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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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2
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Isigkeit L, Kärcher A, Adouvi G, Arifi S, Merk D. Rational design and virtual screening identify mimetics of the RXR agonist valerenic acid. ChemMedChem 2024; 19:e202300379. [PMID: 38235922 DOI: 10.1002/cmdc.202300379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 12/23/2023] [Accepted: 01/17/2024] [Indexed: 01/19/2024]
Abstract
The ligand-sensing transcription factor retinoid X receptor (RXR) is the universal heterodimer partner of nuclear receptors and involved in multiple physiological processes. Its pharmacological modulation holds therapeutic potential in cancer and neurodegeneration but many available RXR ligands lack specificity. The sesquiterpenoid valerenic acid has been identified as RXR agonist with unprecedented subtype and homodimer preference. Here, we identified simplified mimetics of the complex natural product by rational design and virtual screening that exhibited similar activity profiles on RXR and informed about structural elements contributing to the favorable activity.
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Affiliation(s)
- Laura Isigkeit
- Goethe University Frankfurt, Institute of Pharmaceutical Chemistry, 60438, Frankfurt, Germany
| | - Annette Kärcher
- Goethe University Frankfurt, Institute of Pharmaceutical Chemistry, 60438, Frankfurt, Germany
| | - Gustave Adouvi
- Goethe University Frankfurt, Institute of Pharmaceutical Chemistry, 60438, Frankfurt, Germany
| | - Silvia Arifi
- Goethe University Frankfurt, Institute of Pharmaceutical Chemistry, 60438, Frankfurt, Germany
| | - Daniel Merk
- Goethe University Frankfurt, Institute of Pharmaceutical Chemistry, 60438, Frankfurt, Germany
- Ludwig-Maximilians-Universität München, Department of Pharmacy, 81377, Munich, Germany
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3
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Adeoye AO, Porta DJ, Rivoira MA, Garcia NH. Pharmacoinformatics studies of coenzyme Q10 and potassium polyacrylate on angiotensin-converting enzyme associated with hypertension. J Biomol Struct Dyn 2023:1-12. [PMID: 37667993 DOI: 10.1080/07391102.2023.2254395] [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: 05/08/2023] [Accepted: 08/25/2023] [Indexed: 09/06/2023]
Abstract
Coenzyme Q10's (CoQ10) favorable impact on cardiovascular diseases risk factors like hypertension and atherosclerosis is linked to the antioxidant action of CoQ10 in these conditions. This study showed the possible effects of CoQ10, potassium polyacrylate (PCK), and valsartan, a reference drug, on the angiotensin-converting enzyme (ACE), a crucial component of the renin-angiotensin system. The Glide tool on Maestro 11.1 was used to calculate the respective binding affinity and binding energy of these compounds towards ACE. The Schrödinger suite was used to run molecular dynamic simulations for 100 ns. The pkCSM tool was used to forecast the pharmacokinetic characteristics and toxicological effects. The SwissADME server was used to estimate the drug-like properties of these compounds. Based on their corresponding scoring values and the negative values of the binding free energies, molecular docking analysis of CoQ10 and PCK revealed that both exhibited favorable binding affinities towards the ACE, with CoQ10 having the highest binding scores. The results showed that both CoQ10 and PCK and the reference drug, valsartan, have some amino acids in common (at the pocket site of ACE) as the key residues for binding to ACE. Both CoQ10 and PCK demonstrated drug-like qualities and were not harmful, according to the predicted pharmacokinetics and toxicology studies. The results of this study suggest that because of its inhibitory interactions with ACE, CoQ10 in particular could be useful in regulating and reducing hypertension.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Akinwunmi O Adeoye
- INICSA, Enrique Barros Pabellón Biología Celular, Ciudad Universitaria, Córdoba, Argentina
- Department of Biochemistry, Federal University Oye-Ekiti, Oye, Nigeria
| | - Daniela J Porta
- INICSA, Enrique Barros Pabellón Biología Celular, Ciudad Universitaria, Córdoba, Argentina
| | - María A Rivoira
- INICSA, Enrique Barros Pabellón Biología Celular, Ciudad Universitaria, Córdoba, Argentina
| | - Néstor H Garcia
- INICSA, Enrique Barros Pabellón Biología Celular, Ciudad Universitaria, Córdoba, Argentina
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4
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Ghorayshian A, Danesh M, Mostashari-Rad T, fassihi A. Discovery of novel RARα agonists using pharmacophore-based virtual screening, molecular docking, and molecular dynamics simulation studies. PLoS One 2023; 18:e0289046. [PMID: 37616260 PMCID: PMC10449137 DOI: 10.1371/journal.pone.0289046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 07/10/2023] [Indexed: 08/26/2023] Open
Abstract
Nuclear retinoic acid receptors (RARs) are ligand-dependent transcription factors involved in various biological processes, such as embryogenesis, cell proliferation, differentiation, reproduction, and apoptosis. These receptors are regulated by retinoids, i.e., retinoic acid (RA) and its analogs, as receptor agonists. RAR agonists are promising therapeutic agents for the treatment of serious dermatological disorders, including some malignant conditions. By inducing apoptosis, they are able to inhibit the proliferation of diverse cancer cell lines. Also, RAR agonists have recently been identified as therapeutic options for some neurodegenerative diseases. These features make retinoids very attractive molecules for medical purposes. Synthetic selective RAR agonists have several advantages over endogenous ones, but they suffer poor pharmacokinetic properties. These compounds are normally lipophilic acids with unfavorable drug-like features such as poor oral bioavailability. Recently, highly selective, potent, and less toxic RAR agonists with proper lipophilicity, thus, good oral bioavailability have been developed for some therapeutic applications. In the present study, ligand and structure-based virtual screening technique was exploited to introduce some novel RARα agonists. Pharmacokinetic assessment was also performed in silico to suggest those compounds which have optimized drug-like features. Finally, two compounds with the best in silico pharmacological features are proposed as lead molecules for future development of RARα agonists.
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Affiliation(s)
- Atefeh Ghorayshian
- Department of Cell and Molecular Biology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
| | - Mahshid Danesh
- Functional Genomics & System Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, University of Wuerzburg, Wuerzburg, Germany
| | - Tahereh Mostashari-Rad
- Department of Artificial Intelligence, Smart University of Medical Sciences, Tehran, Iran
| | - Afshin fassihi
- Department of Medicinal Chemistry, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
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5
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Adouvi G, Isigkeit L, López-García Ú, Chaikuad A, Marschner JA, Schubert-Zsilavecz M, Merk D. Rational Design of a New RXR Agonist Scaffold Enabling Single-Subtype Preference for RXRα, RXRβ, and RXRγ. J Med Chem 2023; 66:333-344. [PMID: 36533416 DOI: 10.1021/acs.jmedchem.2c01266] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The three retinoid X receptor subtypes (RXRα, RXRβ, RXRγ) exhibit critical regulatory roles in cell proliferation and differentiation, metabolism, and inflammation. Due to their importance in nuclear receptor signaling, RXRs are widely distributed and pan-RXR agonists cause adverse effects, but the three highly conserved RXR ligand binding sites render the development of subtype-selective ligands a major challenge. We have fused elements of known RXR ligands to obtain a new RXR agonist chemotype on which minor structural modifications enabled the development of tools with single-subtype preference for RXRα, RXRβ, and RXRγ. Molecular modeling indicated different binding conformations and interaction patterns with the RXR LBDs as factors of preferential binding. In a phenotypic adipocyte differentiation experiment, only the RXRα preferential tool enhanced the adipogenic effects of pioglitazone, suggesting this subtype as particularly relevant in adipogenesis and highlighting the set of subtype-preferential RXR agonist tools as suitable for functional cellular studies.
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Affiliation(s)
- Gustave Adouvi
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, 60438 Frankfurt, Germany
| | - Laura Isigkeit
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, 60438 Frankfurt, Germany
| | - Úrsula López-García
- Department of Pharmacy, Ludwig-Maximilians-Universität München,81377 Munich, Germany
| | - Apirat Chaikuad
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, 60438 Frankfurt, Germany
| | - Julian A Marschner
- Department of Pharmacy, Ludwig-Maximilians-Universität München,81377 Munich, Germany
| | | | - Daniel Merk
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, 60438 Frankfurt, Germany.,Department of Pharmacy, Ludwig-Maximilians-Universität München,81377 Munich, Germany
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6
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Mathai N, Chen Y, Kirchmair J. Validation strategies for target prediction methods. Brief Bioinform 2021; 21:791-802. [PMID: 31220208 PMCID: PMC7299289 DOI: 10.1093/bib/bbz026] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 01/14/2019] [Accepted: 02/17/2019] [Indexed: 12/11/2022] Open
Abstract
Computational methods for target prediction, based on molecular similarity and network-based approaches, machine learning, docking and others, have evolved as valuable and powerful tools to aid the challenging task of mode of action identification for bioactive small molecules such as drugs and drug-like compounds. Critical to discerning the scope and limitations of a target prediction method is understanding how its performance was evaluated and reported. Ideally, large-scale prospective experiments are conducted to validate the performance of a model; however, this expensive and time-consuming endeavor is often not feasible. Therefore, to estimate the predictive power of a method, statistical validation based on retrospective knowledge is commonly used. There are multiple statistical validation techniques that vary in rigor. In this review we discuss the validation strategies employed, highlighting the usefulness and constraints of the validation schemes and metrics that are employed to measure and describe performance. We address the limitations of measuring only generalized performance, given that the underlying bioactivity and structural data are biased towards certain small-molecule scaffolds and target families, and suggest additional aspects of performance to consider in order to produce more detailed and realistic estimates of predictive power. Finally, we describe the validation strategies that were employed by some of the most thoroughly validated and accessible target prediction methods.
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Affiliation(s)
- Neann Mathai
- Department of Chemistry, University of Bergen, Bergen, Norway.,Computational Biology Unit (CBU), University of Bergen, Bergen, Norway.,Center for Bioinformatics (ZBH), Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Hamburg, Germany
| | - Ya Chen
- Center for Bioinformatics (ZBH), Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Hamburg, Germany
| | - Johannes Kirchmair
- Department of Chemistry, University of Bergen, Bergen, Norway.,Computational Biology Unit (CBU), University of Bergen, Bergen, Norway.,Center for Bioinformatics (ZBH), Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Hamburg, Germany
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7
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Willems S, Zaienne D, Merk D. Targeting Nuclear Receptors in Neurodegeneration and Neuroinflammation. J Med Chem 2021; 64:9592-9638. [PMID: 34251209 DOI: 10.1021/acs.jmedchem.1c00186] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Nuclear receptors, also known as ligand-activated transcription factors, regulate gene expression upon ligand signals and present as attractive therapeutic targets especially in chronic diseases. Despite the therapeutic relevance of some nuclear receptors in various pathologies, their potential in neurodegeneration and neuroinflammation is insufficiently established. This perspective gathers preclinical and clinical data for a potential role of individual nuclear receptors as future targets in Alzheimer's disease, Parkinson's disease, and multiple sclerosis, and concomitantly evaluates the level of medicinal chemistry targeting these proteins. Considerable evidence suggests the high promise of ligand-activated transcription factors to counteract neurodegenerative diseases with a particularly high potential of several orphan nuclear receptors. However, potent tools are lacking for orphan receptors, and limited central nervous system exposure or insufficient selectivity also compromises the suitability of well-studied nuclear receptor ligands for functional studies. Medicinal chemistry efforts are needed to develop dedicated high-quality tool compounds for the therapeutic validation of nuclear receptors in neurodegenerative pathologies.
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Affiliation(s)
- Sabine Willems
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Strasse 9, 60438 Frankfurt, Germany
| | - Daniel Zaienne
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Strasse 9, 60438 Frankfurt, Germany
| | - Daniel Merk
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Strasse 9, 60438 Frankfurt, Germany
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8
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Abstract
Molecular descriptors encode a variety of molecular representations for computer-assisted drug discovery. Here, we focus on the Weighted Holistic Atom Localization and Entity Shape (WHALES) descriptors, which were originally designed for scaffold hopping from natural products to synthetic molecules. WHALES descriptors capture molecular shape and partial charges simultaneously. We introduce the key aspects of the WHALES concept and provide a step-by-step guide on how to use these descriptors for virtual compound screening and scaffold hopping. The results presented can be reproduced by using the code freely available from URL: github.com/ETHmodlab/scaffold_hopping_whales .
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Affiliation(s)
- Francesca Grisoni
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Zurich, Switzerland.
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Zurich, Switzerland
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9
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Chaikuad A, Pollinger J, Rühl M, Ni X, Kilu W, Heering J, Merk D. Comprehensive Set of Tertiary Complex Structures and Palmitic Acid Binding Provide Molecular Insights into Ligand Design for RXR Isoforms. Int J Mol Sci 2020; 21:E8457. [PMID: 33187070 PMCID: PMC7697888 DOI: 10.3390/ijms21228457] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 11/06/2020] [Accepted: 11/09/2020] [Indexed: 01/10/2023] Open
Abstract
The retinoid X receptor (RXR) is a ligand-sensing transcription factor acting mainly as a universal heterodimer partner for other nuclear receptors. Despite presenting as a potential therapeutic target for cancer and neurodegeneration, adverse effects typically observed for RXR agonists, likely due to the lack of isoform selectivity, limit chemotherapeutic application of currently available RXR ligands. The three human RXR isoforms exhibit different expression patterns; however, they share high sequence similarity, presenting a major obstacle toward the development of subtype-selective ligands. Here, we report the discovery of the saturated fatty acid, palmitic acid, as an RXR ligand and disclose a uniform set of crystal structures of all three RXR isoforms in an active conformation induced by palmitic acid. A structural comparison revealed subtle differences among the RXR subtypes. We also observed an ability of palmitic acid as well as myristic acid and stearic acid to induce recruitment of steroid receptor co-activator 1 to the RXR ligand-binding domain with low micromolar potencies. With the high, millimolar endogenous concentrations of these highly abundant lipids, our results suggest their potential involvement in RXR signaling.
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Affiliation(s)
- Apirat Chaikuad
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, 60438 Frankfurt, Germany; (J.P.); (M.R.); (X.N.); (W.K.)
- Structural Genomics Consortium, BMLS, Goethe-University Frankfurt, Max-von-Laue-Str. 15, 60438 Frankfurt, Germany
| | - Julius Pollinger
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, 60438 Frankfurt, Germany; (J.P.); (M.R.); (X.N.); (W.K.)
| | - Michael Rühl
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, 60438 Frankfurt, Germany; (J.P.); (M.R.); (X.N.); (W.K.)
| | - Xiaomin Ni
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, 60438 Frankfurt, Germany; (J.P.); (M.R.); (X.N.); (W.K.)
- Structural Genomics Consortium, BMLS, Goethe-University Frankfurt, Max-von-Laue-Str. 15, 60438 Frankfurt, Germany
| | - Whitney Kilu
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, 60438 Frankfurt, Germany; (J.P.); (M.R.); (X.N.); (W.K.)
| | - Jan Heering
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Branch for Translational Medicine and Pharmacology TMP, Theodor-Stern-Kai 7, 60596 Frankfurt, Germany;
| | - Daniel Merk
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, 60438 Frankfurt, Germany; (J.P.); (M.R.); (X.N.); (W.K.)
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10
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Pollinger J, Schierle S, Gellrich L, Ohrndorf J, Kaiser A, Heitel P, Chaikuad A, Knapp S, Merk D. A Novel Biphenyl-based Chemotype of Retinoid X Receptor Ligands Enables Subtype and Heterodimer Preferences. ACS Med Chem Lett 2019; 10:1346-1352. [PMID: 31531208 DOI: 10.1021/acsmedchemlett.9b00306] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 08/16/2019] [Indexed: 12/11/2022] Open
Abstract
The nuclear retinoid X receptors (RXRs) are key ligand sensing transcription factors that serve as universal nuclear receptor heterodimer partners and are thus involved in numerous physiological processes. Therapeutic targeting of RXRs holds high potential but available RXR activators suffer from limited safety. Selectivity for RXR subtypes or for certain RXR heterodimers are promising strategies for safer RXR modulation. Here, we report systematic structure-activity relationship studies on biphenyl carboxylates as new RXR ligand chemotype. We discovered specific structural modifications that enhance potency on RXRs, govern subtype preference, and vary modulation of different RXR heterodimers. Fusion of these structural motifs enabled specific tuning of subtype preferential profiles with markedly improved potency. Our results provide further evidence that RXR subtype selective ligands can be designed and present a novel chemotype of RXR modulators that can be tuned for subtype and heterodimer preferences.
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Affiliation(s)
- Julius Pollinger
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, D-60438 Frankfurt, Germany
| | - Simone Schierle
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, D-60438 Frankfurt, Germany
| | - Leonie Gellrich
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, D-60438 Frankfurt, Germany
| | - Julia Ohrndorf
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, D-60438 Frankfurt, Germany
| | - Astrid Kaiser
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, D-60438 Frankfurt, Germany
| | - Pascal Heitel
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, D-60438 Frankfurt, Germany
| | - Apirat Chaikuad
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, D-60438 Frankfurt, Germany
| | - Stefan Knapp
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, D-60438 Frankfurt, Germany
| | - Daniel Merk
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 9, D-60438 Frankfurt, Germany
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11
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Button A, Merk D, Hiss JA, Schneider G. Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis. NAT MACH INTELL 2019. [DOI: 10.1038/s42256-019-0067-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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12
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Krężel W, Rühl R, de Lera AR. Alternative retinoid X receptor (RXR) ligands. Mol Cell Endocrinol 2019; 491:110436. [PMID: 31026478 DOI: 10.1016/j.mce.2019.04.016] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 04/06/2019] [Accepted: 04/22/2019] [Indexed: 12/15/2022]
Abstract
Retinoid X receptors (RXRs) control a wide variety of functions by virtue of their dimerization with other nuclear hormone receptors (NRs), contributing thereby to activities of different signaling pathways. We review known RXR ligands as transcriptional modulators of specific RXR-dimers and the associated biological processes. We also discuss the physiological relevance of such ligands, which remains frequently a matter of debate and which at present is best met by member(s) of a novel family of retinoids, postulated as Vitamin A5. Through comparison with other natural, but also with synthetic ligands, we discuss high diversity in the modes of ligand binding to RXRs resulting in agonistic or antagonistic profiles and selectivity towards specific subtypes of permissive heterodimers. Despite such diversity, direct ligand binding to the ligand binding pocket resulting in agonistic activity was preferentially preserved in the course of animal evolution pointing to its functional relevance, and potential for existence of other, species-specific endogenous RXR ligands sharing the same mode of function.
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Affiliation(s)
- Wojciech Krężel
- Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France; Centre National de la Recherche Scientifique, UMR 7104, Illkirch, France; Institut National de la Santé et de la Recherche Médicale, U 1258, Illkirch, France; Université de Strasbourg, Illkirch, France.
| | - Ralph Rühl
- Paprika Bioanalytics BT, Debrecen, Hungary
| | - Angel R de Lera
- Departamento de Química Orgánica, Facultade de Química, Lagoas-Marcosende, 36310, Vigo, Spain
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13
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Grisoni F, Merk D, Friedrich L, Schneider G. Design of Natural-Product-Inspired Multitarget Ligands by Machine Learning. ChemMedChem 2019; 14:1129-1134. [PMID: 30973672 DOI: 10.1002/cmdc.201900097] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 04/02/2019] [Indexed: 11/08/2022]
Abstract
A virtual screening protocol based on machine learning models was used to identify mimetics of the natural product (-)-galantamine. This fully automated approach identified eight compounds with bioactivities on at least one of the macromolecular targets of (-)-galantamine, with different polypharmacological profiles. Two of the computer-generated hits possess an expanded spectrum of bioactivity on targets relevant to the treatment of Alzheimer's disease and are suitable for hit-to-lead expansion. These results advocate multitarget drug design by advanced virtual screening protocols based on chemically informed machine learning models.
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Affiliation(s)
- Francesca Grisoni
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Daniel Merk
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Lukas Friedrich
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
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14
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Grisoni F, Consonni V, Ballabio D. Machine Learning Consensus To Predict the Binding to the Androgen Receptor within the CoMPARA Project. J Chem Inf Model 2019; 59:1839-1848. [PMID: 30668916 DOI: 10.1021/acs.jcim.8b00794] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The nuclear androgen receptor (AR) is one of the most relevant biological targets of Endocrine Disrupting Chemicals (EDCs), which produce adverse effects by interfering with hormonal regulation and endocrine system functioning. This paper describes novel in silico models to identify organic AR modulators in the context of the Collaborative Modeling Project of Androgen Receptor Activity (CoMPARA), coordinated by the National Center of Computational Toxicology (U.S. Environmental Protection Agency). The collaborative project involved 35 international research groups to prioritize the experimental tests of approximatively 40k compounds, based on the predictions provided by each participant. In this paper, we describe our machine learning approach to predict the binding to AR, which is based on a consensus of a multivariate Bernoulli Naive Bayes, a Random Forest, and N-Nearest Neighbor classification models. The approach was developed in compliance with the Organization of Economic Cooperation and Development (OECD) principles, trained on 1687 ToxCast molecules classified according to 11 in vitro assays, and further validated on a set of 3,882 external compounds. The models provided robust and reliable predictions and were used to gather novel data-driven insights on the structural features related to AR binding, agonism, and antagonism.
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Affiliation(s)
- Francesca Grisoni
- Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences , University of Milano-Bicocca , piazza della Scienza 1 , IT-20126 Milano , Italy
| | - Viviana Consonni
- Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences , University of Milano-Bicocca , piazza della Scienza 1 , IT-20126 Milano , Italy
| | - Davide Ballabio
- Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences , University of Milano-Bicocca , piazza della Scienza 1 , IT-20126 Milano , Italy
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15
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Merk D, Grisoni F, Schaller K, Friedrich L, Schneider G. Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning. ChemistryOpen 2019; 8:7-14. [PMID: 30622878 PMCID: PMC6317935 DOI: 10.1002/open.201800156] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Indexed: 11/23/2022] Open
Abstract
The bile acid activated transcription factor farnesoid X receptor (FXR) has revealed therapeutic potential as a molecular drug target for the treatment of hepatic and metabolic disorders. Despite strong efforts in FXR ligand development, the structural diversity among the known FXR modulators is limited. Only four molecular frameworks account for more than 50 % of the FXR modulators annotated in ChEMBL. Here, we leverage machine learning methods to expand the chemical space of FXR-targeting small molecules by employing an ensemble of three complementary machine learning approaches. A counter-propagation artificial neural network, a k-nearest neighbor learner, and a three-dimensional pharmacophore descriptor were combined to retrieve novel FXR ligands from a collection of more than 3 million compounds. The ensemble machine learning model identified six new FXR modulators among ten top-ranked candidates. These active hits comprise both FXR activators and antagonists with micromolar potencies. With four novel FXR ligand scaffolds, these computationally identified bioactive compounds appreciably expand the chemical space of known FXR modulators and may serve as starting points for hit-to-lead expansion.
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Affiliation(s)
- Daniel Merk
- Department of Chemistry and Applied BiosciencesSwiss Federal Institute of Technology (ETH) ZurichVladimir-Prelog-Weg 48093ZurichSwitzerland
| | - Francesca Grisoni
- Department of Chemistry and Applied BiosciencesSwiss Federal Institute of Technology (ETH) ZurichVladimir-Prelog-Weg 48093ZurichSwitzerland
- Department of Earth and Environmental SciencesUniversity of Milano-BicoccaPiazza della Scienza 120126MilanoItaly
| | - Kay Schaller
- Department of Chemistry and Applied BiosciencesSwiss Federal Institute of Technology (ETH) ZurichVladimir-Prelog-Weg 48093ZurichSwitzerland
| | - Lukas Friedrich
- Department of Chemistry and Applied BiosciencesSwiss Federal Institute of Technology (ETH) ZurichVladimir-Prelog-Weg 48093ZurichSwitzerland
| | - Gisbert Schneider
- Department of Chemistry and Applied BiosciencesSwiss Federal Institute of Technology (ETH) ZurichVladimir-Prelog-Weg 48093ZurichSwitzerland
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Khalid S, Zahid MA, Ali H, Kim YS, Khan S. Biaryl scaffold-focused virtual screening for anti-aggregatory and neuroprotective effects in Alzheimer's disease. BMC Neurosci 2018; 19:74. [PMID: 30424732 PMCID: PMC6234579 DOI: 10.1186/s12868-018-0472-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Accepted: 11/03/2018] [Indexed: 02/06/2023] Open
Abstract
Background Alzheimer’s disease (AD) is a primary cause of dementia in ageing population affecting more than 35 million people around the globe. It is a chronic neurodegenerative disease caused by defected folding and aggregation of amyloid beta (Aβ) protein. Aβ is formed by the cleavage of membrane embedded amyloid precursor protein (APP) by using enzyme ‘transmembrane aspartyl protease, β-secretase’. Inhibition of β-secretase is a viable strategy to prevent neurotoxicity in AD. Another strategy in the treatment of AD is inhibition of acetylcholinesterase. This inhibition reduces the degradation of acetylcholine and temporarily restores the cholinergic function of neurons and improves cognitive function. Monoamine oxidase and higher glutamate levels are also found to be linked with Aβ peptide related oxidative stress. Oxidative stress leads to reduced activity of glutamate synthase resulting in significantly higher level of glutamate in brain. The aim of this study is to perform in silico screening of a virtual library of biaryl scaffold containing compounds potentially used for the treatment of AD. Screening was done against the primary targets of AD therapeutics, acetylcholinesterase, β-secretase (BACE1), Monoamine oxidases (MAO) and N-Methyl-D-aspartate (NMDA) receptor. Compounds were screened for their inhibitory potential by employing molecular docking approach using AutoDock vina. Binding energy scores were embodied in the heatmap to display varies strengths of interactions of the ligands targeting AD. Results Several ligands showed notable interaction with at least two targets, but the strong interaction with all the targets is shown by very few ligands. The pharmacokinetics of the interacting ligands was also predicted. The interacting ligands have good drug-likeness and brain availability essential for drugs with intracranial targets. Conclusion These results suggest that biaryl scaffold may be pliable to drug development for neuroprotection in AD and that the synthesis of further analogues to optimize these properties should be considered. Electronic supplementary material The online version of this article (10.1186/s12868-018-0472-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sidra Khalid
- Department of Pharmacy, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan
| | - Muhammad Ammar Zahid
- Department of Pharmacy, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan.,Department of Biotechnology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Hussain Ali
- Department of Pharmacy, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan
| | - Yeong S Kim
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul, South Korea
| | - Salman Khan
- Department of Pharmacy, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan.
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Grisoni F, Merk D, Byrne R, Schneider G. Scaffold-Hopping from Synthetic Drugs by Holistic Molecular Representation. Sci Rep 2018; 8:16469. [PMID: 30405170 PMCID: PMC6220272 DOI: 10.1038/s41598-018-34677-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Accepted: 10/16/2018] [Indexed: 12/31/2022] Open
Abstract
The discovery of novel ligand chemotypes allows to explore uncharted regions in chemical space, thereby potentially improving synthetic accessibility, potency, and the drug-likeness of molecules. Here, we demonstrate the scaffold-hopping ability of the new Weighted Holistic Atom Localization and Entity Shape (WHALES) molecular descriptors compared to seven state-of-the-art molecular representations on 30,000 compounds and 182 biological targets. In a prospective application, we apply WHALES to the discovery of novel retinoid X receptor (RXR) modulators. WHALES descriptors identified four agonists with innovative molecular scaffolds, populating uncharted regions of the chemical space. One of the agonists, possessing a rare non-acidic chemotype, revealed high selectivity on 12 nuclear receptors and comparable efficacy as bexarotene on induction of ATP-binding cassette transporter A1, angiopoietin like protein 4 and apolipoprotein E. The outcome of this research supports WHALES as an innovative tool to explore novel regions of the chemical space and to detect novel bioactive chemotypes by straightforward similarity searching.
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Affiliation(s)
- Francesca Grisoni
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093, Zurich, Switzerland. .,Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, IT-20126, Milano, Italy.
| | - Daniel Merk
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093, Zurich, Switzerland
| | - Ryan Byrne
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093, Zurich, Switzerland
| | - Gisbert Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093, Zurich, Switzerland.
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Merk D, Grisoni F, Friedrich L, Schneider G. Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators. Commun Chem 2018. [DOI: 10.1038/s42004-018-0068-1] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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