland , Kuopio, Finland
| | - Káthia Maria Honório
- Escola de Artes, Ciências e Humanidades, Universidade de São Paulo (USP) , São Paulo, Brazil.,Centro de Ciências Naturais e Humanas, Universidade Federal do ABC , Santo André, Brazil
| | - Bruno Eduardo Fernandes Mota
- Departamento de Análises Clínicas e Toxicológicas, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG) , Belo Horizonte, Brazil
| | - Vinícius Gonçalves Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG) , Belo Horizonte, Brazil
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21
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Shen C, Hu Y, Wang Z, Zhang X, Pang J, Wang G, Zhong H, Xu L, Cao D, Hou T. Beware of the generic machine learning-based scoring functions in structure-based virtual screening. Brief Bioinform 2020; 22:5850047. [PMID: 32484221 DOI: 10.1093/bib/bbaa070] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 04/17/2020] [Accepted: 03/30/2020] [Indexed: 12/14/2022] Open
Abstract
Machine learning-based scoring functions (MLSFs) have attracted extensive attention recently and are expected to be potential rescoring tools for structure-based virtual screening (SBVS). However, a major concern nowadays is whether MLSFs trained for generic uses rather than a given target can consistently be applicable for VS. In this study, a systematic assessment was carried out to re-evaluate the effectiveness of 14 reported MLSFs in VS. Overall, most of these MLSFs could hardly achieve satisfactory results for any dataset, and they could even not outperform the baseline of classical SFs such as Glide SP. An exception was observed for RFscore-VS trained on the Directory of Useful Decoys-Enhanced dataset, which showed its superiority for most targets. However, in most cases, it clearly illustrated rather limited performance on the targets that were dissimilar to the proteins in the corresponding training sets. We also used the top three docking poses rather than the top one for rescoring and retrained the models with the updated versions of the training set, but only minor improvements were observed. Taken together, generic MLSFs may have poor generalization capabilities to be applicable for the real VS campaigns. Therefore, it should be quite cautious to use this type of methods for VS.
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Affiliation(s)
| | - Ye Hu
- Central South University, China
| | | | | | | | | | | | - Lei Xu
- Central South University, China
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22
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Russell LE, Schleiff MA, Gonzalez E, Bart AG, Broccatelli F, Hartman JH, Humphreys WG, Lauschke VM, Martin I, Nwabufo C, Prasad B, Scott EE, Segall M, Takahashi R, Taub ME, Sodhi JK. Advances in the study of drug metabolism - symposium report of the 12th Meeting of the International Society for the Study of Xenobiotics (ISSX). Drug Metab Rev 2020; 52:395-407. [PMID: 32456484 DOI: 10.1080/03602532.2020.1765793] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The 12th International Society for the Study of Xenobiotics (ISSX) meeting, held in Portland, OR, USA from July 28 to 31, 2019, was attended by diverse members of the pharmaceutical sciences community. The ISSX New Investigators Group provides learning and professional growth opportunities for student and early career members of ISSX. To share meeting content with those who were unable to attend, the ISSX New Investigators herein elected to highlight the "Advances in the Study of Drug Metabolism" symposium, as it engaged attendees with diverse backgrounds. This session covered a wide range of current topics in drug metabolism research including predicting sites and routes of metabolism, metabolite identification, ligand docking, and medicinal and natural products chemistry, and highlighted approaches complemented by computational modeling. In silico tools have been increasingly applied in both academic and industrial settings, alongside traditional and evolving in vitro techniques, to strengthen and streamline pharmaceutical research. Approaches such as quantum mechanics simulations facilitate understanding of reaction energetics toward prediction of routes and sites of drug metabolism. Furthermore, in tandem with crystallographic and orthogonal wet lab techniques for structural validation of drug metabolizing enzymes, in silico models can aid understanding of substrate recognition by particular enzymes, identify metabolic soft spots and predict toxic metabolites for improved molecular design. Of note, integration of chemical synthesis and biosynthesis using natural products remains an important approach for identifying new chemical scaffolds in drug discovery. These subjects, compiled by the symposium organizers, presenters, and the ISSX New Investigators Group, are discussed in this review.
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Affiliation(s)
- Laura E Russell
- Department of Physiology and Pharmacology, University of Western Ontario, London, ON, Canada
| | - Mary Alexandra Schleiff
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Eric Gonzalez
- Division of Pre-Clinical Innovation, Therapeutic Development Branch, National Center for Advancing Translational Sciences, Bethesda, MD, USA.,Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Aaron G Bart
- Program in Biophysics, University of Michigan, Ann Arbor, MI, USA
| | - Fabio Broccatelli
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., South San Francisco, CA, USA
| | - Jessica H Hartman
- Nicholas School of the Environment, Duke University, Durham, NC, USA
| | | | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | | | | | - Bhagwat Prasad
- College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, WA, USA
| | - Emily E Scott
- Program in Biophysics, University of Michigan, Ann Arbor, MI, USA.,Department of Medicinal Chemistry and Pharmacology, University of Michigan, Ann Arbor, MI, USA
| | | | | | - Mitchell E Taub
- Department of Drug Metabolism and Pharmacokinetics, Boehringer Ingelheim Pharmaceuticals, Ridgefield, CT, USA
| | - Jasleen K Sodhi
- Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California, San Francisco, CA, USA
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Keyvanpour MR, Shirzad MB. An Analysis of QSAR Research Based on Machine Learning Concepts. Curr Drug Discov Technol 2020; 18:17-30. [PMID: 32178612 DOI: 10.2174/1570163817666200316104404] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 08/22/2019] [Accepted: 10/28/2019] [Indexed: 11/22/2022]
Abstract
Quantitative Structure-Activity Relationship (QSAR) is a popular approach developed to correlate chemical molecules with their biological activities based on their chemical structures. Machine learning techniques have proved to be promising solutions to QSAR modeling. Due to the significant role of machine learning strategies in QSAR modeling, this area of research has attracted much attention from researchers. A considerable amount of literature has been published on machine learning based QSAR modeling methodologies whilst this domain still suffers from lack of a recent and comprehensive analysis of these algorithms. This study systematically reviews the application of machine learning algorithms in QSAR, aiming to provide an analytical framework. For this purpose, we present a framework called 'ML-QSAR'. This framework has been designed for future research to: a) facilitate the selection of proper strategies among existing algorithms according to the application area requirements, b) help to develop and ameliorate current methods and c) providing a platform to study existing methodologies comparatively. In ML-QSAR, first a structured categorization is depicted which studied the QSAR modeling research based on machine models. Then several criteria are introduced in order to assess the models. Finally, inspired by aforementioned criteria the qualitative analysis is carried out.
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Affiliation(s)
| | - Mehrnoush Barani Shirzad
- Data Mining Research Laboratory, Department of Computer Engineering, Alzahra University, Tehran, Iran
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24
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Peiffer-Smadja N, Rawson TM, Ahmad R, Buchard A, Georgiou P, Lescure FX, Birgand G, Holmes AH. Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clin Microbiol Infect 2019; 26:584-595. [PMID: 31539636 DOI: 10.1016/j.cmi.2019.09.009] [Citation(s) in RCA: 178] [Impact Index Per Article: 35.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 08/29/2019] [Accepted: 09/09/2019] [Indexed: 12/22/2022]
Abstract
BACKGROUND Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID). OBJECTIVES We aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID. SOURCES References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019. CONTENT We found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n = 24, 40%), ID consultation (n = 15, 25%), medical or surgical wards (n = 13, 20%), emergency department (n = 4, 7%), primary care (n = 3, 5%) and antimicrobial stewardship (n = 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low- and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%). IMPLICATIONS Considering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.
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Affiliation(s)
- N Peiffer-Smadja
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK; French Institute for Medical Research (Inserm), Infection Antimicrobials Modelling Evolution (IAME), UMR 1137, University Paris Diderot, Paris, France.
| | - T M Rawson
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | - R Ahmad
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | | | - P Georgiou
- Department of Electrical and Electronic Engineering, Imperial College, London, UK
| | - F-X Lescure
- French Institute for Medical Research (Inserm), Infection Antimicrobials Modelling Evolution (IAME), UMR 1137, University Paris Diderot, Paris, France; Infectious Diseases Department, Bichat-Claude Bernard Hospital, Assistance-Publique Hôpitaux de Paris, Paris, France
| | - G Birgand
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | - A H Holmes
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
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25
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Shen C, Ding J, Wang Z, Cao D, Ding X, Hou T. From machine learning to deep learning: Advances in scoring functions for protein–ligand docking. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2019. [DOI: 10.1002/wcms.1429] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Chao Shen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University Hangzhou P. R. China
| | - Junjie Ding
- Beijing Institute of Pharmaceutical Chemistry Beijing P. R. China
| | - Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University Hangzhou P. R. China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University Changsha P. R. China
| | - Xiaoqin Ding
- Beijing Institute of Pharmaceutical Chemistry Beijing P. R. China
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University Hangzhou P. R. China
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26
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Stephenson N, Shane E, Chase J, Rowland J, Ries D, Justice N, Zhang J, Chan L, Cao R. Survey of Machine Learning Techniques in Drug Discovery. Curr Drug Metab 2019; 20:185-193. [DOI: 10.2174/1389200219666180820112457] [Citation(s) in RCA: 111] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 01/01/2018] [Accepted: 03/19/2018] [Indexed: 12/19/2022]
Abstract
Background:Drug discovery, which is the process of discovering new candidate medications, is very important for pharmaceutical industries. At its current stage, discovering new drugs is still a very expensive and time-consuming process, requiring Phases I, II and III for clinical trials. Recently, machine learning techniques in Artificial Intelligence (AI), especially the deep learning techniques which allow a computational model to generate multiple layers, have been widely applied and achieved state-of-the-art performance in different fields, such as speech recognition, image classification, bioinformatics, etc. One very important application of these AI techniques is in the field of drug discovery.Methods:We did a large-scale literature search on existing scientific websites (e.g, ScienceDirect, Arxiv) and startup companies to understand current status of machine learning techniques in drug discovery.Results:Our experiments demonstrated that there are different patterns in machine learning fields and drug discovery fields. For example, keywords like prediction, brain, discovery, and treatment are usually in drug discovery fields. Also, the total number of papers published in drug discovery fields with machine learning techniques is increasing every year.Conclusion:The main focus of this survey is to understand the current status of machine learning techniques in the drug discovery field within both academic and industrial settings, and discuss its potential future applications. Several interesting patterns for machine learning techniques in drug discovery fields are discussed in this survey.
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Affiliation(s)
- Natalie Stephenson
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - Emily Shane
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - Jessica Chase
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - Jason Rowland
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - David Ries
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - Nicola Justice
- Department of Mathematics, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - Jie Zhang
- Key Laboratory of Hebei Province for Plant Physiology and Molecular Pathology, College of Life Sciences, Hebei Agricultural University, Baoding, China
| | - Leong Chan
- School of Business, Pacific Lutheran University, Tacoma, WA 98447, United States
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States
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27
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Nava Lara RA, Aguilera-Mendoza L, Brizuela CA, Peña A, Del Rio G. Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs. Molecules 2019; 24:molecules24071258. [PMID: 30935109 PMCID: PMC6479866 DOI: 10.3390/molecules24071258] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 03/09/2019] [Accepted: 03/14/2019] [Indexed: 12/13/2022] Open
Abstract
The emergence of microbes resistant to common antibiotics represent a current treat to human health. It has been recently recognized that non-antibiotic labeled drugs may promote antibiotic-resistance mechanisms in the human microbiome by presenting a secondary antibiotic activity; hence, the development of computer-assisted procedures to identify antibiotic activity in human-targeted compounds may assist in preventing the emergence of resistant microbes. In this regard, it is worth noting that while most antibiotics used to treat human infectious diseases are non-peptidic compounds, most known antimicrobials nowadays are peptides, therefore all computer-based models aimed to predict antimicrobials either use small datasets of non-peptidic compounds rendering predictions with poor reliability or they predict antimicrobial peptides that are not currently used in humans. Here we report a machine-learning-based approach trained to identify gut antimicrobial compounds; a unique aspect of our model is the use of heterologous training sets, in which peptide and non-peptide antimicrobial compounds were used to increase the size of the training data set. Our results show that combining peptide and non-peptide antimicrobial compounds rendered the best classification of gut antimicrobial compounds. Furthermore, this classification model was tested on the latest human-approved drugs expecting to identify antibiotics with broad-spectrum activity and our results show that the model rendered predictions consistent with current knowledge about broad-spectrum antibiotics. Therefore, heterologous machine learning rendered an efficient computational approach to classify antimicrobial compounds.
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Affiliation(s)
- Rodrigo A Nava Lara
- Department of biochemistry and structural biology, Instituto de Fisiología Celular, UNAM, Mexico City 04510, Mexico.
| | | | - Carlos A Brizuela
- Computer Science Department, CICESE Research Center, Ensenada, Baja California 22860, Mexico.
| | - Antonio Peña
- Department of genetics, Instituto de Fisiología Celular, UNAM, Mexico City 04510, Mexico.
| | - Gabriel Del Rio
- Department of biochemistry and structural biology, Instituto de Fisiología Celular, UNAM, Mexico City 04510, Mexico.
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28
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Jiang X, Qian K, Liu G, Sun L, Zhou G, Li J, Fang X, Ge H, Lv Z. Design and activity study of a melittin-thanatin hybrid peptide. AMB Express 2019; 9:14. [PMID: 30701481 PMCID: PMC6353975 DOI: 10.1186/s13568-019-0739-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 01/23/2019] [Indexed: 12/22/2022] Open
Abstract
The unique antimicrobial mechanism of antimicrobials make them a promising substitute for antibiotics for fighting drug-resistant bacteria. Both melittin and thanatin have antimicrobial bioactivity. However, thanatin does not inhibit the growth of Staphylococcus aureus. Melittin can inhibit S. aureus and has strong hemolytic activity. In the present study, the mutant fragments of melittin and thanatin were combined by flexible peptides to form a novel hybrid peptide, which was synthesized based on the secondary and tertiary structure prediction. The hybrid peptide inhibited S. aureus with a hemolytic concentration of above 45 μmol/L and inhibition rate in SMMC-7721 cells of 19.14%. The hybrid antimicrobial peptide, which was designed by the combination of α-helix and β-lamellar antimicrobial peptides, showed that both types of peptides did not interact with each either on spatial structure or biological activities, thereby providing a novel idea for the design of artificial antimicrobial peptides.
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29
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Rivera-Pérez WA, Yépes-Pérez AF, Martínez-Pabón MC. Molecular docking and in silico studies of the physicochemical properties of potential inhibitors for the phosphotransferase system of Streptococcus mutans. Arch Oral Biol 2018; 98:164-175. [PMID: 30500666 DOI: 10.1016/j.archoralbio.2018.09.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 09/04/2018] [Accepted: 09/05/2018] [Indexed: 10/27/2022]
Abstract
This study identified potential inhibitory compounds of the phosphoenolpyruvate-sugar. Phosphotransferase system of S. mutans, specifically enzyme II mannose transporter (EIIMan) in its subunits IIA, IIB and IIC by means of a selection protocol and in silico molecular analysis. Intervening the phosphotransferase system would compromise the physiological behavior and the pathogenic expression of S. mutans, and possibly other acidogenic bacteria that use phosphotransferases in their metabolism-making the phosphotransferase system a therapeutic target for the selective control of acidogenic microorganisms in caries control. Several computational techniques were used to evaluate molecular, physicochemical, and toxicological aspects of various compounds. Molecular docking was used to calculate the binding potential (ΔG) between receptor protein subunits and more than 836,000 different chemical compounds from the ZINC database. Physicochemical parameters related to the compounds' pharmacokinetic and pharmacodynamic indicators were evaluated, including absorption, distribution, metabolism, excretion, and toxicity (ADMET), and chemical analysis characterized the compounds structures. Thirteen compounds with EII binding potential of the phosphotransferase system of S. mutans and favorable ADMET properties were identified. Six spirooxindoles and three pyrrolidones stand out from the found compounds; unique structural characteristics of spirooxindoles and pyrrolidones associated with various reported biological activities like anti-microbial, antiinflammatory, anticancer, nootropic, neuroprotective and antiepileptic effects, among other pharmacological effects with surprising differences in terms of mechanisms of action. Following studies will provide more evidence of the action of these compounds on the phosphotransferase system of S. mutans, and its possible applications.
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Affiliation(s)
- Wbeimar Andrey Rivera-Pérez
- Faculty of Dentistry, University of Antioquia- UdeA, 64 Street No. 52-59, Block 31, Oral Microbiology Laboratory No. 216, Health Area, Medellin, Colombia.
| | - Andrés Felipe Yépes-Pérez
- Exact and Natural Sciences School, University of Antioquia-UdeA, Universidad de Antioquia. 67 street No. 53-108, Block 2, Chemistry of Colombian, Plants Laboratory, Office 330, Medellin, Colombia.
| | - Maria Cecilia Martínez-Pabón
- Faculty of Dentistry, University of Antioquia- UdeA, 64 Street No. 52-59, Block 31, Oral Microbiology Laboratory No. 216, Health Area, Medellin, Colombia.
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30
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Jukič M, Gobec S, Sova M. Reaching toward underexplored targets in antibacterial drug design. Drug Dev Res 2018; 80:6-10. [PMID: 30312991 DOI: 10.1002/ddr.21465] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 08/13/2018] [Accepted: 08/15/2018] [Indexed: 01/03/2023]
Abstract
The increase of antimicrobial resistance necessitates the renewal and strong research involvement in antibacterial drug design. In the following work, we comment on the key approaches used in development of new antibacterials, focusing on intracellular therapeutic targets that have been so far mostly underexplored: the enzymes of the Mur pathway MurA to MurF. We identify common obstacles observed during research on MurA, MurB, and Mur ligases inhibitors and their development into potential antibacterial compounds, and discern several approaches and solutions to tackle the whole-cell activity of designed compounds. Furthermore, we consolidate recent literature reports and encourage the further research on Mur enzymes.
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Affiliation(s)
- Marko Jukič
- Faculty of Pharmacy, University of Ljubljana, Ljubljana, Slovenia
| | - Stanislav Gobec
- Faculty of Pharmacy, University of Ljubljana, Ljubljana, Slovenia
| | - Matej Sova
- Faculty of Pharmacy, University of Ljubljana, Ljubljana, Slovenia
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31
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Machine learning: novel bioinformatics approaches for combating antimicrobial resistance. Curr Opin Infect Dis 2018; 30:511-517. [PMID: 28914640 DOI: 10.1097/qco.0000000000000406] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
PURPOSE OF REVIEW Antimicrobial resistance (AMR) is a threat to global health and new approaches to combating AMR are needed. Use of machine learning in addressing AMR is in its infancy but has made promising steps. We reviewed the current literature on the use of machine learning for studying bacterial AMR. RECENT FINDINGS The advent of large-scale data sets provided by next-generation sequencing and electronic health records make applying machine learning to the study and treatment of AMR possible. To date, it has been used for antimicrobial susceptibility genotype/phenotype prediction, development of AMR clinical decision rules, novel antimicrobial agent discovery and antimicrobial therapy optimization. SUMMARY Application of machine learning to studying AMR is feasible but remains limited. Implementation of machine learning in clinical settings faces barriers to uptake with concerns regarding model interpretability and data quality.Future applications of machine learning to AMR are likely to be laboratory-based, such as antimicrobial susceptibility phenotype prediction.
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Ragoza M, Hochuli J, Idrobo E, Sunseri J, Koes DR. Protein-Ligand Scoring with Convolutional Neural Networks. J Chem Inf Model 2017; 57:942-957. [PMID: 28368587 PMCID: PMC5479431 DOI: 10.1021/acs.jcim.6b00740] [Citation(s) in RCA: 438] [Impact Index Per Article: 62.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein-ligand binding and structural data enables the use of deep machine learning techniques for protein-ligand scoring. We describe convolutional neural network (CNN) scoring functions that take as input a comprehensive three-dimensional (3D) representation of a protein-ligand interaction. A CNN scoring function automatically learns the key features of protein-ligand interactions that correlate with binding. We train and optimize our CNN scoring functions to discriminate between correct and incorrect binding poses and known binders and nonbinders. We find that our CNN scoring function outperforms the AutoDock Vina scoring function when ranking poses both for pose prediction and virtual screening.
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Affiliation(s)
| | | | - Elisa Idrobo
- Department of Computer Science, The College of New Jersey , Ewing, New Jersey 08628, United States
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Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB). Drug Discov Today 2016; 22:555-565. [PMID: 27884746 DOI: 10.1016/j.drudis.2016.10.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Revised: 10/11/2016] [Accepted: 10/21/2016] [Indexed: 01/30/2023]
Abstract
Neglected disease drug discovery is generally poorly funded compared with major diseases and hence there is an increasing focus on collaboration and precompetitive efforts such as public-private partnerships (PPPs). The More Medicines for Tuberculosis (MM4TB) project is one such collaboration funded by the EU with the goal of discovering new drugs for tuberculosis. Collaborative Drug Discovery has provided a commercial web-based platform called CDD Vault which is a hosted collaborative solution for securely sharing diverse chemistry and biology data. Using CDD Vault alongside other commercial and free cheminformatics tools has enabled support of this and other large collaborative projects, aiding drug discovery efforts and fostering collaboration. We will describe CDD's efforts in assisting with the MM4TB project.
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A D3R prospective evaluation of machine learning for protein-ligand scoring. J Comput Aided Mol Des 2016; 30:761-771. [PMID: 27592011 DOI: 10.1007/s10822-016-9960-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Accepted: 08/31/2016] [Indexed: 12/20/2022]
Abstract
We assess the performance of several machine learning-based scoring methods at protein-ligand pose prediction, virtual screening, and binding affinity prediction. The methods and the manner in which they were trained make them sufficiently diverse to evaluate the utility of various strategies for training set curation and binding pose generation, but they share a novel approach to classification in the context of protein-ligand scoring. Rather than explicitly using structural data such as affinity values or information extracted from crystal binding poses for training, we instead exploit the abundance of data available from high-throughput screening to approach the problem as one of discriminating binders from non-binders. We evaluate the performance of our various scoring methods in the 2015 D3R Grand Challenge and find that although the merits of some features of our approach remain inconclusive, our scoring methods performed comparably to a state-of-the-art scoring function that was fit to binding affinity data.
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Synthesis and Antimicrobial Evaluation of Amixicile-Based Inhibitors of the Pyruvate-Ferredoxin Oxidoreductases of Anaerobic Bacteria and Epsilonproteobacteria. Antimicrob Agents Chemother 2016; 60:3980-7. [PMID: 27090174 DOI: 10.1128/aac.00670-16] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Accepted: 04/11/2016] [Indexed: 01/04/2023] Open
Abstract
Amixicile is a promising derivative of nitazoxanide (an antiparasitic therapeutic) developed to treat systemic infections caused by anaerobic bacteria, anaerobic parasites, and members of the Epsilonproteobacteria (Campylobacter and Helicobacter). Amixicile selectively inhibits pyruvate-ferredoxin oxidoreductase (PFOR) and related enzymes by inhibiting the function of the vitamin B1 cofactor (thiamine pyrophosphate) by a novel mechanism. Here, we interrogate the amixicile scaffold, guided by docking simulations, direct PFOR inhibition assays, and MIC tests against Clostridium difficile, Campylobacter jejuni, and Helicobacter pylori Docking simulations revealed that the nitro group present in nitazoxanide interacts with the protonated N4'-aminopyrimidine of thiamine pyrophosphate (TPP). The ortho-propylamine on the benzene ring formed an electrostatic interaction with an aspartic acid moiety (B456) of PFOR that correlated with improved PFOR-inhibitory activity and potency by MIC tests. Aryl substitution with electron-withdrawing groups and substitutions of the propylamine with other alkyl amines or nitrogen-containing heterocycles both improved PFOR inhibition and, in many cases, biological activity against C. difficile Docking simulation results correlate well with mechanistic enzymology and nuclear magnetic resonance (NMR) studies that show members of this class of antimicrobials to be specific inhibitors of vitamin B1 function by proton abstraction, which is both novel and likely to limit mutation-based drug resistance.
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Durrant JD, Carlson KE, Martin TA, Offutt TL, Mayne CG, Katzenellenbogen JA, Amaro RE. Neural-Network Scoring Functions Identify Structurally Novel Estrogen-Receptor Ligands. J Chem Inf Model 2015; 55:1953-61. [PMID: 26286148 PMCID: PMC4780411 DOI: 10.1021/acs.jcim.5b00241] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The magnitude of the investment required to bring a drug to the market hinders medical progress, requiring hundreds of millions of dollars and years of research and development. Any innovation that improves the efficiency of the drug-discovery process has the potential to accelerate the delivery of new treatments to countless patients in need. "Virtual screening," wherein molecules are first tested in silico in order to prioritize compounds for subsequent experimental testing, is one such innovation. Although the traditional scoring functions used in virtual screens have proven useful, improved accuracy requires novel approaches. In the current work, we use the estrogen receptor to demonstrate that neural networks are adept at identifying structurally novel small molecules that bind to a selected drug target, ultimately allowing experimentalists to test fewer compounds in the earliest stages of lead identification while obtaining higher hit rates. We describe 39 novel estrogen-receptor ligands identified in silico with experimentally determined Ki values ranging from 460 nM to 20 μM, presented here for the first time.
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Affiliation(s)
- Jacob D. Durrant
- Department of Chemistry & Biochemistry and the National Biomedical Computation Resource, University of California, San Diego, La Jolla, CA, 92093
| | - Kathryn E. Carlson
- Department of Chemistry, University of Illinois at Urbana-Champaign, Champaign, IL, 61801
| | - Teresa A. Martin
- Department of Chemistry, University of Illinois at Urbana-Champaign, Champaign, IL, 61801
| | - Tavina L. Offutt
- Department of Chemistry & Biochemistry and the National Biomedical Computation Resource, University of California, San Diego, La Jolla, CA, 92093
| | - Christopher G. Mayne
- Department of Chemistry, University of Illinois at Urbana-Champaign, Champaign, IL, 61801
| | | | - Rommie E. Amaro
- Department of Chemistry & Biochemistry and the National Biomedical Computation Resource, University of California, San Diego, La Jolla, CA, 92093
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Abstract
Governments, academics and industry are beginning to listen to the medical communities call for new anti-bacterials. This special issue brings together diverse review articles on topics from economics and pricing to new discovery methods.
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Affiliation(s)
- Roberta J Melander
- Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA
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