1
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Singh A, Tanwar M, Singh TP, Sharma S, Sharma P. An escape from ESKAPE pathogens: A comprehensive review on current and emerging therapeutics against antibiotic resistance. Int J Biol Macromol 2024; 279:135253. [PMID: 39244118 DOI: 10.1016/j.ijbiomac.2024.135253] [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: 05/22/2024] [Revised: 08/29/2024] [Accepted: 08/30/2024] [Indexed: 09/09/2024]
Abstract
The rise of antimicrobial resistance has positioned ESKAPE pathogens as a serious global health threat, primarily due to the limitations and frequent failures of current treatment options. This growing risk has spurred the scientific community to seek innovative antibiotic therapies and improved oversight strategies. This review aims to provide a comprehensive overview of the origins and resistance mechanisms of ESKAPE pathogens, while also exploring next-generation treatment strategies for these infections. In addition, it will address both traditional and novel approaches to combating antibiotic resistance, offering insights into potential new therapeutic avenues. Emerging research underscores the urgency of developing new antimicrobial agents and strategies to overcome resistance, highlighting the need for novel drug classes and combination therapies. Advances in genomic technologies and a deeper understanding of microbial pathogenesis are crucial in identifying effective treatments. Integrating precision medicine and personalized approaches could enhance therapeutic efficacy. The review also emphasizes the importance of global collaboration in surveillance and stewardship, as well as policy reforms, enhanced diagnostic tools, and public awareness initiatives, to address resistance on a worldwide scale.
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Affiliation(s)
- Anamika Singh
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Mansi Tanwar
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi 110029, India
| | - T P Singh
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Sujata Sharma
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi 110029, India.
| | - Pradeep Sharma
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi 110029, India.
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2
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Ancajas CMF, Oyedele AS, Butt CM, Walker AS. Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products. Nat Prod Rep 2024; 41:1543-1578. [PMID: 38912779 PMCID: PMC11484176 DOI: 10.1039/d4np00009a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Indexed: 06/25/2024]
Abstract
Time span in literature: 1985-early 2024Natural products play a key role in drug discovery, both as a direct source of drugs and as a starting point for the development of synthetic compounds. Most natural products are not suitable to be used as drugs without further modification due to insufficient activity or poor pharmacokinetic properties. Choosing what modifications to make requires an understanding of the compound's structure-activity relationships. Use of structure-activity relationships is commonplace and essential in medicinal chemistry campaigns applied to human-designed synthetic compounds. Structure-activity relationships have also been used to improve the properties of natural products, but several challenges still limit these efforts. Here, we review methods for studying the structure-activity relationships of natural products and their limitations. Specifically, we will discuss how synthesis, including total synthesis, late-stage derivatization, chemoenzymatic synthetic pathways, and engineering and genome mining of biosynthetic pathways can be used to produce natural product analogs and discuss the challenges of each of these approaches. Finally, we will discuss computational methods including machine learning methods for analyzing the relationship between biosynthetic genes and product activity, computer aided drug design techniques, and interpretable artificial intelligence approaches towards elucidating structure-activity relationships from models trained to predict bioactivity from chemical structure. Our focus will be on these latter topics as their applications for natural products have not been extensively reviewed. We suggest that these methods are all complementary to each other, and that only collaborative efforts using a combination of these techniques will result in a full understanding of the structure-activity relationships of natural products.
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Affiliation(s)
| | | | - Caitlin M Butt
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA.
| | - Allison S Walker
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA.
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
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3
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Kelleci Çelik F, Karaduman G. Computational modeling of air pollutants for aquatic risk: Prediction of ecological toxicity and exploring structural characteristics. CHEMOSPHERE 2024; 366:143501. [PMID: 39384138 DOI: 10.1016/j.chemosphere.2024.143501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 09/22/2024] [Accepted: 10/05/2024] [Indexed: 10/11/2024]
Abstract
Assessing the aquatic toxicity originating from air pollutants is essential in sustaining water resources and maintaining the ecosystem's safety. Quantitative structure-activity relationship (QSAR) models provide a computational tool for predicting pollutant toxicity, facilitating the identification/evaluation of the contaminants and identifying responsible structural fragments. One-vs-all (OvA) QSAR is a tailored approach to address multi-class QSAR problems. The study aims to determine five distinct levels of aquatic hazard categories for airborne pollutants using OvA-QSAR modeling containing 254 air contaminants. This QSAR analysis reveals the critical descriptors of air pollutants to target for molecular modification. Various factors, including the selection of relevant mechanistic descriptors, data quality, and outliers, determine the reliability of QSAR models. By employing feature selection and outlier identification approaches, the robustness and accuracy of our QSAR models were significantly increased, leading to more reliable predictions in chemical hazard assessment. The results revealed that models using the Random Forest algorithm performed the best based on the selected descriptors, with internal and external validation accuracy ranging from 71.90% to 97.53% and 76.47%-98.03%, respectively. This study indicated that the aquatic risk of air contaminants might be attributed predominantly to their sp3/sp2 carbon ratio, hydrogen-bond acceptor capability, hydrophilicity/lipophilicity, and van der Waals volumes. These structures can be critical in developing innovative strategies to mitigate or avoid the chemicals' harmful effects. Supporting air quality improvement, this study contributes to the rapid implementation of measures to protect aquatic ecosystems affected by air pollution.
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Affiliation(s)
- Feyza Kelleci Çelik
- Karamanoglu Mehmetbey University, Vocational School of Health Services, 70200, Karaman, Turkey.
| | - Gul Karaduman
- Karamanoglu Mehmetbey University, Department of Mathematics, 70100, Karaman, Turkey.
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4
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Kelleci Çelik F, Doğan S, Karaduman G. Drug-induced torsadogenicity prediction model: An explainable machine learning-driven quantitative structure-toxicity relationship approach. Comput Biol Med 2024; 182:109209. [PMID: 39332120 DOI: 10.1016/j.compbiomed.2024.109209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 09/03/2024] [Accepted: 09/23/2024] [Indexed: 09/29/2024]
Abstract
Drug-induced Torsade de Pointes (TdP), a life-threatening polymorphic ventricular tachyarrhythmia, emerges due to the cardiotoxic effects of pharmaceuticals. The need for precise mechanisms and clinical biomarkers to detect this adverse effect presents substantial challenges in drug safety assessment. In this study, we propose that analyzing the physicochemical properties of pharmaceuticals can provide valuable insights into their potential for torsadogenic cardiotoxicity. Our research centers on estimating TdP risk based on the molecular structure of drugs. We introduce a novel quantitative structure-toxicity relationship (QSTR) prediction model that leverages an in silico approach developed by adopting the 4R rule in laboratory animals. This approach eliminates the need for animal testing, saves time, and reduces cost. Our algorithm has successfully predicted the torsadogenic risks of various pharmaceutical compounds. To develop this model, we employed Support Vector Machine (SVM) and ensemble techniques, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). We enhanced the model's predictive accuracy through a rigorous two-step feature selection process. Furthermore, we utilized the SHapley Additive exPlanations (SHAP) technique to explain the prediction of torsadogenic risk, particularly within the RF model. This study represents a significant step towards creating a robust QSTR model, which can serve as an early screening tool for assessing the torsadogenic potential of pharmaceutical candidates or existing drugs. By incorporating molecular structure-based insights, we aim to enhance drug safety evaluation and minimize the risks of drug-induced TdP, ultimately benefiting both patients and the pharmaceutical industry.
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Affiliation(s)
- Feyza Kelleci Çelik
- Karamanoğlu Mehmetbey University, Vocational School of Health Services, 70200, Karaman, Turkey.
| | - Seyyide Doğan
- Karamanoğlu Mehmetbey University, Faculty of Economics and Administrative Science, 70200, Karaman, Turkey
| | - Gül Karaduman
- Karamanoğlu Mehmetbey University, Department of Mathematics, 70100, Karaman, Turkey
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5
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Arora S, Chettri S, Percha V, Kumar D, Latwal M. Artifical intelligence: a virtual chemist for natural product drug discovery. J Biomol Struct Dyn 2024; 42:3826-3835. [PMID: 37232451 DOI: 10.1080/07391102.2023.2216295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023]
Abstract
Nature is full of a bundle of medicinal substances and its product perceived as a prerogative structure to collaborate with protein drug targets. The natural product's (NPs) structure heterogeneity and eccentric characteristics inspired scientists to work on natural product-inspired medicine. To gear NP drug-finding artificial intelligence (AI) to confront and excavate unexplored opportunities. Natural product-inspired drug discoveries based on AI to act as an innovative tool for molecular design and lead discovery. Various models of machine learning produce quickly synthesizable mimetics of the natural products templates. The invention of novel natural products mimetics by computer-assisted technology provides a feasible strategy to get the natural product with defined bio-activities. AI's hit rate makes its high importance by improving trail patterns such as dose selection, trail life span, efficacy parameters, and biomarkers. Along these lines, AI methods can be a successful tool in a targeted way to formulate advanced medicinal applications for natural products. 'Prediction of future of natural product based drug discovery is not magic, actually its artificial intelligence'Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shefali Arora
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
| | - Sukanya Chettri
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
| | - Versha Percha
- Department of Pharmaceutical Chemistry, Dolphin(PG) Institute of Biomedical and Natural Sciences, Dehradun, Uttarakhand, India
| | - Deepak Kumar
- Department of Pharmaceutical Chemistry, Dolphin(PG) Institute of Biomedical and Natural Sciences, Dehradun, Uttarakhand, India
| | - Mamta Latwal
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
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Sabotič J, Bayram E, Ezra D, Gaudêncio SP, Haznedaroğlu BZ, Janež N, Ktari L, Luganini A, Mandalakis M, Safarik I, Simes D, Strode E, Toruńska-Sitarz A, Varamogianni-Mamatsi D, Varese GC, Vasquez MI. A guide to the use of bioassays in exploration of natural resources. Biotechnol Adv 2024; 71:108307. [PMID: 38185432 DOI: 10.1016/j.biotechadv.2024.108307] [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: 07/24/2023] [Revised: 12/05/2023] [Accepted: 01/01/2024] [Indexed: 01/09/2024]
Abstract
Bioassays are the main tool to decipher bioactivities from natural resources thus their selection and quality are critical for optimal bioprospecting. They are used both in the early stages of compounds isolation/purification/identification, and in later stages to evaluate their safety and efficacy. In this review, we provide a comprehensive overview of the most common bioassays used in the discovery and development of new bioactive compounds with a focus on marine bioresources. We present a comprehensive list of practical considerations for selecting appropriate bioassays and discuss in detail the bioassays typically used to explore antimicrobial, antibiofilm, cytotoxic, antiviral, antioxidant, and anti-ageing potential. The concept of quality control and bioassay validation are introduced, followed by safety considerations, which are critical to advancing bioactive compounds to a higher stage of development. We conclude by providing an application-oriented view focused on the development of pharmaceuticals, food supplements, and cosmetics, the industrial pipelines where currently known marine natural products hold most potential. We highlight the importance of gaining reliable bioassay results, as these serve as a starting point for application-based development and further testing, as well as for consideration by regulatory authorities.
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Affiliation(s)
- Jerica Sabotič
- Department of Biotechnology, Jožef Stefan Institute, 1000 Ljubljana, Slovenia.
| | - Engin Bayram
- Institute of Environmental Sciences, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - David Ezra
- Department of Plant Pathology and Weed Research, ARO, The Volcani Institute, P.O.Box 15159, Rishon LeZion 7528809, Israel
| | - Susana P Gaudêncio
- Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2819-516 Caparica, Portugal; UCIBIO - Applied Biomolecular Sciences Unit, Department of Chemistry, Blue Biotechnology & Biomedicine Lab, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Berat Z Haznedaroğlu
- Institute of Environmental Sciences, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Nika Janež
- Department of Biotechnology, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
| | - Leila Ktari
- B3Aqua Laboratory, National Institute of Marine Sciences and Technologies, Carthage University, Tunis, Tunisia
| | - Anna Luganini
- Department of Life Sciences and Systems Biology, University of Turin, 10123 Turin, Italy
| | - Manolis Mandalakis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, 71500 Heraklion, Greece
| | - Ivo Safarik
- Department of Nanobiotechnology, Biology Centre, ISBB, CAS, Na Sadkach 7, 370 05 Ceske Budejovice, Czech Republic; Regional Centre of Advanced Technologies and Materials, Czech Advanced Technology and Research Institute, Palacky University, Slechtitelu 27, 783 71 Olomouc, Czech Republic
| | - Dina Simes
- Centre of Marine Sciences (CCMAR), Universidade do Algarve, 8005-139 Faro, Portugal; 2GenoGla Diagnostics, Centre of Marine Sciences (CCMAR), Universidade do Algarve, Faro, Portugal
| | - Evita Strode
- Latvian Institute of Aquatic Ecology, Agency of Daugavpils University, Riga LV-1007, Latvia
| | - Anna Toruńska-Sitarz
- Department of Marine Biology and Biotechnology, Faculty of Oceanography and Geography, University of Gdańsk, 81-378 Gdynia, Poland
| | - Despoina Varamogianni-Mamatsi
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, 71500 Heraklion, Greece
| | | | - Marlen I Vasquez
- Department of Chemical Engineering, Cyprus University of Technology, 3036 Limassol, Cyprus
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7
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Yuan Y, Shi C, Zhao H. Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products. ACS Synth Biol 2023; 12:2650-2662. [PMID: 37607352 PMCID: PMC10615616 DOI: 10.1021/acssynbio.3c00234] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Natural products (NPs) produced by microorganisms and plants are a major source of drugs, herbicides, and fungicides. Thanks to recent advances in DNA sequencing, bioinformatics, and genome mining tools, a vast amount of data on NP biosynthesis has been generated over the years, which has been increasingly exploited to develop machine learning (ML) tools for NP discovery. In this review, we discuss the latest advances in developing and applying ML tools for exploring the potential NPs that can be encoded by genomic language and predicting the types of bioactivities of NPs. We also examine the technical challenges associated with the development and application of ML tools for NP research.
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Affiliation(s)
- Yujie Yuan
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Chengyou Shi
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Huimin Zhao
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Departments of Chemistry, Biochemistry, and Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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8
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Gaudêncio SP, Bayram E, Lukić Bilela L, Cueto M, Díaz-Marrero AR, Haznedaroglu BZ, Jimenez C, Mandalakis M, Pereira F, Reyes F, Tasdemir D. Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation. Mar Drugs 2023; 21:md21050308. [PMID: 37233502 DOI: 10.3390/md21050308] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023] Open
Abstract
Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.
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Affiliation(s)
- Susana P Gaudêncio
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2819-516 Caparica, Portugal
- UCIBIO-Applied Molecular Biosciences Unit, Chemistry Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Engin Bayram
- Institute of Environmental Sciences, Room HKC-202, Hisar Campus, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Lada Lukić Bilela
- Department of Biology, Faculty of Science, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina
| | - Mercedes Cueto
- Instituto de Productos Naturales y Agrobiología-CSIC, 38206 La Laguna, Spain
| | - Ana R Díaz-Marrero
- Instituto de Productos Naturales y Agrobiología-CSIC, 38206 La Laguna, Spain
- Instituto Universitario de Bio-Orgánica (IUBO), Universidad de La Laguna, 38206 La Laguna, Spain
| | - Berat Z Haznedaroglu
- Institute of Environmental Sciences, Room HKC-202, Hisar Campus, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Carlos Jimenez
- CICA- Centro Interdisciplinar de Química e Bioloxía, Departamento de Química, Facultade de Ciencias, Universidade da Coruña, 15071 A Coruña, Spain
| | - Manolis Mandalakis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, HCMR Thalassocosmos, 71500 Gournes, Crete, Greece
| | - Florbela Pereira
- LAQV, REQUIMTE, Chemistry Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Fernando Reyes
- Fundación MEDINA, Avda. del Conocimiento 34, 18016 Armilla, Spain
| | - Deniz Tasdemir
- GEOMAR Centre for Marine Biotechnology (GEOMAR-Biotech), Research Unit Marine Natural Products Chemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Am Kiel-Kanal 44, 24106 Kiel, Germany
- Faculty of Mathematics and Natural Science, Kiel University, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
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9
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Talat A, Khan AU. Artificial intelligence as a smart approach to develop antimicrobial drug molecules: A paradigm to combat drug-resistant infections. Drug Discov Today 2023; 28:103491. [PMID: 36646245 DOI: 10.1016/j.drudis.2023.103491] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 01/01/2023] [Accepted: 01/05/2023] [Indexed: 01/15/2023]
Abstract
Antimicrobial resistance (AMR) is a silent pandemic with the third highest global mortality. The antibiotic development pipeline is scarce even though AMR has escalated uncontrollably. Artificial intelligence (AI) is a revolutionary approach, accelerating drug discovery because of its fast pace, cost efficiency, lower labor requirements, and fewer chances of failure. AI has been used to discover several beta-lactamase inhibitors and antibiotic alternatives from antimicrobial peptides (AMPs), nonribosomal peptides, bacteriocins, and marine natural products. The significant recent increase in the use of AI platforms by pharmaceutical companies could result in the discovery of efficient antibiotic alternatives with lower chances of resistance generation.
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Affiliation(s)
- Absar Talat
- Medical Microbiology and Molecular Biology Laboratory, Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India
| | - Asad U Khan
- Medical Microbiology and Molecular Biology Laboratory, Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India.
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10
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Moshawih S, Goh HP, Kifli N, Idris AC, Yassin H, Kotra V, Goh KW, Liew KB, Ming LC. Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives. Chem Biol Drug Des 2022; 100:185-217. [PMID: 35490393 DOI: 10.1111/cbdd.14062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 04/15/2022] [Accepted: 04/23/2022] [Indexed: 11/28/2022]
Abstract
Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.
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Affiliation(s)
- Said Moshawih
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Hui Poh Goh
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Nurolaini Kifli
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Azam Che Idris
- Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Hayati Yassin
- Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Vijay Kotra
- Faculty of Pharmacy, Quest International University, Perak, Malaysia
| | - Khang Wen Goh
- Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia
| | - Kai Bin Liew
- Faculty of Pharmacy, University of Cyberjaya, Cyberjaya, Malaysia
| | - Long Chiau Ming
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
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11
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Tao Xue H, Stanley-Baker M, Wai Kin Kong A, Leung Li H, Wen Bin Goh W. Data considerations for predictive modeling applied to the discovery of bioactive natural products. Drug Discov Today 2022; 27:2235-2243. [DOI: 10.1016/j.drudis.2022.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/21/2022] [Accepted: 05/10/2022] [Indexed: 11/29/2022]
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12
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Saldívar-González FI, Aldas-Bulos VD, Medina-Franco JL, Plisson F. Natural product drug discovery in the artificial intelligence era. Chem Sci 2022; 13:1526-1546. [PMID: 35282622 PMCID: PMC8827052 DOI: 10.1039/d1sc04471k] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/10/2021] [Indexed: 12/19/2022] Open
Abstract
Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.
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Affiliation(s)
- F I Saldívar-González
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - V D Aldas-Bulos
- Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
| | - J L Medina-Franco
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - F Plisson
- CONACYT - Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
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13
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Spiegel J, Senderowitz H. A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening. Int J Mol Sci 2021; 23:43. [PMID: 35008467 PMCID: PMC8744642 DOI: 10.3390/ijms23010043] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/18/2021] [Accepted: 12/19/2021] [Indexed: 12/30/2022] Open
Abstract
Virtual screening (VS) is a well-established method in the initial stages of many drug and material design projects. VS is typically performed using structure-based approaches such as molecular docking, or various ligand-based approaches. Most docking tools were designed to be as global as possible, and consequently only require knowledge on the 3D structure of the biotarget. In contrast, many ligand-based approaches (e.g., 3D-QSAR and pharmacophore) require prior development of project-specific predictive models. Depending on the type of model (e.g., classification or regression), predictive ability is typically evaluated using metrics of performance on either the training set (e.g.,QCV2) or the test set (e.g., specificity, selectivity or QF1/F2/F32). However, none of these metrics were developed with VS in mind, and consequently, their ability to reliably assess the performances of a model in the context of VS is at best limited. With this in mind we have recently reported the development of the enrichment optimization algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations for VS by optimizing an enrichment-based metric in the space of the descriptors. Here we present an improved version of the algorithm which better handles active compounds and which also takes into account information on inactive (either known inactive or decoy) compounds. We compared the improved EOA in small-scale VS experiments with three common docking tools, namely, Glide-SP, GOLD and AutoDock Vina, employing five molecular targets (acetylcholinesterase, human immunodeficiency virus type 1 protease, MAP kinase p38 alpha, urokinase-type plasminogen activator, and trypsin I). We found that EOA consistently outperformed all docking tools in terms of the area under the ROC curve (AUC) and EF1% metrics that measured the overall and initial success of the VS process, respectively. This was the case when the docking metrics were calculated based on a consensus approach and when they were calculated based on two different sets of single crystal structures. Finally, we propose that EOA could be combined with molecular docking to derive target-specific scoring functions.
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Affiliation(s)
| | - Hanoch Senderowitz
- Department of Chemistry, Bar-Ilan University, Ramat-Gan 5290002, Israel;
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14
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Santana K, do Nascimento LD, Lima e Lima A, Damasceno V, Nahum C, Braga RC, Lameira J. Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products. Front Chem 2021; 9:662688. [PMID: 33996755 PMCID: PMC8117418 DOI: 10.3389/fchem.2021.662688] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 02/25/2021] [Indexed: 12/22/2022] Open
Abstract
Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.
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Affiliation(s)
- Kauê Santana
- Instituto de Biodiversidade, Universidade Federal do Oeste do Pará, Santarém, Brazil
| | | | - Anderson Lima e Lima
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Vinícius Damasceno
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Claudio Nahum
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | | | - Jerônimo Lameira
- Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil
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15
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Zhang R, Li X, Zhang X, Qin H, Xiao W. Machine learning approaches for elucidating the biological effects of natural products. Nat Prod Rep 2021; 38:346-361. [PMID: 32869826 DOI: 10.1039/d0np00043d] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural products (NPs) has developed in order to manage the challenge of the discovery of bioactive NPs. In the present review, we will introduce the basic principles and protocols for using the ML approach to investigate the bioactivity of NPs, citing a series of practical examples regarding the study of anti-microbial, anti-cancer, and anti-inflammatory NPs, etc. ML algorithms manage a variety of classification and regression problems associated with bioactive NPs, from those that are linear to non-linear and from pure compounds to plant extracts. Inspired by cases reported in the literature and our own experience, a number of key points have been emphasized for reducing modeling errors, including dataset preparation and applicability domain analysis.
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Affiliation(s)
- Ruihan Zhang
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Xiaoli Li
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Xingjie Zhang
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Huayan Qin
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Weilie Xiao
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
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16
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Suay‐Garcia B, Bueso‐Bordils JI, Falcó A, Pérez‐Gracia MT, Antón‐Fos G, Alemán‐López P. Quantitative structure–activity relationship methods in the discovery and development of antibacterials. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1472] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Beatriz Suay‐Garcia
- Departamento de Matemáticas, Física y Ciencias Tecnológicas Universidad Cardenal Herrera‐CEU, CEU Universities Alfara del Patriarca, Valencia Spain
| | - Jose Ignacio Bueso‐Bordils
- Departamento de Farmacia, Universidad Cardenal Herrera‐CEU CEU Universities Alfara del Patriarca, Valencia Spain
| | - Antonio Falcó
- Departamento de Matemáticas, Física y Ciencias Tecnológicas Universidad Cardenal Herrera‐CEU, CEU Universities Alfara del Patriarca, Valencia Spain
| | - María Teresa Pérez‐Gracia
- Departamento de Farmacia, Universidad Cardenal Herrera‐CEU CEU Universities Alfara del Patriarca, Valencia Spain
| | - Gerardo Antón‐Fos
- Departamento de Farmacia, Universidad Cardenal Herrera‐CEU CEU Universities Alfara del Patriarca, Valencia Spain
| | - Pedro Alemán‐López
- Departamento de Farmacia, Universidad Cardenal Herrera‐CEU CEU Universities Alfara del Patriarca, Valencia Spain
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17
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Kundu M, Das S, Dhara D, Mandal M. Prospect of natural products in glioma: A novel avenue in glioma management. Phytother Res 2019; 33:2571-2584. [PMID: 31359523 DOI: 10.1002/ptr.6426] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 05/28/2019] [Accepted: 06/09/2019] [Indexed: 12/26/2022]
Abstract
Glioma is one of the most perplexing cancers because of its infiltrating nature, molecular signaling, and location in central nervous system. Blood-brain barrier acts as a natural barrier to the glioma making it difficult to access by conventional chemotherapy. Clinicians are using natural compounds or their derivatives for several diseases including different cancers. However, the feasibility of using natural compounds in glioma is not explored in details. Natural compounds can act over a wide variety of signaling pathways such as survival and metabolic pathways and induce cell death. Some of the natural agents have additional benefits of crossing biological barriers such as blood-brain barrier with ease having few or no impact on the surrounding healthy cells. All of these benefits make natural compounds a prospective candidate for the glioma management. This article evaluates the benefits of using natural compounds for glioma therapy and their possible mechanism of actions. We have discussed the natural compounds assessed currently for glioma therapy and proposed a few novel natural compounds with potential antiglioma effect based on their mechanism of action.
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Affiliation(s)
- Moumita Kundu
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Subhayan Das
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Dibakar Dhara
- Department of Chemistry, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Mahitosh Mandal
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, India
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18
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Koulouridi E, Valli M, Ntie-Kang F, Bolzani VDS. A primer on natural product-based virtual screening. PHYSICAL SCIENCES REVIEWS 2019. [DOI: 10.1515/psr-2018-0105] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Abstract
Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.
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19
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Tripathi VC, Satish S, Horam S, Raj S, lal A, Arockiaraj J, Pasupuleti M, Dikshit DK. Natural products from polar organisms: Structural diversity, bioactivities and potential pharmaceutical applications. POLAR SCIENCE 2018; 18:147-166. [DOI: 10.1016/j.polar.2018.04.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/16/2023]
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20
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Floresta G, Amata E, Barbaraci C, Gentile D, Turnaturi R, Marrazzo A, Rescifina A. A Structure- and Ligand-Based Virtual Screening of a Database of "Small" Marine Natural Products for the Identification of "Blue" Sigma-2 Receptor Ligands. Mar Drugs 2018; 16:md16100384. [PMID: 30322188 PMCID: PMC6212963 DOI: 10.3390/md16100384] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 10/10/2018] [Accepted: 10/11/2018] [Indexed: 12/17/2022] Open
Abstract
Sigma receptors are a fascinating receptor protein class whose ligands are actually under clinical evaluation for the modulation of opioid analgesia and their use as positron emission tomography radiotracers. In particular, peculiar biological and therapeutic functions are associated with the sigma-2 (σ2) receptor. The σ2 receptor ligands determine tumor cell death through apoptotic and non-apoptotic pathways, and the overexpression of σ2 receptors in several tumor cell lines has been well documented, with significantly higher levels in proliferating tumor cells compared to quiescent ones. This acknowledged feature has found practical application in the development of cancer cell tracers and for ligand-targeting therapy. In this context, the development of new ligands that target the σ2 receptors is beneficial for those diseases in which this protein is involved. In this paper, we conducted a search of new potential σ2 receptor ligands among a database of 1517 “small” marine natural products constructed by the union of the Seaweed Metabolite and the Chemical Entities of Biological Interest (ChEBI) Databases. The structures were passed through two filters that were constituted by our developed two-dimensional (2D) and three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) statistical models, and successively docked upon a σ2 receptor homology model that we built according to the FASTA sequence of the σ2/TMEM97 (SGMR2_HUMAN) receptor.
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Affiliation(s)
- Giuseppe Floresta
- Department of Drug Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy.
- Department of Chemical Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy.
| | - Emanuele Amata
- Department of Chemical Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy.
| | - Carla Barbaraci
- Department of Drug Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy.
| | - Davide Gentile
- Department of Drug Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy.
| | - Rita Turnaturi
- Department of Drug Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy.
| | - Agostino Marrazzo
- Department of Drug Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy.
| | - Antonio Rescifina
- Department of Drug Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy.
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21
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In Silico HCT116 Human Colon Cancer Cell-Based Models En Route to the Discovery of Lead-Like Anticancer Drugs. Biomolecules 2018; 8:biom8030056. [PMID: 30018273 PMCID: PMC6164384 DOI: 10.3390/biom8030056] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 07/10/2018] [Accepted: 07/11/2018] [Indexed: 01/04/2023] Open
Abstract
To discover new inhibitors against the human colon carcinoma HCT116 cell line, two quantitative structure–activity relationship (QSAR) studies using molecular and nuclear magnetic resonance (NMR) descriptors were developed through exploration of machine learning techniques and using the value of half maximal inhibitory concentration (IC50). In the first approach, A, regression models were developed using a total of 7339 molecules that were extracted from the ChEMBL and ZINC databases and recent literature. The performance of the regression models was successfully evaluated by internal and external validations, the best model achieved R2 of 0.75 and 0.73 and root mean square error (RMSE) of 0.66 and 0.69 for the training and test sets, respectively. With the inherent time-consuming efforts of working with natural products (NPs), we conceived a new NP drug hit discovery strategy that consists in frontloading samples with 1D NMR descriptors to predict compounds with anticancer activity prior to bioactivity screening for NPs discovery, approach B. The NMR QSAR classification models were built using 1D NMR data (1H and 13C) as descriptors, from 50 crude extracts, 55 fractions and five pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 63% for both training and test sets.
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22
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Pereira F, Aires-de-Sousa J. Computational Methodologies in the Exploration of Marine Natural Product Leads. Mar Drugs 2018; 16:md16070236. [PMID: 30011882 PMCID: PMC6070892 DOI: 10.3390/md16070236] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 07/02/2018] [Accepted: 07/06/2018] [Indexed: 12/18/2022] Open
Abstract
Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty. In this review, the most relevant current computational dereplication methodologies are highlighted. Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of NPs either in small datasets of isolated compounds or in large-scale databases. The most common LB techniques include Quantitative Structure–Activity Relationships (QSAR), estimation of drug likeness, prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, similarity searching, and pharmacophore identification. Analogously, molecular dynamics, docking and binding cavity analysis have been used in SB approaches. Their significance and achievements are the main focus of this review.
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Affiliation(s)
- Florbela Pereira
- LAQV and REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.
| | - Joao Aires-de-Sousa
- LAQV and REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.
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23
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Pereira G, Szwarc B, Mondragão MA, Lima PA, Pereira F. A Ligand-Based Approach to the Discovery of Lead-Like Potassium Channel KV
1.3 Inhibitors. ChemistrySelect 2018. [DOI: 10.1002/slct.201702977] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Gilberto Pereira
- LAQV and REQUIMTE; Departamento de Química; Faculdade de Ciências e Tecnologia; Universidade Nova de Lisboa; 2829-516 Caparica Portugal
- NOVA Medical School; Laboratório de Fisiologia; Faculdade de Ciências Médicas; Universidade Nova de Lisboa; Campo dos Mártires da Pátria, 130 1169-056 Lisboa PORTUGAL
| | - Beatriz Szwarc
- Sea4Us - Biotecnologia e Recursos Marinhos, Lda; Rua do Poente S/N 8650-378 Sagres Portugal
- NOVA Medical School; Laboratório de Fisiologia; Faculdade de Ciências Médicas; Universidade Nova de Lisboa; Campo dos Mártires da Pátria, 130 1169-056 Lisboa PORTUGAL
| | - Miguel A. Mondragão
- Sea4Us - Biotecnologia e Recursos Marinhos, Lda; Rua do Poente S/N 8650-378 Sagres Portugal
- NOVA Medical School; Laboratório de Fisiologia; Faculdade de Ciências Médicas; Universidade Nova de Lisboa; Campo dos Mártires da Pátria, 130 1169-056 Lisboa PORTUGAL
| | - Pedro A. Lima
- Sea4Us - Biotecnologia e Recursos Marinhos, Lda; Rua do Poente S/N 8650-378 Sagres Portugal
- NOVA Medical School; Laboratório de Fisiologia; Faculdade de Ciências Médicas; Universidade Nova de Lisboa; Campo dos Mártires da Pátria, 130 1169-056 Lisboa PORTUGAL
| | - Florbela Pereira
- LAQV and REQUIMTE; Departamento de Química; Faculdade de Ciências e Tecnologia; Universidade Nova de Lisboa; 2829-516 Caparica Portugal
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24
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Niu B, Zhao M, Su Q, Zhang M, Lv W, Chen Q, Chen F, Chu D, Du D, Zhang Y. 2D-SAR and 3D-QSAR analyses for acetylcholinesterase inhibitors. Mol Divers 2017; 21:413-426. [PMID: 28275924 DOI: 10.1007/s11030-017-9732-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Accepted: 02/20/2017] [Indexed: 12/12/2022]
Abstract
Alzheimer's disease (AD) accounts for almost three quarters of dementia patients and interferes people's normal life. Great progress has been made recently in the study of Acetylcholinesterase (AChE), known as one of AD's biomarkers. In this study, acetylcholinesterase inhibitors (AChEI) were collected to build a two-dimensional structure-activity relationship (2D-SAR) model and three-dimensional quantitative structure-activity relationship (3D-QSAR) model based on feature selection method combined with random forest. After calculation, the prediction accuracy of the 2D-SAR model was 89.63% by using the tenfold cross-validation test and 87.27% for the independent test set. Three cutting ways were employed to build 3D-QSAR models. A model with the highest [Formula: see text] (cross-validated correlation coefficient) and [Formula: see text](non-cross-validated correlation coefficient) was obtained to predict AChEI activity. The mean absolute error (MAE) of the training set and the test set was 0.0689 and 0.5273, respectively. In addition, molecular docking was also employed to reveal that the ionization state of the compounds had an impact upon their interaction with AChE. Molecular docking results indicate that Ser124 might be one of the active site residues.
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Affiliation(s)
- Bing Niu
- Shanghai Key Laboratory of Bio-Energy Crops, College of Life Science, Shanghai University, Shanghai, 200444, People's Republic of China.
| | - Manman Zhao
- Shanghai Key Laboratory of Bio-Energy Crops, College of Life Science, Shanghai University, Shanghai, 200444, People's Republic of China
| | - Qiang Su
- Shanghai Key Laboratory of Bio-Energy Crops, College of Life Science, Shanghai University, Shanghai, 200444, People's Republic of China
| | - Mengying Zhang
- Shanghai Key Laboratory of Bio-Energy Crops, College of Life Science, Shanghai University, Shanghai, 200444, People's Republic of China
| | - Wei Lv
- School of Materials Science and Engineering, Shanghai University, 99 Shangda Road, Shanghai, 200444, People's Republic of China
| | - Qin Chen
- Shanghai Key Laboratory of Bio-Energy Crops, College of Life Science, Shanghai University, Shanghai, 200444, People's Republic of China
| | - Fuxue Chen
- Shanghai Key Laboratory of Bio-Energy Crops, College of Life Science, Shanghai University, Shanghai, 200444, People's Republic of China
| | - Dechang Chu
- Department of Life Science, Heze University, Heze, Shandong, 274500, People's Republic of China
| | - Dongshu Du
- Shanghai Key Laboratory of Bio-Energy Crops, College of Life Science, Shanghai University, Shanghai, 200444, People's Republic of China.
- Department of Life Science, Heze University, Heze, Shandong, 274500, People's Republic of China.
| | - Yuhui Zhang
- Changhai Hospital, Second Military Medical University, Shanghai, 200433, People's Republic of China.
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25
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Sakhteman A, Edraki N, Hemmateenejad B, Miri R, Foroumadi A, Shafiee A, Khoshneviszadeh M. In Silico Screening of IL-1β Production Inhibitors Using Chemometric Tools. IRANIAN JOURNAL OF PHARMACEUTICAL RESEARCH : IJPR 2017; 16:513-524. [PMID: 28979306 PMCID: PMC5603860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
The IL-1β plays a major role in inflammatory disorders and IL-1β production inhibitors can be used in the treatment of inflammatory and related diseases. In this study, quantitative relationships between the structures of 46 pyridazine derivatives (inhibitors of IL-1β production) and their activities were investigated by Multiple Linear Regression (MLR) technique Stepwise Regression Method (ES-SWR). The genetic algorithm (GA) has been proposed for improvement of the performance of the MLR modeling by choosing the most relevant descriptors. The results show that eight descriptors are able to describe about 83.70% of the variance in the experimental activity of the molecules in the training set. The physical meaning of the selected descriptors is discussed in detail. Power predictions of the QSAR models developed were evaluated using cross-validation, and validation through an external prediction set. The results showed satisfactory goodness-of-fit, robustness and perfect external predictive performance. The applicability domain was used to define the area of reliable predictions. Furthermore, the in silico screening technique was applied in order to predict the structure and potency of new compounds of this type using the proposed QSAR model.
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Affiliation(s)
- Amirhossein Sakhteman
- Department of Medicinal Chemistry, Faculty of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Najmeh Edraki
- Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
| | | | - Ramin Miri
- Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Alireza Foroumadi
- Pharmaceutical Sciences Research Center, Tehran University of Medical Sciences, Tehran, Iran.
| | - Abbas Shafiee
- Pharmaceutical Sciences Research Center, Tehran University of Medical Sciences, Tehran, Iran.,Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran.
| | - Mehdi Khoshneviszadeh
- Department of Medicinal Chemistry, Faculty of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran.,Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.,Corresponding author: E-mail:
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