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Škuta C, Cortés-Ciriano I, Dehaen W, Kříž P, van Westen GJP, Tetko IV, Bender A, Svozil D. QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping. J Cheminform 2020; 12:39. [PMID: 33431038 PMCID: PMC7260783 DOI: 10.1186/s13321-020-00443-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 05/16/2020] [Indexed: 02/11/2023] Open
Abstract
An affinity fingerprint is the vector consisting of compound’s affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based affinity fingerprint, components of which are predicted by Random Forest regression models trained on bioactivity data from the ChEMBL database. Both real-valued (rv-QAFFP) and binary (b-QAFFP) versions of the QAFFP fingerprint were implemented and their performance in similarity searching, biological activity classification and scaffold hopping was assessed and compared to that of the 1024 bits long Morgan2 fingerprint (the RDKit implementation of the ECFP4 fingerprint). In both similarity searching and biological activity classification, the QAFFP fingerprint yields retrieval rates, measured by AUC (~ 0.65 and ~ 0.70 for similarity searching depending on data sets, and ~ 0.85 for classification) and EF5 (~ 4.67 and ~ 5.82 for similarity searching depending on data sets, and ~ 2.10 for classification), comparable to that of the Morgan2 fingerprint (similarity searching AUC of ~ 0.57 and ~ 0.66, and EF5 of ~ 4.09 and ~ 6.41, depending on data sets, classification AUC of ~ 0.87, and EF5 of ~ 2.16). However, the QAFFP fingerprint outperforms the Morgan2 fingerprint in scaffold hopping as it is able to retrieve 1146 out of existing 1749 scaffolds, while the Morgan2 fingerprint reveals only 864 scaffolds.![]()
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Affiliation(s)
- C Škuta
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20, Prague 4, Czech Republic
| | - I Cortés-Ciriano
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - W Dehaen
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20, Prague 4, Czech Republic.,CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic
| | - P Kříž
- Department of Mathematics, Faculty of Chemical Engineering, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic
| | - G J P van Westen
- Computational Drug Discovery, Drug Discovery and Safety, LACDR, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - I V Tetko
- Helmholtz Zentrum Muenchen - German Research Center for Environmental Health (GmbH) and BIGCHEM GmbH, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany
| | - A Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - D Svozil
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20, Prague 4, Czech Republic. .,CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic.
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Lee SY, Song MY, Kim D, Park C, Park DK, Kim DG, Yoo JS, Kim YH. A Proteotranscriptomic-Based Computational Drug-Repositioning Method for Alzheimer's Disease. Front Pharmacol 2020; 10:1653. [PMID: 32063857 PMCID: PMC7000455 DOI: 10.3389/fphar.2019.01653] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 12/17/2019] [Indexed: 12/24/2022] Open
Abstract
Numerous clinical trials of drug candidates for Alzheimer’s disease (AD) have failed, and computational drug repositioning approaches using omics data have been proposed as effective alternative approaches to the discovery of drug candidates. However, little multi-omics data is available for AD, due to limited availability of brain tissues. Even if omics data exist, systematic drug repurposing study for AD has suffered from lack of big data, insufficient clinical information, and difficulty in data integration on account of sample heterogeneity derived from poor diagnosis or shortage of qualified post-mortem tissue. In this study, we developed a proteotranscriptomic-based computational drug repositioning method named Drug Repositioning Perturbation Score/Class (DRPS/C) based on inverse associations between disease- and drug-induced gene and protein perturbation patterns, incorporating pharmacogenomic knowledge. We constructed a Drug-induced Gene Perturbation Signature Database (DGPSD) comprised of 61,019 gene signatures perturbed by 1,520 drugs from the Connectivity Map (CMap) and the L1000 CMap. Drugs were classified into three DRPCs (High, Intermediate, and Low) according to DRPSs that were calculated using drug- and disease-induced gene perturbation signatures from DGPSD and The Cancer Genome Atlas (TCGA), respectively. The DRPS/C method was evaluated using the area under the ROC curve, with a prescribed drug list from TCGA as the gold standard. Glioblastoma had the highest AUC. To predict anti-AD drugs, DRPS were calculated using DGPSD and AD-induced gene/protein perturbation signatures generated from RNA-seq, microarray and proteomic datasets in the Synapse database, and the drugs were classified into DRPCs. We predicted 31 potential anti-AD drug candidates commonly belonged to high DRPCs of transcriptomic and proteomic signatures. Of these, four drugs classified into the nervous system group of Anatomical Therapeutic Chemical (ATC) system are voltage-gated sodium channel blockers (bupivacaine, topiramate) and monamine oxidase inhibitors (selegiline, iproniazid), and their mechanism of action was inferred from a potential anti-AD drug perspective. Our approach suggests a shortcut to discover new efficacy of drugs for AD.
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Affiliation(s)
- Soo Youn Lee
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, South Korea
| | - Min-Young Song
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, South Korea
| | - Dain Kim
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, South Korea
| | - Chaewon Park
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, South Korea
| | - Da Kyeong Park
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, South Korea
| | - Dong Geun Kim
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, South Korea.,Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, South Korea
| | - Jong Shin Yoo
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, South Korea.,Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, South Korea
| | - Young Hye Kim
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, South Korea
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Sangenito LS, Menna-Barreto RFS, d'Avila-Levy CM, Branquinha MH, Santos ALS. Repositioning of HIV Aspartyl Peptidase Inhibitors for Combating the Neglected Human Pathogen Trypanosoma cruzi. Curr Med Chem 2019; 26:6590-6613. [PMID: 31187704 DOI: 10.2174/0929867326666190610152934] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 08/11/2018] [Accepted: 08/23/2018] [Indexed: 12/11/2022]
Abstract
Chagas disease, caused by the flagellate parasite Trypanosoma cruzi, is a wellknown neglected tropical disease. This parasitic illness affects 6-7 million people and can lead to severe myocarditis and/or complications of the digestive tract. The changes in its epidemiology facilitate co-infection with the Human Immunodeficiency Virus (HIV), making even more difficult the diagnosis and prognosis. The parasitic infection is reactivated in T. cruzi/HIV co-infection, with the appearance of unusual manifestations in the chronic phase and the exacerbation of classical clinical signs. The therapeutic arsenal to treat Chagas disease, in all its clinical forms, is restricted basically to two drugs, benznidazole and nifurtimox. Both drugs are extremely toxic and the therapeutic efficacy is still unclear, making the clinical treatment a huge issue to be solved. Therefore, it seems obvious the necessity of new tangible approaches to combat this illness. In this sense, the repositioning of approved drugs appears as an interesting and viable strategy. The discovery of Human Immunodeficiency Virus Aspartyl Peptidase Inhibitors (HIV-PIs) represented a milestone in the treatment of Acquired Immune Deficiency Syndrome (AIDS) and, concomitantly, a marked reduction in both the incidence and prevalence of important bacterial, fungal and parasitic co-infections was clearly observed. Taking all these findings into consideration, the present review summarizes the promising and beneficial data concerning the effects of HIV-PIs on all the evolutionary forms of T. cruzi and in important steps of the parasite's life cycle, which highlight their possible application as alternative drugs to treat Chagas disease.
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Affiliation(s)
- Leandro S Sangenito
- Laboratorio de Estudos Avancados de Microrganismos Emergentes e Resistentes (LEAMER), Departamento de Microbiologia Geral, Instituto de Microbiologia Paulo de Goes (IMPG), Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Rubem F S Menna-Barreto
- Laboratorio de Biologia Celular, Instituto Oswaldo Cruz (IOC), Fundacao Oswaldo Cruz (FIOCRUZ), Rio de Janeiro, Brazil
| | - Cláudia M d'Avila-Levy
- Laboratorio de Estudos Integrados em Protozoologia, Instituto Oswaldo Cruz (IOC), Fundacao Oswaldo Cruz (FIOCRUZ), Rio de Janeiro, Brazil
| | - Marta H Branquinha
- Laboratorio de Estudos Avancados de Microrganismos Emergentes e Resistentes (LEAMER), Departamento de Microbiologia Geral, Instituto de Microbiologia Paulo de Goes (IMPG), Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - André L S Santos
- Laboratorio de Estudos Avancados de Microrganismos Emergentes e Resistentes (LEAMER), Departamento de Microbiologia Geral, Instituto de Microbiologia Paulo de Goes (IMPG), Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil.,Programa de Pós-Graduação em Bioquímica, Instituto de Química, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
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Wu G, Zhao T, Kang D, Zhang J, Song Y, Namasivayam V, Kongsted J, Pannecouque C, De Clercq E, Poongavanam V, Liu X, Zhan P. Overview of Recent Strategic Advances in Medicinal Chemistry. J Med Chem 2019; 62:9375-9414. [PMID: 31050421 DOI: 10.1021/acs.jmedchem.9b00359] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Introducing novel strategies, concepts, and technologies that speed up drug discovery and the drug development cycle is of great importance both in the highly competitive pharmaceutical industry as well as in academia. This Perspective aims to present a "big-picture" overview of recent strategic innovations in medicinal chemistry and drug discovery.
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Affiliation(s)
- Gaochan Wu
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Tong Zhao
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Dongwei Kang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Jian Zhang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Yuning Song
- Department of Clinical Pharmacy , Qilu Hospital of Shandong University , 250012 Ji'nan , China
| | - Vigneshwaran Namasivayam
- Pharmaceutical Institute, Pharmaceutical Chemistry II , University of Bonn , 53121 Bonn , Germany
| | - Jacob Kongsted
- Department of Physics, Chemistry, and Pharmacy , University of Southern Denmark , DK-5230 Odense M , Denmark
| | - Christophe Pannecouque
- Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy , K.U. Leuven , Herestraat 49 Postbus 1043 (09.A097) , B-3000 Leuven , Belgium
| | - Erik De Clercq
- Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy , K.U. Leuven , Herestraat 49 Postbus 1043 (09.A097) , B-3000 Leuven , Belgium
| | - Vasanthanathan Poongavanam
- Department of Physics, Chemistry, and Pharmacy , University of Southern Denmark , DK-5230 Odense M , Denmark
| | - Xinyong Liu
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Peng Zhan
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
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Druzhilovskiy DS, Rudik AV, Filimonov DA, Gloriozova TA, Lagunin AA, Dmitriev AV, Pogodin PV, Dubovskaya VI, Ivanov SM, Tarasova OA, Bezhentsev VM, Murtazalieva KA, Semin MI, Maiorov IS, Gaur AS, Sastry GN, Poroikov VV. Computational platform Way2Drug: from the prediction of biological activity to drug repurposing. Russ Chem Bull 2018. [DOI: 10.1007/s11172-017-1954-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Chen H, Bauer U, Engkvist O. Merged Multiple Ligands. METHODS AND PRINCIPLES IN MEDICINAL CHEMISTRY 2017. [DOI: 10.1002/9783527674381.ch9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Affiliation(s)
- Hongming Chen
- Discovery Sciences, Innovative Medicines and Early Development; AstraZeneca; Pepparedsleden 1 431 83 Mölndal Sweden
| | - Udo Bauer
- Cardiovascular and Metabolic Diseases, Innovative Medicines and Early Development; AstraZeneca; Pepparedsleden 1 431 83 Mölndal Sweden
| | - Ola Engkvist
- Discovery Sciences, Innovative Medicines and Early Development; AstraZeneca; Pepparedsleden 1 431 83 Mölndal Sweden
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Liu T, Altman RB. Relating Essential Proteins to Drug Side-Effects Using Canonical Component Analysis: A Structure-Based Approach. J Chem Inf Model 2015; 55:1483-94. [PMID: 26121262 DOI: 10.1021/acs.jcim.5b00030] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The molecular mechanism of many drug side-effects is unknown and difficult to predict. Previous methods for explaining side-effects have focused on known drug targets and their pathways. However, low affinity binding to proteins that are not usually considered drug targets may also drive side-effects. In order to assess these alternative targets, we used the 3D structures of 563 essential human proteins systematically to predict binding to 216 drugs. We first benchmarked our affinity predictions with available experimental data. We then combined singular value decomposition and canonical component analysis (SVD-CCA) to predict side-effects based on these novel target profiles. Our method predicts side-effects with good accuracy (average AUC: 0.82 for side effects present in <50% of drug labels). We also noted that side-effect frequency is the most important feature for prediction and can confound efforts at elucidating mechanism; our method allows us to remove the contribution of frequency and isolate novel biological signals. In particular, our analysis produces 2768 triplet associations between 50 essential proteins, 99 drugs, and 77 side-effects. Although experimental validation is difficult because many of our essential proteins do not have validated assays, we nevertheless attempted to validate a subset of these associations using experimental assay data. Our focus on essential proteins allows us to find potential associations that would likely be missed if we used recognized drug targets. Our associations provide novel insights about the molecular mechanisms of drug side-effects and highlight the need for expanded experimental efforts to investigate drug binding to proteins more broadly.
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Affiliation(s)
- Tianyun Liu
- †Department of Genetics, Stanford University, Stanford, California 94305, United States
| | - Russ B Altman
- ‡Department of Genetics and Department of Bioengineering, Stanford University, Stanford, California 94305, United States
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Patel H, Lucas X, Bendik I, Günther S, Merfort I. Target Fishing by Cross-Docking to Explain Polypharmacological Effects. ChemMedChem 2015; 10:1209-17. [DOI: 10.1002/cmdc.201500123] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Revised: 04/29/2015] [Indexed: 01/18/2023]
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10
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Li J, Zheng S, Chen B, Butte AJ, Swamidass SJ, Lu Z. A survey of current trends in computational drug repositioning. Brief Bioinform 2015; 17:2-12. [PMID: 25832646 DOI: 10.1093/bib/bbv020] [Citation(s) in RCA: 338] [Impact Index Per Article: 37.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2014] [Indexed: 12/26/2022] Open
Abstract
Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path toward new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. In this review, we show recent advancements in the critical areas of computational drug repositioning from multiple aspects. First, we summarize available data sources and the corresponding computational repositioning strategies. Second, we characterize the commonly used computational techniques. Third, we discuss validation strategies for repositioning studies, including both computational and experimental methods. Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning.
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Leach AG. Predicting the activity and toxicity of new psychoactive substances: a pharmaceutical industry perspective. Drug Test Anal 2013; 6:739-45. [DOI: 10.1002/dta.1593] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2013] [Revised: 11/12/2013] [Accepted: 11/15/2013] [Indexed: 12/15/2022]
Affiliation(s)
- Andrew G. Leach
- Liverpool John Moores University; James Parsons Building, Byrom Street Liverpool L3 3AF UK
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