1
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Mervin L, Voronov A, Kabeshov M, Engkvist O. QSARtuna: An Automated QSAR Modeling Platform for Molecular Property Prediction in Drug Design. J Chem Inf Model 2024; 64:5365-5374. [PMID: 38950185 DOI: 10.1021/acs.jcim.4c00457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Machine-learning (ML) and deep-learning (DL) approaches to predict the molecular properties of small molecules are increasingly deployed within the design-make-test-analyze (DMTA) drug design cycle to predict molecular properties of interest. Despite this uptake, there are only a few automated packages to aid their development and deployment that also support uncertainty estimation, model explainability, and other key aspects of model usage. This represents a key unmet need within the field, and the large number of molecular representations and algorithms (and associated parameters) means it is nontrivial to robustly optimize, evaluate, reproduce, and deploy models. Here, we present QSARtuna, a molecule property prediction modeling pipeline, written in Python and utilizing the Optuna, Scikit-learn, RDKit, and ChemProp packages, which enables the efficient and automated comparison between molecular representations and machine learning models. The platform was developed by considering the increasingly important aspect of model uncertainty quantification and explainability by design. We provide details for our framework and provide illustrative examples to demonstrate the capability of the software when applied to simple molecular property, reaction/reactivity prediction, and DNA encoded library enrichment classification. We hope that the release of QSARtuna will further spur innovation in automatic ML modeling and provide a platform for education of best practices in molecular property modeling. The code for the QSARtuna framework is made freely available via GitHub.
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Affiliation(s)
- Lewis Mervin
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge CB2 0AA, United Kingdom
| | - Alexey Voronov
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 412 96, Sweden
| | - Mikhail Kabeshov
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 412 96, Sweden
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 412 96, Sweden
- Department of Computer Science and Engineering, University of Gothenburg, Chalmers University of Technology, Gothenburg 412 96, Sweden
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2
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Demin KA, Kupriyanova OV, Shevyrin VA, Derzhavina KA, Krotova NA, Ilyin NP, Kolesnikova TO, Galstyan DS, Kositsyn YM, Khaybaev AAS, Seredinskaya MV, Dubrovskii Y, Sadykova RG, Nerush MO, Mor MS, Petersen EV, Strekalova T, Efimova EV, Kuvarzin SR, Yenkoyan KB, Bozhko DV, Myrov VO, Kolchanova SM, Polovian AI, Galumov GK, Kalueff AV. Acute behavioral and Neurochemical Effects of Novel N-Benzyl-2-Phenylethylamine Derivatives in Adult Zebrafish. ACS Chem Neurosci 2022; 13:1902-1922. [PMID: 35671176 DOI: 10.1021/acschemneuro.2c00123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Hallucinogenic drugs potently affect brain and behavior and have also recently emerged as potentially promising agents in pharmacotherapy. Complementing laboratory rodents, the zebrafish (Danio rerio) is a powerful animal model organism for screening neuroactive drugs, including hallucinogens. Here, we test a battery of ten novel N-benzyl-2-phenylethylamine (NBPEA) derivatives with the 2,4- and 3,4-dimethoxy substitutions in the phenethylamine moiety and the -OCH3, -OCF3, -F, -Cl, and -Br substitutions in the ortho position of the phenyl ring of the N-benzyl moiety, assessing their acute behavioral and neurochemical effects in the adult zebrafish. Overall, substitutions in the Overall, substitutions in the N-benzyl moiety modulate locomotion, and substitutions in the phenethylamine moiety alter zebrafish anxiety-like behavior, also affecting the brain serotonin and/or dopamine turnover. The 24H-NBOMe(F) and 34H-NBOMe(F) treatment also reduced zebrafish despair-like behavior. Computational analyses of zebrafish behavioral data by artificial intelligence identified several distinct clusters for these agents, including anxiogenic/hypolocomotor (24H-NBF, 24H-NBOMe, and 34H-NBF), behaviorally inert (34H-NBBr, 34H-NBCl, and 34H-NBOMe), anxiogenic/hallucinogenic-like (24H-NBBr, 24H-NBCl, and 24H-NBOMe(F)), and anxiolytic/hallucinogenic-like (34H-NBOMe(F)) drugs. Our computational analyses also revealed phenotypic similarity of the behavioral activity of some NBPEAs to that of selected conventional serotonergic and antiglutamatergic hallucinogens. In silico functional molecular activity modeling further supported the overlap of the drug targets for NBPEAs tested here and the conventional serotonergic and antiglutamatergic hallucinogens. Overall, these findings suggest potent neuroactive properties of several novel synthetic NBPEAs, detected in a sensitive in vivo vertebrate model system, the zebrafish, raising the possibility of their potential clinical use and abuse.
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Affiliation(s)
- Konstantin A Demin
- Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia.,Almazov National Medical Research Centre, St. Petersburg 197341, Russia
| | - Olga V Kupriyanova
- Institute of Fundamental Medicine and Biology, Kazan Volga Region Federal University, Kazan 420008, Russia.,Kazan State Medical University, Kazan 420012, Russia
| | - Vadim A Shevyrin
- Institute of Chemistry and Technology, Ural Federal University, 19 Mira Str., Ekaterinburg 620002, Russia
| | - Ksenia A Derzhavina
- Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia.,Almazov National Medical Research Centre, St. Petersburg 197341, Russia
| | - Nataliya A Krotova
- Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia.,Almazov National Medical Research Centre, St. Petersburg 197341, Russia
| | - Nikita P Ilyin
- Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia.,Almazov National Medical Research Centre, St. Petersburg 197341, Russia
| | - Tatiana O Kolesnikova
- Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia.,Neurobiology Program, Sirius University of Science and Technology, Sochi 354340, Russia
| | - David S Galstyan
- Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia.,Laboratory of Preclinical Bioscreening, Granov Russian Research Center of Radiology and Surgical Technologies, Ministry of Healthcare of Russian Federation, Pesochny 197758, Russia
| | - Yurii M Kositsyn
- Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia
| | | | - Maria V Seredinskaya
- Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia
| | - Yaroslav Dubrovskii
- Almazov National Medical Research Centre, St. Petersburg 197341, Russia.,Institute of Chemistry, St. Petersburg State University, St. Petersburg 199034, Russia.,St. Petersburg State Chemical Pharmaceutical University, St. Petersburg 197022, Russia
| | | | - Maria O Nerush
- Almazov National Medical Research Centre, St. Petersburg 197341, Russia
| | - Mikael S Mor
- Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia
| | - Elena V Petersen
- Moscow Institute of Physics and Technology, Moscow 141701, Russia
| | | | - Evgeniya V Efimova
- Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia
| | - Savelii R Kuvarzin
- Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia
| | - Konstantin B Yenkoyan
- Neuroscience Laboratory, COBRAIN Center, M. Heratsi Yerevan State Medical University, Yerevan AM 0025, Armenia.,COBRAIN Scientific Educational Center for Fundamental Brain Research, Yerevan AM 0025, Armenia
| | | | | | | | | | | | - Allan V Kalueff
- Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg 199034, Russia.,Almazov National Medical Research Centre, St. Petersburg 197341, Russia.,Ural Federal University, Ekaterinburg 620075, Russia.,Granov Russian Research Center of Radiology and Surgical Technologies, Ministry of Healthcare of Russian Federation, Pesochny 197758, Russia.,Moscow Institute of Physics and Technology, Moscow 141701, Russia.,COBRAIN Scientific Educational Center for Fundamental Brain Research, Yerevan AM 0025, Armenia.,Scientific Research Institute of Neuroscience and Medicine, Novosibirsk, 630117, Russia
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3
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Humbeck L, Pretzel J, Spitzer S, Koch O. Discovery of an Unexpected Similarity in Ligand Binding between BRD4 and PPARγ. ACS Chem Biol 2021; 16:1255-1265. [PMID: 34180651 DOI: 10.1021/acschembio.1c00323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Knowledge about interrelationships between different proteins is crucial in fundamental research for the elucidation of protein networks and pathways. Furthermore, it is especially critical in chemical biology to identify further key regulators of a disease and to take advantage of polypharmacology effects. Here, we present a new concept that combines a scaffold-based analysis of bioactivity data with a subsequent screening to identify novel inhibitors for a protein target of interest. The initial scaffold-based analysis revealed a flavone-like scaffold that can be found in ligands of different unrelated proteins indicating a similarity in ligand binding. This similarity was further investigated by testing compounds on bromodomain-containing protein 4 (BRD4) that were similar to known ligands of the other identified protein targets. Several new BRD4 inhibitors were identified and proven to be validated hits based on orthogonal assays and X-ray crystallography. The most important discovery was an unexpected relationship between BRD4 and peroxisome-proliferator activated receptor gamma (PPARγ). Both proteins share binding site similarities near a common hydrophobic subpocket which should allow the design of a polypharmacology-based ligand targeting both proteins. Such dual-BRD4-PPARγ modulators open up new therapeutic opportunities, because both are important drug targets for cancer therapy and many more important diseases. Thereon, a complex structure of sulfasalazine was obtained that involves two bromodomains and could be a potential starting point for the design of a bivalent BRD4 inhibitor.
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Affiliation(s)
- Lina Humbeck
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227 Dortmund, Germany
| | - Jette Pretzel
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227 Dortmund, Germany
| | - Saskia Spitzer
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227 Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227 Dortmund, Germany
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4
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Moreira-Filho JT, Silva AC, Dantas RF, Gomes BF, Souza Neto LR, Brandao-Neto J, Owens RJ, Furnham N, Neves BJ, Silva-Junior FP, Andrade CH. Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence. Front Immunol 2021; 12:642383. [PMID: 34135888 PMCID: PMC8203334 DOI: 10.3389/fimmu.2021.642383] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 04/30/2021] [Indexed: 12/20/2022] Open
Abstract
Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.
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Affiliation(s)
- José T. Moreira-Filho
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
| | - Arthur C. Silva
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
| | - Rafael F. Dantas
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Barbara F. Gomes
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Lauro R. Souza Neto
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Jose Brandao-Neto
- Diamond Light Source Ltd., Didcot, United Kingdom
- Research Complex at Harwell, Didcot, United Kingdom
| | - Raymond J. Owens
- The Rosalind Franklin Institute, Harwell, United Kingdom
- Division of Structural Biology, The Wellcome Centre for Human Genetic, University of Oxford, Oxford, United Kingdom
| | - Nicholas Furnham
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Bruno J. Neves
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
| | - Floriano P. Silva-Junior
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Carolina H. Andrade
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
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5
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Mervin LH, Afzal AM, Engkvist O, Bender A. Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Protein–Ligand Predictions. J Chem Inf Model 2020; 60:4546-4559. [DOI: 10.1021/acs.jcim.0c00476] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Lewis H. Mervin
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Avid M. Afzal
- Data Sciences & Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Ola Engkvist
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Mölndal SE-431 83, Sweden
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge CB2 1TN, U.K
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6
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Ravikumar B, Alam Z, Peddinti G, Aittokallio T. C-SPADE: a web-tool for interactive analysis and visualization of drug screening experiments through compound-specific bioactivity dendrograms. Nucleic Acids Res 2019; 45:W495-W500. [PMID: 28472495 PMCID: PMC5570255 DOI: 10.1093/nar/gkx384] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 04/25/2017] [Indexed: 12/20/2022] Open
Abstract
The advent of polypharmacology paradigm in drug discovery calls for novel chemoinformatic tools for analyzing compounds’ multi-targeting activities. Such tools should provide an intuitive representation of the chemical space through capturing and visualizing underlying patterns of compound similarities linked to their polypharmacological effects. Most of the existing compound-centric chemoinformatics tools lack interactive options and user interfaces that are critical for the real-time needs of chemical biologists carrying out compound screening experiments. Toward that end, we introduce C-SPADE, an open-source exploratory web-tool for interactive analysis and visualization of drug profiling assays (biochemical, cell-based or cell-free) using compound-centric similarity clustering. C-SPADE allows the users to visually map the chemical diversity of a screening panel, explore investigational compounds in terms of their similarity to the screening panel, perform polypharmacological analyses and guide drug-target interaction predictions. C-SPADE requires only the raw drug profiling data as input, and it automatically retrieves the structural information and constructs the compound clusters in real-time, thereby reducing the time required for manual analysis in drug development or repurposing applications. The web-tool provides a customizable visual workspace that can either be downloaded as figure or Newick tree file or shared as a hyperlink with other users. C-SPADE is freely available at http://cspade.fimm.fi/.
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Affiliation(s)
- Balaguru Ravikumar
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - Zaid Alam
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - Gopal Peddinti
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland.,Department of Mathematics and Statistics, University of Turku, Turku, Finland
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7
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Lenci E, Trabocchi A. Smart Design of Small‐Molecule Libraries: When Organic Synthesis Meets Cheminformatics. Chembiochem 2019; 20:1115-1123. [DOI: 10.1002/cbic.201800751] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Indexed: 12/25/2022]
Affiliation(s)
- Elena Lenci
- Department of Chemistry “Ugo Schiff”University of Florence Via della Lastruccia 13 50019 Sesto Fiorentino Florence Italy
| | - Andrea Trabocchi
- Department of Chemistry “Ugo Schiff”University of Florence Via della Lastruccia 13 50019 Sesto Fiorentino Florence Italy
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8
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Abstract
Drugs modulate disease states through their actions on targets in the body. Determining these targets aids the focused development of new treatments, and helps to better characterize those already employed. One means of accomplishing this is through the deployment of in silico methodologies, harnessing computational analytical and predictive power to produce educated hypotheses for experimental verification. Here, we provide an overview of the current state of the art, describe some of the well-established methods in detail, and reflect on how they, and emerging technologies promoting the incorporation of complex and heterogeneous data-sets, can be employed to improve our understanding of (poly)pharmacology.
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Affiliation(s)
- Ryan Byrne
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland.
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9
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Basith S, Cui M, Macalino SJY, Park J, Clavio NAB, Kang S, Choi S. Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design. Front Pharmacol 2018; 9:128. [PMID: 29593527 PMCID: PMC5854945 DOI: 10.3389/fphar.2018.00128] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 02/06/2018] [Indexed: 01/14/2023] Open
Abstract
The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.
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Affiliation(s)
| | | | | | | | | | - Soosung Kang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
| | - Sun Choi
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
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10
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Gladysz R, Dos Santos FM, Langenaeker W, Thijs G, Augustyns K, De Winter H. Spectrophores as one-dimensional descriptors calculated from three-dimensional atomic properties: applications ranging from scaffold hopping to multi-target virtual screening. J Cheminform 2018. [PMID: 29516311 PMCID: PMC5842169 DOI: 10.1186/s13321-018-0268-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Spectrophores are novel descriptors that are calculated from the three-dimensional atomic properties of molecules. In our current implementation, the atomic properties that were used to calculate spectrophores include atomic partial charges, atomic lipophilicity indices, atomic shape deviations and atomic softness properties. This approach can easily be widened to also include additional atomic properties. Our novel methodology finds its roots in the experimental affinity fingerprinting technology developed in the 1990’s by Terrapin Technologies. Here we have translated it into a purely virtual approach using artificial affinity cages and a simplified metric to calculate the interaction between these cages and the atomic properties. A typical spectrophore consists of a vector of 48 real numbers. This makes it highly suitable for the calculation of a wide range of similarity measures for use in virtual screening and for the investigation of quantitative structure–activity relationships in combination with advanced statistical approaches such as self-organizing maps, support vector machines and neural networks. In our present report we demonstrate the applicability of our novel methodology for scaffold hopping as well as virtual screening.
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Affiliation(s)
- Rafaela Gladysz
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Biomedical and Veterinary Sciences, Campus Drie Eiken, Building A, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Fabio Mendes Dos Santos
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Biomedical and Veterinary Sciences, Campus Drie Eiken, Building A, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Wilfried Langenaeker
- Department of Chemistry, Faculty of Science, Campus Diepenbeek, Agoralaan, Building D, 3590, Diepenbeek, Belgium
| | - Gert Thijs
- Agilent, Clinical Applications Division, Technologielaan 3, 3001, Louvain, Belgium
| | - Koen Augustyns
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Biomedical and Veterinary Sciences, Campus Drie Eiken, Building A, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Hans De Winter
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Biomedical and Veterinary Sciences, Campus Drie Eiken, Building A, Universiteitsplein 1, 2610, Antwerp, Belgium.
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11
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Humbeck L, Weigang S, Schäfer T, Mutzel P, Koch O. CHIPMUNK: A Virtual Synthesizable Small-Molecule Library for Medicinal Chemistry, Exploitable for Protein-Protein Interaction Modulators. ChemMedChem 2018; 13:532-539. [PMID: 29392860 DOI: 10.1002/cmdc.201700689] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 01/27/2018] [Indexed: 02/05/2023]
Abstract
A common issue during drug design and development is the discovery of novel scaffolds for protein targets. On the one hand the chemical space of purchasable compounds is rather limited; on the other hand artificially generated molecules suffer from a grave lack of accessibility in practice. Therefore, we generated a novel virtual library of small molecules which are synthesizable from purchasable educts, called CHIPMUNK (CHemically feasible In silico Public Molecular UNiverse Knowledge base). Altogether, CHIPMUNK covers over 95 million compounds and encompasses regions of the chemical space that are not covered by existing databases. The coverage of CHIPMUNK exceeds the chemical space spanned by the Lipinski rule of five to foster the exploration of novel and difficult target classes. The analysis of the generated property space reveals that CHIPMUNK is well suited for the design of protein-protein interaction inhibitors (PPIIs). Furthermore, a recently developed structural clustering algorithm (StruClus) for big data was used to partition the sub-libraries into meaningful subsets and assist scientists to process the large amount of data. These clustered subsets also contain the target space based on ChEMBL data which was included during clustering.
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Affiliation(s)
- Lina Humbeck
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, Dortmund, 44227, Germany
| | - Sebastian Weigang
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, Dortmund, 44227, Germany
| | - Till Schäfer
- Department of Computer Science, TU Dortmund University, Otto-Hahn-Straße 14, Dortmund, 44227, Germany
| | - Petra Mutzel
- Department of Computer Science, TU Dortmund University, Otto-Hahn-Straße 14, Dortmund, 44227, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, Dortmund, 44227, Germany
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12
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Jacobson KA, Tosh DK, Toti KS, Ciancetta A. Polypharmacology of conformationally locked methanocarba nucleosides. Drug Discov Today 2017; 22:1782-1791. [PMID: 28781163 PMCID: PMC5705437 DOI: 10.1016/j.drudis.2017.07.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Revised: 06/13/2017] [Accepted: 07/26/2017] [Indexed: 12/13/2022]
Abstract
A single molecular scaffold can be adapted to interact with diverse targets, either separately or simultaneously. Nucleosides and nucleotides in which ribose is substituted with bicyclo[3.1.0]hexane are an example of a versatile drug-like scaffold for increasing selectivity at their classical targets: kinases, polymerases, adenosine and P2 receptors. Also, by applying structure-based functional group manipulations, rigidified adenosine derivatives can be repurposed to satisfy pharmacophoric requirements of various GPCRs, ion channels, enzymes and transporters, initially detected as off-target activities. Recent examples include 5HT2B serotonin receptor antagonists and novel dopamine transporter allosteric modulators. This directable target diversity establishes rigid nucleosides as privileged scaffolds.
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Affiliation(s)
- Kenneth A Jacobson
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes & Digestive & Kidney Diseases, National Institutes of Health, Bldg 8A, Rm B1A-19, Bethesda, MD 20892-0810, USA.
| | - Dilip K Tosh
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes & Digestive & Kidney Diseases, National Institutes of Health, Bldg 8A, Rm B1A-19, Bethesda, MD 20892-0810, USA
| | - Kiran S Toti
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes & Digestive & Kidney Diseases, National Institutes of Health, Bldg 8A, Rm B1A-19, Bethesda, MD 20892-0810, USA
| | - Antonella Ciancetta
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes & Digestive & Kidney Diseases, National Institutes of Health, Bldg 8A, Rm B1A-19, Bethesda, MD 20892-0810, USA
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Varsou DD, Melagraki G, Sarimveis H, Afantitis A. MouseTox: An online toxicity assessment tool for small molecules through Enalos Cloud platform. Food Chem Toxicol 2017; 110:83-93. [PMID: 28988138 DOI: 10.1016/j.fct.2017.09.058] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Revised: 09/29/2017] [Accepted: 09/30/2017] [Indexed: 11/26/2022]
Abstract
Advances in the drug discovery research substantially depend on in silico methods and techniques that capitalize on experimental data to enable the accurate property/activity assessment by employing a variety of computational techniques. These in silico tools can significantly reduce expensive and time consuming experimental procedures required and are strongly recommended to avoid animal testing, especially as far as toxicity evaluation and risk assessment is concerned. In this context, in the present work we aim to develop a predictive model for the cytotoxic effects of a wide range of compounds based solely on calculated molecular descriptors that account for their topological, geometric and structural characteristics. The developed model was fully validated and was released online via Enalos Cloud platform accessible through http://enalos.insilicotox.com/MouseTox/. This ready-to-use web service offers, through a user-friendly interface, free access to the model results and therefore can act as a toxicity prediction tool for the risk assessment of novel compounds, without any special requirements or prior programming skills.
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Affiliation(s)
- Dimitra-Danai Varsou
- NovaMechanics Ltd, Nicosia, Cyprus; School of Chemical Engineering, National Technical University of Athens, Athens, Greece
| | - Georgia Melagraki
- Department of Military Sciences, Division of Physical Sciences and Applications, Hellenic Army Academy, Vari, Greece.
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, Athens, Greece
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Abstract
With the advent of very rapid and cheap genome analyses and the linkage of these plus microbial metabolomics to potential compound structures came the realization that there was an immense sea of novel agents to be mined and tested. In addition, it is now recognized that there is significant microbial involvement in many natural products isolated from “nominally non-microbial sources”. This short review covers the current screening methods that have evolved and one might even be tempted to say “devolved” in light of the realization that target-based screens had problems when the products entered clinical testing, with off-target effects being the major ones. Modern systems include, but are not limited to, screening in cell lines utilizing very modern techniques (a high content screen) that are designed to show interactions within cells when treated with an “agent”. The underlying principle(s) used in such systems dated back to unpublished attempts in the very early 1980s by the pharmaceutical industry to show toxic interactions within animal cells by using automated light microscopy. Though somewhat successful, the technology was not adequate for any significant commercialization. Somewhat later, mammalian cell lines that were “genetically modified” to alter signal transduction cascades, either up or down, and frequently linked to luciferase readouts, were then employed in a 96-well format. In the case of microbes, specific resistance parameters were induced in isogenic cell lines from approximately the mid-1970s. In the latter two cases, comparisons against parent and sibling cell lines were used in order that a rapid determination of potential natural product “hits” could be made. Obviously, all of these assay systems could also be, and were, used for synthetic molecules. These methods and their results have led to a change in what the term “screening for bioactivity” means. In practice, versions of phenotypic screening are returning, but in a dramatically different scientific environment from the 1970s, as I hope to demonstrate in the short article that follows.
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Schäfer T, Kriege N, Humbeck L, Klein K, Koch O, Mutzel P. Scaffold Hunter: a comprehensive visual analytics framework for drug discovery. J Cheminform 2017; 9:28. [PMID: 29086162 PMCID: PMC5425364 DOI: 10.1186/s13321-017-0213-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Accepted: 04/10/2017] [Indexed: 01/31/2023] Open
Abstract
The era of big data is influencing the way how rational drug discovery and the development of bioactive molecules is performed and versatile tools are needed to assist in molecular design workflows. Scaffold Hunter is a flexible visual analytics framework for the analysis of chemical compound data and combines techniques from several fields such as data mining and information visualization. The framework allows analyzing high-dimensional chemical compound data in an interactive fashion, combining intuitive visualizations with automated analysis methods including versatile clustering methods. Originally designed to analyze the scaffold tree, Scaffold Hunter is continuously revised and extended. We describe recent extensions that significantly increase the applicability for a variety of tasks.
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Affiliation(s)
- Till Schäfer
- Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 14, Dortmund, 44227, Germany
| | - Nils Kriege
- Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 14, Dortmund, 44227, Germany
| | - Lina Humbeck
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Str. 6, Dortmund, 44227, Germany
| | - Karsten Klein
- Department of Computer and Information Science, University of Konstanz, Universitaetsstrasse 10, Konstanz, 78464, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Str. 6, Dortmund, 44227, Germany.
| | - Petra Mutzel
- Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 14, Dortmund, 44227, Germany.
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