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Zhou Y, Zhang Y, Zhao D, Yu X, Shen X, Zhou Y, Wang S, Qiu Y, Chen Y, Zhu F. TTD: Therapeutic Target Database describing target druggability information. Nucleic Acids Res 2024; 52:D1465-D1477. [PMID: 37713619 PMCID: PMC10767903 DOI: 10.1093/nar/gkad751] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 07/31/2023] [Accepted: 09/05/2023] [Indexed: 09/17/2023] Open
Abstract
Target discovery is one of the essential steps in modern drug development, and the identification of promising targets is fundamental for developing first-in-class drug. A variety of methods have emerged for target assessment based on druggability analysis, which refers to the likelihood of a target being effectively modulated by drug-like agents. In the therapeutic target database (TTD), nine categories of established druggability characteristics were thus collected for 426 successful, 1014 clinical trial, 212 preclinical/patented, and 1479 literature-reported targets via systematic review. These characteristic categories were classified into three distinct perspectives: molecular interaction/regulation, human system profile and cell-based expression variation. With the rapid progression of technology and concerted effort in drug discovery, TTD and other databases were highly expected to facilitate the explorations of druggability characteristics for the discovery and validation of innovative drug target. TTD is now freely accessible at: https://idrblab.org/ttd/.
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Affiliation(s)
- Ying Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yintao Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Donghai Zhao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyuan Yu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyi Shen
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven 06510, USA
| | - Yuan Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shanshan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Yunqing Qiu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
- Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518000, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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Mogwera KSP, Chibale K, Arendse LB. Developing kinase inhibitors for malaria: an opportunity or liability? Trends Parasitol 2023; 39:720-731. [PMID: 37385921 DOI: 10.1016/j.pt.2023.06.001] [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: 04/18/2023] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 07/01/2023]
Abstract
Highly druggable and essential to almost all aspects of cellular life, the protein and phosphoinositide kinase gene families offer a wealth of potential targets for pharmacological modulation for both noncommunicable and infectious diseases. Despite the success of kinase inhibitors in oncology and other disease indications, targeting kinases comes with significant challenges. Key hurdles for kinase drug discovery include selectivity and acquired resistance. The phosphatidylinositol 4-kinase beta inhibitor MMV390048 showed good efficacy in Phase 2a clinical trials, demonstrating the potential of kinase inhibitors for malaria treatment. Here we argue that the potential benefits of Plasmodium kinase inhibitors outweigh the risks, and we highlight the opportunity for designed polypharmacology to reduce the risk of resistance.
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Affiliation(s)
- Koketso S P Mogwera
- Drug Discovery and Development Centre (H3D), South African Medical Research Council Drug Discovery and Development Research Unit, Department of Chemistry and Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Rondebosch 7701, South Africa
| | - Kelly Chibale
- Drug Discovery and Development Centre (H3D), South African Medical Research Council Drug Discovery and Development Research Unit, Department of Chemistry and Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Rondebosch 7701, South Africa
| | - Lauren B Arendse
- Drug Discovery and Development Centre (H3D), South African Medical Research Council Drug Discovery and Development Research Unit, Department of Chemistry and Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Rondebosch 7701, South Africa.
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Revillo Imbernon J, Chiesa L, Kellenberger E. Mining the Protein Data Bank to inspire fragment library design. Front Chem 2023; 11:1089714. [PMID: 36846858 PMCID: PMC9950109 DOI: 10.3389/fchem.2023.1089714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 01/27/2023] [Indexed: 02/12/2023] Open
Abstract
The fragment approach has emerged as a method of choice for drug design, as it allows difficult therapeutic targets to be addressed. Success lies in the choice of the screened chemical library and the biophysical screening method, and also in the quality of the selected fragment and structural information used to develop a drug-like ligand. It has recently been proposed that promiscuous compounds, i.e., those that bind to several proteins, present an advantage for the fragment approach because they are likely to give frequent hits in screening. In this study, we searched the Protein Data Bank for fragments with multiple binding modes and targeting different sites. We identified 203 fragments represented by 90 scaffolds, some of which are not or hardly present in commercial fragment libraries. By contrast to other available fragment libraries, the studied set is enriched in fragments with a marked three-dimensional character (download at 10.5281/zenodo.7554649).
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Affiliation(s)
- Julia Revillo Imbernon
- Laboratoire d’Innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS Université de Strasbourg, Illkirch-Graffenstaden, France
| | - Luca Chiesa
- Laboratoire d’Innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS Université de Strasbourg, Illkirch-Graffenstaden, France
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Bernal L, Pinzi L, Rastelli G. Identification of Promising Drug Candidates against Prostate Cancer through Computationally-Driven Drug Repurposing. Int J Mol Sci 2023; 24:ijms24043135. [PMID: 36834548 PMCID: PMC9964599 DOI: 10.3390/ijms24043135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/02/2023] [Accepted: 02/03/2023] [Indexed: 02/08/2023] Open
Abstract
Prostate cancer (PC) is one of the most common types of cancer in males. Although early stages of PC are generally associated with favorable outcomes, advanced phases of the disease present a significantly poorer prognosis. Moreover, currently available therapeutic options for the treatment of PC are still limited, being mainly focused on androgen deprivation therapies and being characterized by low efficacy in patients. As a consequence, there is a pressing need to identify alternative and more effective therapeutics. In this study, we performed large-scale 2D and 3D similarity analyses between compounds reported in the DrugBank database and ChEMBL molecules with reported anti-proliferative activity on various PC cell lines. The analyses included also the identification of biological targets of ligands with potent activity on PC cells, as well as investigations on the activity annotations and clinical data associated with the more relevant compounds emerging from the ligand-based similarity results. The results led to the prioritization of a set of drugs and/or clinically tested candidates potentially useful in drug repurposing against PC.
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Affiliation(s)
- Leonardo Bernal
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125 Modena, Italy
- Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Luca Pinzi
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125 Modena, Italy
| | - Giulio Rastelli
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125 Modena, Italy
- Correspondence: ; Tel.: +39-059-2058564
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Feldmann C, Bajorath J. Advances in Computational Polypharmacology. Mol Inform 2022; 41:e2200190. [PMID: 36002382 PMCID: PMC10078381 DOI: 10.1002/minf.202200190] [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: 07/31/2022] [Accepted: 08/24/2022] [Indexed: 12/13/2022]
Abstract
In drug discovery, polypharmacology encompasses the use of small molecules with defined multi-target activity and in vivo effects resulting from multi-target engagement. Multi-target compounds are often efficacious in the treatment of complex diseases involving target and pathway networks, but might also elicit unwanted side effects. Computational approaches such as target prediction or multi-target ligand design have been used to support polypharmacological drug discovery. In addition to efforts directed at the identification or design of new multi-target compounds, other computational investigations have aimed to differentiate such compounds from potential false-positives or explore the molecular basis of multi-target activities. Herein, a concise overview of the field is provided and recent advances in computational polypharmacology through machine learning are discussed.
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Affiliation(s)
- Christian Feldmann
- Department of Life Science Informatics, Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Friedrich-Hirzebruch-Allee 5/6, D-53115, Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Friedrich-Hirzebruch-Allee 5/6, D-53115, Bonn, Germany
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Network Pharmacology of Adaptogens in the Assessment of Their Pleiotropic Therapeutic Activity. Pharmaceuticals (Basel) 2022; 15:ph15091051. [PMID: 36145272 PMCID: PMC9504187 DOI: 10.3390/ph15091051] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/11/2022] [Accepted: 08/19/2022] [Indexed: 02/07/2023] Open
Abstract
The reductionist concept, based on the ligand–receptor interaction, is not a suitable model for adaptogens, and herbal preparations affect multiple physiological functions, revealing polyvalent pharmacological activities, and are traditionally used in many conditions. This review, for the first time, provides a rationale for the pleiotropic therapeutic efficacy of adaptogens based on evidence from recent gene expression studies in target cells and where the network pharmacology and systems biology approaches were applied. The specific molecular targets and adaptive stress response signaling mechanisms involved in nonspecific modes of action of adaptogens are identified.
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Rastelli G. Meet the Editorial Board Member. Curr Med Chem 2022. [DOI: 10.2174/092986732915220411100429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Giulio Rastelli
- Department of Life Sciences
University of Modena and Reggio Emilia
Modena
Italy
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Tinivella A, Pinzi L, Gambacorta G, Baxendale I, Rastelli G. Identification of potential biological targets of oxindole scaffolds via in silico repositioning strategies. F1000Res 2022; 11:Chem Inf Sci-217. [PMID: 37767081 PMCID: PMC10521104 DOI: 10.12688/f1000research.109017.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/14/2022] [Indexed: 09/29/2023] Open
Abstract
Background: Drug repurposing is an alternative strategy to traditional drug discovery that aims at predicting new uses for already existing drugs or clinical candidates. Drug repurposing has many advantages over traditional drug development, such as reduced attrition rates, time and costs. This is especially the case considering that most drugs investigated for repurposing have already been assessed for their safety in clinical trials. Repurposing campaigns can also be designed for libraries of already synthesized molecules at different levels of biological experimentation, from null to in vitro and in vivo. Such an extension of the "repurposing" concept is expected to provide significant advantages for the identification of novel drugs, as the synthetic accessibility of the desired compounds is often one of the limiting factors in the traditional drug discovery pipeline. Methods: In this work, we performed a computational repurposing campaign on a library of previously synthesized oxindole-based compounds, in order to identify potential new targets for this versatile scaffold. To this aim, ligand-based approaches were firstly applied to evaluate the similarity degree of the investigated compound library, with respect to ligands extracted from the DrugBank, Protein Data Bank (PDB) and ChEMBL databases. In particular, the 2D fingerprint-based and 3D shape-based similarity profiles were evaluated and compared for the oxindole derivates. Results: The analyses predicted a set of potential candidate targets for repurposing, some of them emerging by consensus of different computational analyses. One of the identified targets, i.e., the vascular endothelial growth factor receptor 2 (VEGFR-2) kinase, was further investigated by means of docking calculations, followed by biological testing of one candidate. Conclusions: While the compound did not show potent inhibitory activity towards VEGFR-2, the study highlighted several other possibilities of therapeutically relevant targets that may be worth of consideration for drug repurposing.
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Affiliation(s)
- Annachiara Tinivella
- Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Luca Pinzi
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | | | - Ian Baxendale
- Department of Chemistry, University of Durham, Durham, UK
| | - Giulio Rastelli
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy
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Bonanni D, Citarella A, Moi D, Pinzi L, Bergamini E, Rastelli G. Dual Targeting Strategies On Histone Deacetylase 6 (HDAC6) And Heat Shock Protein 90 (Hsp90). Curr Med Chem 2021; 29:1474-1502. [PMID: 34477503 DOI: 10.2174/0929867328666210902145102] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 07/08/2021] [Accepted: 07/23/2021] [Indexed: 11/22/2022]
Abstract
The design of multi-target drugs acting simultaneously on multiple signaling pathways is a growing field in medicinal chemistry, especially for the treatment of complex diseases such as cancer. Histone deacetylase 6 (HDAC6) is an established anticancer drug target involved in tumor cells transformation. Being an epigenetic enzyme at the interplay of many biological processes, HDAC6 has become an attractive target for polypharmacology studies aimed at improving therapeutic efficacy of anticancer drugs. For example, the molecular chaperone Heat shock protein 90 (Hsp90) is a substrate of HDAC6 deacetylation, and several lines of evidence demonstrate that simultaneous inhibition of HDAC6 and Hsp90 promote synergistic antitumor effects on different cancer cell lines, highlighting the potential benefits of developing a single molecule endowed with multi-target activity. This review will summarize the complex interplay between HDAC6 and Hsp90, providing also useful hints for multi-target drug design and discovery approaches in this field. To this end, crystallographic structures of HDAC6 and Hsp90 complexes will be extensively reviewed in the light of discussing binding pockets features and pharmacophore requirements and providing useful guidelines for the design of dual inhibitors. The few examples of multi-target inhibitors obtained so far, mostly based on chimeric approaches, will be summarized and put into context. Finally, the main features of HDAC6 and Hsp90 inhibitors will be compared, and ligand- and structure-based strategies potentially useful for the development of small molecular weight dual inhibitors will be proposed and discussed.
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Affiliation(s)
- Davide Bonanni
- Department of Life Sciences, University of Modena and Reggio Emilia Via Campi 183, 41125 Modena, Italy
| | - Andrea Citarella
- Department of Life Sciences, University of Modena and Reggio Emilia Via Campi 183, 41125 Modena, Italy
| | - Davide Moi
- Department of Life Sciences, University of Modena and Reggio Emilia Via Campi 183, 41125 Modena, Italy
| | - Luca Pinzi
- Department of Life Sciences, University of Modena and Reggio Emilia Via Campi 183, 41125 Modena, Italy
| | - Elisa Bergamini
- Department of Life Sciences, University of Modena and Reggio Emilia Via Campi 183, 41125 Modena, Italy
| | - Giulio Rastelli
- Department of Life Sciences, University of Modena and Reggio Emilia Via Campi 183, 41125 Modena, Italy
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Pinzi L, Tinivella A, Gagliardelli L, Beneventano D, Rastelli G. LigAdvisor: a versatile and user-friendly web-platform for drug design. Nucleic Acids Res 2021; 49:W326-W335. [PMID: 34023895 PMCID: PMC8262749 DOI: 10.1093/nar/gkab385] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 04/19/2021] [Accepted: 04/27/2021] [Indexed: 12/17/2022] Open
Abstract
Although several tools facilitating in silico drug design are available, their results are usually difficult to integrate with publicly available information or require further processing to be fully exploited. The rational design of multi-target ligands (polypharmacology) and the repositioning of known drugs towards unmet therapeutic needs (drug repurposing) have raised increasing attention in drug discovery, although they usually require careful planning of tailored drug design strategies. Computational tools and data-driven approaches can help to reveal novel valuable opportunities in these contexts, as they enable to efficiently mine publicly available chemical, biological, clinical, and disease-related data. Based on these premises, we developed LigAdvisor, a data-driven webserver which integrates information reported in DrugBank, Protein Data Bank, UniProt, Clinical Trials and Therapeutic Target Database into an intuitive platform, to facilitate drug discovery tasks as drug repurposing, polypharmacology, target fishing and profiling. As designed, LigAdvisor enables easy integration of similarity estimation results with clinical data, thereby allowing a more efficient exploitation of information in different drug discovery contexts. Users can also develop customizable drug design tasks on their own molecules, by means of ligand- and target-based search modes, and download their results. LigAdvisor is publicly available at https://ligadvisor.unimore.it/.
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Affiliation(s)
- Luca Pinzi
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena 41125, Italy
| | - Annachiara Tinivella
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena 41125, Italy.,Clinical and Experimental Medicine, PhD Program, University of Modena and Reggio Emilia, Modena 41125, Italy
| | - Luca Gagliardelli
- Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Modena 41125, Italy
| | - Domenico Beneventano
- Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Modena 41125, Italy
| | - Giulio Rastelli
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena 41125, Italy
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11
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Rastelli G. Meet Our Editorial Board Member. Curr Med Chem 2021. [DOI: 10.2174/092986732815210505120530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Giulio Rastelli
- Department of Life Sciences University of Modena and Reggio Emilia Modena,Italy
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Abstract
INTRODUCTION Molecular docking has been consolidated as one of the most important methods in the molecular modeling field. It has been recognized as a prominent tool in the study of protein-ligand complexes, to describe intermolecular interactions, to accurately predict poses of multiple ligands, to discover novel promising bioactive compounds. Molecular docking methods have evolved in terms of their accuracy and reliability; but there are pending issues to solve for improving the connection between the docking results and the experimental evidence. AREAS COVERED In this article, the author reviews very recent innovative molecular docking applications with special emphasis on reverse docking, treatment of protein flexibility, the use of experimental data to guide the selection of docking poses, the application of Quantum mechanics(QM) in docking, and covalent docking. EXPERT OPINION There are several issues being worked on in recent years that will lead to important breakthroughs in molecular docking methods in the near future These developments are related to more efficient exploration of large datasets and receptor conformations, advances in electronic description, and the use of structural information for guiding the selection of results.
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Affiliation(s)
- Julio Caballero
- Departamento De Bioinformática, Centro De Bioinformática, Simulación Y Modelado (CBSM), Facultad De Ingeniería, Universidad De Talca, Talca, Chile
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13
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Chaudhari R, Fong LW, Tan Z, Huang B, Zhang S. An up-to-date overview of computational polypharmacology in modern drug discovery. Expert Opin Drug Discov 2020; 15:1025-1044. [PMID: 32452701 PMCID: PMC7415563 DOI: 10.1080/17460441.2020.1767063] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 05/06/2020] [Indexed: 12/30/2022]
Abstract
INTRODUCTION In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success. AREAS COVERED In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies. EXPERT OPINION Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.
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Affiliation(s)
- Rajan Chaudhari
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
| | - Long Wolf Fong
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
- MD Anderson UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Avenue, Houston, Texas 77030, United States
| | - Zhi Tan
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
| | - Beibei Huang
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
| | - Shuxing Zhang
- Intelligent Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030, United States
- MD Anderson UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Avenue, Houston, Texas 77030, United States
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Singh N, Chaput L, Villoutreix BO. Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace. Brief Bioinform 2020; 22:1790-1818. [PMID: 32187356 PMCID: PMC7986591 DOI: 10.1093/bib/bbaa034] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.
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Affiliation(s)
- Natesh Singh
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Ludovic Chaput
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Bruno O Villoutreix
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
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