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Bolz SN, Schroeder M. Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances. Expert Opin Drug Discov 2023; 18:973-985. [PMID: 37489516 DOI: 10.1080/17460441.2023.2239700] [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: 06/09/2023] [Accepted: 07/19/2023] [Indexed: 07/26/2023]
Abstract
INTRODUCTION Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks. AREAS COVERED This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets. EXPERT OPINION Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.
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Affiliation(s)
- Sarah Naomi Bolz
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Dresden, Germany
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Dresden, Germany
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2
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Rodríguez-Pérez R, Miljković F, Bajorath J. Machine Learning in Chemoinformatics and Medicinal Chemistry. Annu Rev Biomed Data Sci 2022; 5:43-65. [PMID: 35440144 DOI: 10.1146/annurev-biodatasci-122120-124216] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In chemoinformatics and medicinal chemistry, machine learning has evolved into an important approach. In recent years, increasing computational resources and new deep learning algorithms have put machine learning onto a new level, addressing previously unmet challenges in pharmaceutical research. In silico approaches for compound activity predictions, de novo design, and reaction modeling have been further advanced by new algorithmic developments and the emergence of big data in the field. Herein, novel applications of machine learning and deep learning in chemoinformatics and medicinal chemistry are reviewed. Opportunities and challenges for new methods and applications are discussed, placing emphasis on proper baseline comparisons, robust validation methodologies, and new applicability domains. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Raquel Rodríguez-Pérez
- Department of Life Science Informatics, B-IT (Bonn-Aachen International Center for Information Technology), Chemical Biology and Medicinal Chemistry Program Unit, LIMES (Life and Medical Sciences Institute), Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany; .,Current affiliation: Novartis Institutes for Biomedical Research, Novartis Campus, Basel, Switzerland
| | - Filip Miljković
- Department of Life Science Informatics, B-IT (Bonn-Aachen International Center for Information Technology), Chemical Biology and Medicinal Chemistry Program Unit, LIMES (Life and Medical Sciences Institute), Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany; .,Current affiliation: Data Science and AI, Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D AstraZeneca, Gothenburg, Sweden
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT (Bonn-Aachen International Center for Information Technology), Chemical Biology and Medicinal Chemistry Program Unit, LIMES (Life and Medical Sciences Institute), Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany;
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3
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Differentiating Inhibitors of Closely Related Protein Kinases with Single- or Multi-Target Activity via Explainable Machine Learning and Feature Analysis. Biomolecules 2022; 12:biom12040557. [PMID: 35454147 PMCID: PMC9032434 DOI: 10.3390/biom12040557] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/01/2022] [Accepted: 04/06/2022] [Indexed: 01/01/2023] Open
Abstract
Protein kinases are major drug targets. Most kinase inhibitors are directed against the adenosine triphosphate (ATP) cofactor binding site, which is largely conserved across the human kinome. Hence, such kinase inhibitors are often thought to be promiscuous. However, experimental evidence and activity data for publicly available kinase inhibitors indicate that this is not generally the case. We have investigated whether inhibitors of closely related human kinases with single- or multi-kinase activity can be differentiated on the basis of chemical structure. Therefore, a test system consisting of two distinct kinase triplets has been devised for which inhibitors with reported triple-kinase activities and corresponding single-kinase activities were assembled. Machine learning models derived on the basis of chemical structure distinguished between these multi- and single-kinase inhibitors with high accuracy. A model-independent explanatory approach was applied to identify structural features determining accurate predictions. For both kinase triplets, the analysis revealed decisive features contained in multi-kinase inhibitors. These features were found to be absent in corresponding single-kinase inhibitors, thus providing a rationale for successful machine learning. Mapping of features determining accurate predictions revealed that they formed coherent and chemically meaningful substructures that were characteristic of multi-kinase inhibitors compared with single-kinase inhibitors.
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4
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Monroy-Jaramillo N, Martínez-Magaña JJ, Pérez-Aldana BE, Ortega-Vázquez A, Montalvo-Ortiz J, López-López M. The role of alcohol intake in the pharmacogenetics of treatment with clozapine. Pharmacogenomics 2022; 23:371-392. [PMID: 35311547 DOI: 10.2217/pgs-2022-0006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Clozapine (CLZ) is an atypical antipsychotic reserved for patients with refractory psychosis, but it is associated with a significant risk of severe adverse reactions (ADRs) that are potentiated with the concomitant use of alcohol. Additionally, pharmacogenetic studies have explored the influence of several genetic variants in CYP450, receptors and transporters involved in the interindividual response to CLZ. Herein, we systematically review the current multiomics knowledge behind the interaction between CLZ and alcohol intake, and how its concomitant use might modulate the pharmacogenetics. CYP1A2*1F, *1C and other alleles not yet discovered could support a precision medicine approach for better therapeutic effects and fewer CLZ ADRs. CLZ monitoring systems should be amended and include alcohol intake to protect patients from severe CLZ ADRs.
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Affiliation(s)
- Nancy Monroy-Jaramillo
- Department of Genetics, National Institute of Neurology & Neurosurgery, Manuel Velasco Suárez, La Fama, Tlalpan, Mexico City, 14269, Mexico
| | - José Jaime Martínez-Magaña
- Department of Psychiatry, Division of Human Genetics, Yale University School of Medicine, Orange, West Haven, CT 06477, USA
| | - Blanca Estela Pérez-Aldana
- Doctorado en Ciencias Biológicas y de la Salud, Metropolitan Autonomous University, Campus Xochimilco, Villa Quietud, Coyoacán, Mexico City, 04960, Mexico
| | - Alberto Ortega-Vázquez
- Metropolitan Autonomous University, Campus Xochimilco, Villa Quietud, Coyoacán, Mexico City, 04960, Mexico
| | - Janitza Montalvo-Ortiz
- Department of Psychiatry, Division of Human Genetics, Yale University School of Medicine, Orange, West Haven, CT 06477, USA
| | - Marisol López-López
- Metropolitan Autonomous University, Campus Xochimilco, Villa Quietud, Coyoacán, Mexico City, 04960, Mexico
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5
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Ye Z, Chen F, Zeng J, Gao J, Zhang MQ. ScaffComb: A Phenotype-Based Framework for Drug Combination Virtual Screening in Large-Scale Chemical Datasets. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:e2102092. [PMID: 34723439 PMCID: PMC8693048 DOI: 10.1002/advs.202102092] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 07/29/2021] [Indexed: 06/13/2023]
Abstract
Combinational therapy is used for a long time in cancer treatment to overcome drug resistance related to monotherapy. Increased pharmacological data and the rapid development of deep learning methods have enabled the construction of models to predict and screen drug pairs. However, the size of drug libraries is restricted to hundreds to thousands of compounds. The ScaffComb framework, which aims to bridge the gaps in the virtual screening of drug combinations in large-scale databases, is proposed here. Inspired by phenotype-based drug design, ScaffComb integrates phenotypic information into molecular scaffolds, which can be used to screen the drug library and identify potent drug combinations. First, ScaffComb is validated using the US food and drug administration dataset and known drug combinations are successfully reidentified. Then, ScaffComb is applied to screen the ZINC and ChEMBL databases, which yield novel drug combinations and reveal an ability to discover new synergistic mechanisms. To our knowledge, ScaffComb is the first method to use phenotype-based virtual screening of drug combinations in large-scale chemical datasets.
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Affiliation(s)
- Zhaofeng Ye
- MOE Key Laboratory of BioinformaticsBioinformatics DivisionCenter for Synthetic and Systems BiologyBNRistDepartment of AutomationTsinghua UniversityBeijing100084China
- School of MedicineTsinghua UniversityBeijing100084China
| | - Fengling Chen
- Center for Stem Cell Biology and Regenerative MedicineMOE Key Laboratory of BioinformaticsTsinghua UniversityBeijing100084China
- Tsinghua‐Peking Center for Life SciencesBeijing100084China
| | - Jiangyang Zeng
- MOE Key Laboratory of BioinformaticsBioinformatics DivisionCenter for Synthetic and Systems BiologyBNRistDepartment of AutomationTsinghua UniversityBeijing100084China
- Institute for Interdisciplinary Information SciencesTsinghua UniversityBeijing100084China
| | - Juntao Gao
- MOE Key Laboratory of BioinformaticsBioinformatics DivisionCenter for Synthetic and Systems BiologyBNRistDepartment of AutomationTsinghua UniversityBeijing100084China
| | - Michael Q. Zhang
- MOE Key Laboratory of BioinformaticsBioinformatics DivisionCenter for Synthetic and Systems BiologyBNRistDepartment of AutomationTsinghua UniversityBeijing100084China
- School of MedicineTsinghua UniversityBeijing100084China
- Department of Biological SciencesCenter for Systems BiologyThe University of Texas at DallasRichardsonTX75080‐3021USA
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Kell DB. The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes. Molecules 2021; 26:5629. [PMID: 34577099 PMCID: PMC8470029 DOI: 10.3390/molecules26185629] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/03/2021] [Accepted: 09/14/2021] [Indexed: 12/12/2022] Open
Abstract
Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".
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Affiliation(s)
- Douglas B. Kell
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool L69 7ZB, UK;
- Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs Lyngby, Denmark
- Mellizyme Biotechnology Ltd., IC1, Liverpool Science Park, Mount Pleasant, Liverpool L3 5TF, UK
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García Del Valle EP, Lagunes García G, Prieto Santamaría L, Zanin M, Menasalvas Ruiz E, Rodríguez-González A. Leveraging network analysis to evaluate biomedical named entity recognition tools. Sci Rep 2021; 11:13537. [PMID: 34188248 PMCID: PMC8242017 DOI: 10.1038/s41598-021-93018-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 06/18/2021] [Indexed: 02/06/2023] Open
Abstract
The ever-growing availability of biomedical text sources has resulted in a boost in clinical studies based on their exploitation. Biomedical named-entity recognition (bio-NER) techniques have evolved remarkably in recent years and their application in research is increasingly successful. Still, the disparity of tools and the limited available validation resources are barriers preventing a wider diffusion, especially within clinical practice. We here propose the use of omics data and network analysis as an alternative for the assessment of bio-NER tools. Specifically, our method introduces quality criteria based on edge overlap and community detection. The application of these criteria to four bio-NER solutions yielded comparable results to strategies based on annotated corpora, without suffering from their limitations. Our approach can constitute a guide both for the selection of the best bio-NER tool given a specific task, and for the creation and validation of novel approaches.
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Affiliation(s)
| | - Gerardo Lagunes García
- ETS de Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, Spain
- Centro de Tecnología Biomédica, ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, Spain
| | - Lucía Prieto Santamaría
- Centro de Tecnología Biomédica, ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, Spain
| | - Massimiliano Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, Palma de Mallorca, Spain
| | - Ernestina Menasalvas Ruiz
- ETS de Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, Spain
- Centro de Tecnología Biomédica, ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, Spain
| | - Alejandro Rodríguez-González
- ETS de Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, Spain
- Centro de Tecnología Biomédica, ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, Spain
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8
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Blaschke T, Bajorath J. Fine-tuning of a generative neural network for designing multi-target compounds. J Comput Aided Mol Des 2021; 36:363-371. [PMID: 34046745 PMCID: PMC9325839 DOI: 10.1007/s10822-021-00392-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 05/23/2021] [Indexed: 12/20/2022]
Abstract
Exploring the origin of multi-target activity of small molecules and designing new multi-target compounds are highly topical issues in pharmaceutical research. We have investigated the ability of a generative neural network to create multi-target compounds. Data sets of experimentally confirmed multi-target, single-target, and consistently inactive compounds were extracted from public screening data considering positive and negative assay results. These data sets were used to fine-tune the REINVENT generative model via transfer learning to systematically recognize multi-target compounds, distinguish them from single-target or inactive compounds, and construct new multi-target compounds. During fine-tuning, the model showed a clear tendency to increasingly generate multi-target compounds and structural analogs. Our findings indicate that generative models can be adopted for de novo multi-target compound design.
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Affiliation(s)
- Thomas Blaschke
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, 53115, Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, 53115, Bonn, Germany.
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9
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Blaschke T, Bajorath J. Compound dataset and custom code for deep generative multi-target compound design. Future Sci OA 2021; 7:FSO715. [PMID: 34046209 PMCID: PMC8147756 DOI: 10.2144/fsoa-2021-0033] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 04/16/2021] [Indexed: 12/12/2022] Open
Abstract
AIM Generating a data and software infrastructure for evaluating multi-target compound (MT-CPD) design via deep generative modeling. METHODOLOGY The REINVENT 2.0 approach for generative modeling was extended for MT-CPD design and a large benchmark data set was curated. EXEMPLARY RESULTS & DATA Proof-of-concept for deep generative MT-CPD design was established. Custom code and the benchmark set comprising 2809 MT-CPDs, 61,928 single-target and 295,395 inactive compounds from biological screens are made freely available. LIMITATIONS & NEXT STEPS MT-CPD design via deep learning is still at its conceptual stages. It will be required to demonstrate experimental impact. The data and software we provide enable further investigation of MT-CPD design and generation of candidate molecules for experimental programs.
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Affiliation(s)
- Thomas Blaschke
- Department of Life Science Informatics & Data Science, B-IT, LIMES Program Unit Chemical Biology & Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, Bonn, D-53113, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics & Data Science, B-IT, LIMES Program Unit Chemical Biology & Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, Bonn, D-53113, Germany
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10
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Stumpfe D, Hoch A, Bajorath J. Introducing the metacore concept for multi-target ligand design. RSC Med Chem 2021; 12:628-635. [PMID: 34046634 PMCID: PMC8128067 DOI: 10.1039/d1md00056j] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Accepted: 04/04/2021] [Indexed: 01/25/2023] Open
Abstract
In this work, we introduce the concept of "metacores" (MCs) for the organization of analog series (ASs) and multi-target (MT) ligand design. Generating compounds that are active against distantly related or unrelated targets is a central task in polypharmacology-oriented drug discovery. MCs are obtained by two-stage extraction of structural cores from ASs. The methodology is chemically intuitive and generally applicable. Each MC represents a set of related ASs and a template for the generation of new structures. We have systematically identified ASs that exclusively consisted of analogs with MT activity and determined their target profiles. From these ASs, a large set of 317 structurally diverse MCs was extracted, 127 of which were associated with different target families. The newly generated MCs were characterized and further prioritized on the basis of AS, compound, and target coverage. The analysis indicated that 260 MCs were pharmaceutically relevant. These MCs and the compound and target information they capture are made freely available for medicinal chemistry applications.
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Affiliation(s)
- Dagmar Stumpfe
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität Friedrich-Hirzebruch-Allee 6 D-53115 Bonn Germany +49 228 73 69101 +49 228 73 69100
| | - Alexander Hoch
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität Friedrich-Hirzebruch-Allee 6 D-53115 Bonn Germany +49 228 73 69101 +49 228 73 69100
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität Friedrich-Hirzebruch-Allee 6 D-53115 Bonn Germany +49 228 73 69101 +49 228 73 69100
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Bajorath J. State-of-the-art of artificial intelligence in medicinal chemistry. Future Sci OA 2021; 7:FSO702. [PMID: 34046204 PMCID: PMC8147736 DOI: 10.2144/fsoa-2021-0030] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 03/10/2021] [Indexed: 12/22/2022] Open
Affiliation(s)
- Jürgen Bajorath
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology & Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, Bonn D, 53115, Germany
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12
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Blaschke T, Feldmann C, Bajorath J. Prediction of Promiscuity Cliffs Using Machine Learning. Mol Inform 2021; 40:e2000196. [PMID: 32881355 PMCID: PMC7816223 DOI: 10.1002/minf.202000196] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 09/03/2020] [Indexed: 12/22/2022]
Abstract
Compounds with the ability to interact with multiple targets, also called promiscuous compounds, provide the basis for polypharmacological drug discovery. In recent years, a plethora of structural analogs with different promiscuity has been identified. Nevertheless, the molecular origins of promiscuity remain to be elucidated. In this study, we systematically extracted different structural analogs with varying promiscuity using the matched molecular pair (MMP) formalism from public biological screening and medicinal chemistry data. Care was taken to eliminate all compounds with potential false-positive activity annotations from the analysis. Promiscuity predictions were then attempted at the level of compound pairs representing promiscuity cliffs (PCs; formed by analogs with large promiscuity differences) and corresponding non-PC MMPs (analog pairs without significant promiscuity differences). To address this prediction task, different machine learning models were generated and the results were compared with single compound predictions. PCs encoding promiscuity differences were found to contain more structure-promiscuity relationship information than sets of individual promiscuous compounds. In addition, feature analysis was carried out revealing key contributions to the correct prediction of PCs and non-PC MMPs via machine learning.
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Affiliation(s)
- Thomas Blaschke
- Department of Life Science InformaticsB-ITLIMES Program Unit Chemical Biology and Medicinal ChemistryRheinische Friedrich-Wilhelms-UniversitätEndenicher Allee 19cD-53115BonnGermany
| | - Christian Feldmann
- Department of Life Science InformaticsB-ITLIMES Program Unit Chemical Biology and Medicinal ChemistryRheinische Friedrich-Wilhelms-UniversitätEndenicher Allee 19cD-53115BonnGermany
| | - Jürgen Bajorath
- Department of Life Science InformaticsB-ITLIMES Program Unit Chemical Biology and Medicinal ChemistryRheinische Friedrich-Wilhelms-UniversitätEndenicher Allee 19cD-53115BonnGermany
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13
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Feldmann C, Yonchev D, Bajorath J. Analysis of Biological Screening Compounds with Single- or Multi-Target Activity via Diagnostic Machine Learning. Biomolecules 2020; 10:biom10121605. [PMID: 33260876 PMCID: PMC7761051 DOI: 10.3390/biom10121605] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 11/20/2020] [Accepted: 11/26/2020] [Indexed: 01/06/2023] Open
Abstract
Predicting compounds with single- and multi-target activity and exploring origins of compound specificity and promiscuity is of high interest for chemical biology and drug discovery. We present a large-scale analysis of compound promiscuity including two major components. First, high-confidence datasets of compounds with multi- and corresponding single-target activity were extracted from biological screening data. Positive and negative assay results were taken into account and data completeness was ensured. Second, these datasets were investigated using diagnostic machine learning to systematically distinguish between compounds with multi- and single-target activity. Models built on the basis of chemical structure consistently produced meaningful predictions. These findings provided evidence for the presence of structural features differentiating promiscuous and non-promiscuous compounds. Machine learning under varying conditions using modified datasets revealed a strong influence of nearest neighbor relationship on the predictions. Many multi-target compounds were found to be more similar to other multi-target compounds than single-target compounds and vice versa, which resulted in consistently accurate predictions. The results of our study confirm the presence of structural relationships that differentiate promiscuous and non-promiscuous compounds.
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Feldmann C, Yonchev D, Stumpfe D, Bajorath J. Systematic Data Analysis and Diagnostic Machine Learning Reveal Differences between Compounds with Single- and Multitarget Activity. Mol Pharm 2020; 17:4652-4666. [PMID: 33151084 DOI: 10.1021/acs.molpharmaceut.0c00901] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Small molecules with multitarget activity are capable of triggering polypharmacological effects and are of high interest in drug discovery. Compared to single-target compounds, promiscuity also affects drug distribution and pharmacodynamics and alters ADMET characteristics. Features distinguishing between compounds with single- and multitarget activity are currently only little understood. On the basis of systematic data analysis, we have assembled large sets of promiscuous compounds with activity against related or functionally distinct targets and the corresponding compounds with single-target activity. Machine learning predicted promiscuous compounds with surprisingly high accuracy. Molecular similarity analysis combined with control calculations under varying conditions revealed that accurate predictions were largely determined by structural nearest-neighbor relationships between compounds from different classes. We also found that large proportions of promiscuous compounds with activity against related or unrelated targets and corresponding single-target compounds formed analog series with distinct chemical space coverage, which further rationalized the predictions. Moreover, compounds with activity against proteins from functionally distinct classes were often active against unique targets that were not covered by other promiscuous compounds. The results of our analysis revealed that nearest-neighbor effects determined the prediction of promiscuous compounds and that preferential partitioning of compounds with single- and multitarget activity into structurally distinct analog series was responsible for such effects, hence providing a rationale for the presence of different structure-promiscuity relationships.
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Affiliation(s)
- Christian Feldmann
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53115 Bonn, Germany
| | - Dimitar Yonchev
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53115 Bonn, Germany
| | - Dagmar Stumpfe
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53115 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53115 Bonn, Germany
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Laufkötter O, Laufer S, Bajorath J. Kinase inhibitor data set for systematic analysis of representative kinases across the human kinome. Data Brief 2020; 32:106189. [PMID: 32904416 PMCID: PMC7452594 DOI: 10.1016/j.dib.2020.106189] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Accepted: 08/12/2020] [Indexed: 11/06/2022] Open
Abstract
A large set of multi-kinase inhibitors with high-confidence activity data was assembled and used to generate network representations revealing kinase relationships based upon shared inhibitors [1]. Compounds and activity annotations were originally selected from public repositories and organized in an in-house database from which the data set was extracted and curated. The new data set comprises more than 36,000 inhibitors with multiple activity annotations for a total of 420 human kinases (providing 81% coverage of the human kinome), representing a total of ∼127,000 kinase-inhibitor interactions. Use of the data is not limited to the network application reported in [1]. It can also be used, for example, for different types of compound promiscuity analysis or machine learning (such a multi-task modeling). In addition, the data set provides a large resource for complementing kinase drug discovery projects with external compound information.
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Affiliation(s)
- Oliver Laufkötter
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, Bonn D-53115, Germany
| | - Stefan Laufer
- Department of Pharmacy and Biochemistry, Pharmaceutical/Medicinal Chemistry, TüCADD (Tübingen Center for Academic Drug Discovery), Eberhard Karls Universität Tübingen, Auf der Morgenstelle 8, Tübingen D-72076, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, Bonn D-53115, Germany
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16
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Bernetti M, Bertazzo M, Masetti M. Data-Driven Molecular Dynamics: A Multifaceted Challenge. Pharmaceuticals (Basel) 2020; 13:E253. [PMID: 32961909 PMCID: PMC7557855 DOI: 10.3390/ph13090253] [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: 08/25/2020] [Revised: 09/14/2020] [Accepted: 09/16/2020] [Indexed: 12/18/2022] Open
Abstract
The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data.
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Affiliation(s)
- Mattia Bernetti
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), via Bonomea 265, I-34136 Trieste, Italy;
| | - Martina Bertazzo
- Computational Sciences, Istituto Italiano di Tecnologia, via Morego 30, I-16163 Genova, Italy;
| | - Matteo Masetti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—Università di Bologna, via Belmeloro 6, I-40126 Bologna, Italy
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17
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Compounds with multitarget activity: structure-based analysis and machine learning. FUTURE DRUG DISCOVERY 2020. [DOI: 10.4155/fdd-2020-0014] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
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X-ray Structure-Based Chemoinformatic Analysis Identifies Promiscuous Ligands Binding to Proteins from Different Classes with Varying Shapes. Int J Mol Sci 2020; 21:ijms21113782. [PMID: 32471121 PMCID: PMC7312685 DOI: 10.3390/ijms21113782] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 05/18/2020] [Accepted: 05/24/2020] [Indexed: 12/11/2022] Open
Abstract
(1) Background: Compounds with multitarget activity are of interest in basic research to explore molecular foundations of promiscuous binding and in drug discovery as agents eliciting polypharmacological effects. Our study has aimed to systematically identify compounds that form complexes with proteins from distinct classes and compare their bioactive conformations and molecular properties. (2) Methods: A large-scale computational investigation was carried out that combined the analysis of complex X-ray structures, ligand binding modes, compound activity data, and various molecular properties. (3) Results: A total of 515 ligands with multitarget activity were identified that included 70 organic compounds binding to proteins from different classes. These multiclass ligands (MCLs) were often flexible and surprisingly hydrophilic. Moreover, they displayed a wide spectrum of binding modes. In different target structure environments, binding shapes of MCLs were often similar, but also distinct. (4) Conclusions: Combined structural and activity data analysis identified compounds with activity against proteins with distinct structures and functions. MCLs were found to have greatly varying shape similarity when binding to different protein classes. Hence, there were no apparent canonical binding shapes indicating multitarget activity. Rather, conformational versatility characterized MCL binding.
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Rodríguez-Pérez R, Miljković F, Bajorath J. Assessing the information content of structural and protein-ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning. J Cheminform 2020; 12:36. [PMID: 33431025 PMCID: PMC7245824 DOI: 10.1186/s13321-020-00434-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 04/27/2020] [Indexed: 12/27/2022] Open
Abstract
For kinase inhibitors, X-ray crystallography has revealed different types of binding modes. Currently, more than 2000 kinase inhibitors with known binding modes are available, which makes it possible to derive and test machine learning models for the prediction of inhibitors with different binding modes. We have addressed this prediction task to evaluate and compare the information content of distinct molecular representations including protein–ligand interaction fingerprints (IFPs) and compound structure-based structural fingerprints (i.e., atom environment/fragment fingerprints). IFPs were designed to capture binding mode-specific interaction patterns at different resolution levels. Accurate predictions of kinase inhibitor binding modes were achieved with random forests using both representations. The performance of IFPs was consistently superior to atom environment fingerprints, albeit only by less than 10%. An active learning strategy applying information entropy-based selection of training instances was applied as a diagnostic approach to assess the relative information content of distinct representations. IFPs were found to capture more binding mode-relevant information than atom environment fingerprints, leading to highly predictive models even when training instances were randomly selected. By contrast, for atom environment fingerprints, the derivation of accurate models via active learning depended on entropy-based selection of informative training compounds. Notably, higher information content of IFPs confirmed by active learning only resulted in small improvements in global prediction accuracy compared to models derived using atom environment fingerprints. For practical applications, prediction of binding modes of new kinase inhibitors on the basis of chemical structure is highly attractive.![]()
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Affiliation(s)
- Raquel Rodríguez-Pérez
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, 53115, Bonn, Germany
| | - Filip Miljković
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, 53115, Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, 53115, Bonn, Germany.
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20
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Biological Activity Profiles of Multitarget Ligands from X-ray Structures. Molecules 2020; 25:molecules25040794. [PMID: 32059498 PMCID: PMC7070578 DOI: 10.3390/molecules25040794] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Revised: 02/10/2020] [Accepted: 02/11/2020] [Indexed: 11/17/2022] Open
Abstract
In pharmaceutical research, compounds with multitarget activity receive increasing attention. Such promiscuous chemical entities are prime candidates for polypharmacology, but also prone to causing undesired side effects. In addition, understanding the molecular basis and magnitude of multitarget activity is a stimulating topic for exploratory research. Computationally, compound promiscuity can be estimated through large-scale analysis of activity data. To these ends, it is critically important to take data confidence criteria and data consistency across different sources into consideration. Especially the consistency aspect has thus far only been little investigated. Therefore, we have systematically determined activity annotations and profiles of known multitarget ligands (MTLs) on the basis of activity data from different sources. All MTLs used were confirmed by X-ray crystallography of complexes with multiple targets. One of the key questions underlying our analysis has been how MTLs act in biological screens. The results of our analysis revealed significant variations of MTL activity profiles originating from different data sources. Such variations must be carefully considered in promiscuity analysis. Our study raises awareness of these issues and provides guidance for large-scale activity data analysis.
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21
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Exploring structure-promiscuity relationships using dual-site promiscuity cliffs and corresponding single-site analogs. Bioorg Med Chem 2020; 28:115238. [DOI: 10.1016/j.bmc.2019.115238] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 11/20/2019] [Accepted: 11/23/2019] [Indexed: 01/22/2023]
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22
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Data structures for computational compound promiscuity analysis and exemplary applications to inhibitors of the human kinome. J Comput Aided Mol Des 2019; 34:1-10. [DOI: 10.1007/s10822-019-00266-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Accepted: 11/26/2019] [Indexed: 02/05/2023]
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23
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Identifying Promiscuous Compounds with Activity against Different Target Classes. Molecules 2019; 24:molecules24224185. [PMID: 31752252 PMCID: PMC6891533 DOI: 10.3390/molecules24224185] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 11/12/2019] [Accepted: 11/15/2019] [Indexed: 11/21/2022] Open
Abstract
Compounds with multitarget activity are of high interest for polypharmacological drug discovery. Such promiscuous compounds might be active against closely related target proteins from the same family or against distantly related or unrelated targets. Compounds with activity against distinct targets are not only of interest for polypharmacology but also to better understand how small molecules might form specific interactions in different binding site environments. We have aimed to identify compounds with activity against drug targets from different classes. To these ends, a systematic analysis of public biological screening data was carried out. Care was taken to exclude compounds from further consideration that were prone to experimental artifacts and false positive activity readouts. Extensively assayed compounds were identified and found to contain molecules that were consistently inactive in all assays, active against a single target, or promiscuous. The latter included more than 1000 compounds that were active against 10 or more targets from different classes. These multiclass ligands were further analyzed and exemplary compounds were found in X-ray structures of complexes with distinct targets. Our collection of multiclass ligands should be of interest for pharmaceutical applications and further exploration of binding characteristics at the molecular level. Therefore, these highly promiscuous compounds are made publicly available.
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24
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Abstract
Aim: The druggability of epigenetic targets has prompted researchers to develop small-molecule therapeutics. However, no systematic assessment has ever been done to investigate the chemical space of epigenetic modulators. Herein, we report a comprehensive chemoinformatic analysis of epigenetic ligands from EpiDBase, HEMD, ChEMBL and PubChem databases. Results: Nearly, 0.45 × 106 ligands were analyzed for assay interference compounds, target profiling, drug-like properties and hit prioritization. After eliminating approximately 96,000 problematic compounds, the remaining 0.36 × 106 compounds were studied for their physicochemical distributions, principal component analysis and hit prioritization. More than 30% of assay interference compounds were determined for many proteins. Conclusion: This systematic assessment of epigenetic ligands will help in the enrichment of screening libraries with high-quality compounds and thus, the generation of efficacious drug candidates.
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Abstract
The Chemical Information Science Gateway (CISG) of F1000Research was originally conceptualized as a forum for high-quality publications in chemical information science (CIS) including chemoinformatics. Adding a publication venue with open access and open peer review to the CIS field was a prime motivation for the introduction of CISG, aiming to support open science in this area. Herein, the CISG concept is revisited and the development of the gateway over the past four years is reviewed. In addition, opportunities are discussed to better position CISG within the publication spectrum of F1000Research and further increase its visibility and attractiveness for scientific contributions.
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Abstract
Polypharmacology has expanded enormously over the last ten years, with several multitarget drugs (MTDs) already in the market. This Viewpoint provides a basis for a discussion about the critical need to develop MTDs in a more rationale and conscious way. A checklist to maximize success in polypharmacology is proposed.
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Affiliation(s)
- Maria Laura Bolognesi
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum − University of Bologna, I-40126 Bologna, Italy
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27
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Abstract
The development of high-throughput, data-intensive biomedical research assays and technologies has created a need for researchers to develop strategies for analyzing, integrating, and interpreting the massive amounts of data they generate. Although a wide variety of statistical methods have been designed to accommodate 'big data,' experiences with the use of artificial intelligence (AI) techniques suggest that they might be particularly appropriate. In addition, the results of the application of these assays reveal a great heterogeneity in the pathophysiologic factors and processes that contribute to disease, suggesting that there is a need to tailor, or 'personalize,' medicines to the nuanced and often unique features possessed by individual patients. Given how important data-intensive assays are to revealing appropriate intervention targets and strategies for treating an individual with a disease, AI can play an important role in the development of personalized medicines. We describe many areas where AI can play such a role and argue that AI's ability to advance personalized medicine will depend critically on not only the refinement of relevant assays, but also on ways of storing, aggregating, accessing, and ultimately integrating, the data they produce. We also point out the limitations of many AI techniques in developing personalized medicines as well as consider areas for further research.
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Affiliation(s)
- Nicholas J Schork
- Department of Quantitative Medicine, The Translational Genomics Research Institute (TGen), Phoenix, AZ, USA.
- The City of Hope/TGen IMPACT Center, Duarte, CA, USA.
- The University of California San Diego, La Jolla, CA, USA.
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28
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[Special Issue for Honor Award dedicating to Prof Kimito Funatsu](Mini-review)Meanings of the Honor Award for Prof Kimito Funatsu. JOURNAL OF COMPUTER AIDED CHEMISTRY 2019. [DOI: 10.2751/jcac.20.23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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29
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30
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Avram S, Curpan R, Bora A, Neanu C, Halip L. Enhancing Molecular Promiscuity Evaluation Through Assay Profiles. Pharm Res 2018; 35:240. [DOI: 10.1007/s11095-018-2523-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 10/10/2018] [Indexed: 10/28/2022]
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31
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Pinzi L, Caporuscio F, Rastelli G. Selection of protein conformations for structure-based polypharmacology studies. Drug Discov Today 2018; 23:1889-1896. [PMID: 30099123 DOI: 10.1016/j.drudis.2018.08.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 08/03/2018] [Accepted: 08/06/2018] [Indexed: 11/29/2022]
Abstract
Several drugs exert their therapeutic effect through the modulation of multiple targets. Structure-based approaches hold great promise for identifying compounds with the desired polypharmacological profiles. These methods use knowledge of the protein binding sites to identify stereoelectronically complementary ligands. The selection of the most suitable protein conformations to be used in the design process is vital, especially for multitarget drug design in which the same ligand has to be accommodated in multiple binding pockets. Herein, we focus on currently available techniques for the selection of the most suitable protein conformations for multitarget drug design, compare the potential advantages and limitations of each method, and comment on how their combination could help in polypharmacology drug design.
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Affiliation(s)
- Luca Pinzi
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125, Modena, Italy
| | - Fabiana Caporuscio
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125, Modena, Italy
| | - Giulio Rastelli
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125, Modena, Italy.
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33
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Young RJ, Leeson PD. Mapping the Efficiency and Physicochemical Trajectories of Successful Optimizations. J Med Chem 2018; 61:6421-6467. [DOI: 10.1021/acs.jmedchem.8b00180] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Robert J. Young
- GlaxoSmithKline, Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
| | - Paul D. Leeson
- Paul Leeson Consulting Ltd., The Malt House, Main Street, Congerstone, Nuneaton, Warwickshire CV13 6LZ, U.K
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34
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Miljković F, Bajorath J. Evaluation of Kinase Inhibitor Selectivity Using Cell-based Profiling Data. Mol Inform 2018; 37:e1800024. [PMID: 29600830 DOI: 10.1002/minf.201800024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 03/10/2018] [Indexed: 01/10/2023]
Abstract
Kinases are among the most heavily investigated drug targets and inhibition of kinases and kinase-dependent signaling has become a paradigm for therapeutic intervention. Kinase inhibitors and associated activity data have increasing 'big data' character, which presents challenges for computational analysis, but also unprecedented opportunities for learning from compound data and for data-driven medicinal chemistry. Herein, publicly available kinase inhibitor data are evaluated and a number of characteristics are discussed. In addition, selectivity of clinical kinase inhibitors is explored computationally on the basis of recently reported cell-based profiling data. For inhibitors shared by pairs of kinases, selectivity profiles were generated and a variety of selective inhibitors were identified. Uni-directional selectivity profiles revealed inhibitors that were selective for one kinase over the other, while bi-directional profiles uncovered compounds with inverted selectivity for paired kinases.
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Affiliation(s)
- Filip Miljković
- Department of Life Science Informatics Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Endenicher Allee 19c, D-53115, Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Endenicher Allee 19c, D-53115, Bonn, Germany
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35
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Opassi G, Gesù A, Massarotti A. The hitchhiker’s guide to the chemical-biological galaxy. Drug Discov Today 2018; 23:565-574. [DOI: 10.1016/j.drudis.2018.01.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Revised: 11/25/2017] [Accepted: 01/04/2018] [Indexed: 12/21/2022]
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36
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Cerchia C, Dimova D, Lavecchia A, Bajorath J. Exploring Structural Relationships between Bioactive and Commercial Chemical Space and Developing Target Hypotheses for Compound Acquisition. ACS OMEGA 2017; 2:7760-7766. [PMID: 30023563 PMCID: PMC6044811 DOI: 10.1021/acsomega.7b01338] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2017] [Accepted: 10/31/2017] [Indexed: 06/08/2023]
Abstract
Analog series were systematically extracted from more than 650 000 bioactive compounds originating from medicinal chemistry and screening sources and more than 3.6 million commercial compounds that were not biologically annotated. Then, analog series-based (ASB) scaffolds were generated. For each scaffold from a bioactive series, a target profile was derived and ASB scaffolds shared by bioactive and commercial compounds were determined. On the basis of our analysis, large segments of commercial chemical space were not yet explored biologically. Shared ASB scaffolds established structural relationships between bioactive and commercial chemical space, and the target profiles of these scaffolds were transferred to commercially available analogs of active compounds. This made it possible to derive target hypotheses for more than 37 000 compounds without biological annotations covering more than 1000 different targets. For many molecules, alternative target assignments were available. Target hypotheses for these compounds should be of interest, for example, for hit expansion, acquisition of compounds to design or further extend focused libraries for drug discovery, or testing of expanded analog series on different targets. They can also be used to search for analogs and complement compound series during target-directed optimization. Therefore, all of the commercial molecules with new target hypotheses as well as key scaffolds identified in our analysis and their target profiles are made freely available.
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Affiliation(s)
- Carmen Cerchia
- Department
of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology
and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn, Germany
- Department
of Pharmacy, “Drug Discovery” Laboratory, University of Naples Federico II, 80131 Naples, Italy
| | - Dilyana Dimova
- Department
of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology
and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn, Germany
| | - Antonio Lavecchia
- Department
of Pharmacy, “Drug Discovery” Laboratory, University of Naples Federico II, 80131 Naples, Italy
| | - Jürgen Bajorath
- Department
of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology
and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn, Germany
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38
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Mucaji P, Atanasov AG, Bak A, Kozik V, Sieron K, Olsen M, Pan W, Liu Y, Hu S, Lan J, Haider N, Musiol R, Vanco J, Diederich M, Ji S, Zitko J, Wang D, Agbaba D, Nikolic K, Oljacic S, Vucicevic J, Jezova D, Tsantili-Kakoulidou A, Tsopelas F, Giaginis C, Kowalska T, Sajewicz M, Silberring J, Mielczarek P, Smoluch M, Jendrzejewska I, Polanski J, Jampilek J. The Forty-Sixth Euro Congress on Drug Synthesis and Analysis: Snapshot †. Molecules 2017; 22:molecules22111848. [PMID: 29143778 PMCID: PMC6150335 DOI: 10.3390/molecules22111848] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 10/26/2017] [Accepted: 10/26/2017] [Indexed: 01/08/2023] Open
Abstract
The 46th EuroCongress on Drug Synthesis and Analysis (ECDSA-2017) was arranged within the celebration of the 65th Anniversary of the Faculty of Pharmacy at Comenius University in Bratislava, Slovakia from 5-8 September 2017 to get together specialists in medicinal chemistry, organic synthesis, pharmaceutical analysis, screening of bioactive compounds, pharmacology and drug formulations; promote the exchange of scientific results, methods and ideas; and encourage cooperation between researchers from all over the world. The topic of the conference, "Drug Synthesis and Analysis," meant that the symposium welcomed all pharmacists and/or researchers (chemists, analysts, biologists) and students interested in scientific work dealing with investigations of biologically active compounds as potential drugs. The authors of this manuscript were plenary speakers and other participants of the symposium and members of their research teams. The following summary highlights the major points/topics of the meeting.
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Affiliation(s)
- Pavel Mucaji
- Department of Pharmacognosy and Botany, Faculty of Pharmacy, Comenius University, Odbojarov 10, 83232 Bratislava, Slovakia.
| | - Atanas G Atanasov
- Institute of Genetics and Animal Breeding of the Polish Academy of Sciences, Postepu 36A, 05-552 Jastrzebiec, Poland.
- Department of Pharmacognosy, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria.
| | - Andrzej Bak
- Institute of Chemistry, University of Silesia, Szkolna 9, 40007 Katowice, Poland.
| | - Violetta Kozik
- Department of Synthesis Chemistry, Faculty of Mathematics, Physics and Chemistry, University of Silesia, Szkolna 9, 40007 Katowice, Poland.
| | - Karolina Sieron
- Department of Physical Medicine, Medical University of Silesia, Medykow 18, 40752 Katowice, Poland.
| | - Mark Olsen
- Department of Pharmaceutical Sciences, College of Pharmacy Glendale, Midwestern University, 19555 N. 59th Avenue, Glendale, AZ 85308, USA.
| | - Weidong Pan
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, 3491 Baijin Road, Guiyang 550014, China.
- Key Laboratory of Chemistry for Natural Products of Guizhou Province and Chinese Academy of Sciences, 3491 Baijin Road, Guiyang, 550014, China.
| | - Yazhou Liu
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, 3491 Baijin Road, Guiyang 550014, China.
- Key Laboratory of Chemistry for Natural Products of Guizhou Province and Chinese Academy of Sciences, 3491 Baijin Road, Guiyang, 550014, China.
| | - Shengchao Hu
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, 3491 Baijin Road, Guiyang 550014, China.
- Key Laboratory of Chemistry for Natural Products of Guizhou Province and Chinese Academy of Sciences, 3491 Baijin Road, Guiyang, 550014, China.
| | - Junjie Lan
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, 3491 Baijin Road, Guiyang 550014, China.
- Key Laboratory of Chemistry for Natural Products of Guizhou Province and Chinese Academy of Sciences, 3491 Baijin Road, Guiyang, 550014, China.
| | - Norbert Haider
- Department of Pharmaceutical Chemistry, University of Vienna, Althanstraße 14, A-1090 Vienna, Austria.
| | - Robert Musiol
- Institute of Chemistry, University of Silesia, Szkolna 9, 40007 Katowice, Poland.
| | - Jan Vanco
- Department of Inorganic Chemistry & Regional Centre of Advanced Technologies and Materials, Faculty of Science, Palacky University, 17. listopadu 12, 77146 Olomouc, Czech Republic.
| | - Marc Diederich
- Department of Pharmacy, College of Pharmacy, Seoul National University, 1 Gwanak-ro, Seoul 08826, Korea.
| | - Seungwon Ji
- Department of Pharmacy, College of Pharmacy, Seoul National University, 1 Gwanak-ro, Seoul 08826, Korea.
| | - Jan Zitko
- Department of Pharmaceutical Chemistry and Pharmaceutical Analysis, Faculty of Pharmacy in Hradec Kralove, Charles University, Heyrovskeho 1203, 50005 Hradec Kralove, Czech Republic.
| | - Dongdong Wang
- Institute of Genetics and Animal Breeding of the Polish Academy of Sciences, Postepu 36A, 05-552 Jastrzebiec, Poland.
- Department of Pharmacognosy, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria.
| | - Danica Agbaba
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia.
| | - Katarina Nikolic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia.
| | - Slavica Oljacic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia.
| | - Jelica Vucicevic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia.
| | - Daniela Jezova
- Laboratory of Pharmacological Neuroendocrinology, Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Dubravska cesta 9, 84505 Bratislava, Slovakia.
| | - Anna Tsantili-Kakoulidou
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Zografou, 15771 Athens, Greece.
| | - Fotios Tsopelas
- Laboratory of Inorganic and Analytical Chemistry, School of Chemical Engineering, National Technical University of Athens, Iroon Polytechniou 9, 15780 Athens, Greece.
| | - Constantinos Giaginis
- Department of Food Science and Nutrition, School of Environment, University of the Aegean, 81400 Myrina, Lemnos, Greece.
| | - Teresa Kowalska
- Institute of Chemistry, University of Silesia, Szkolna 9, 40007 Katowice, Poland.
| | - Mieczyslaw Sajewicz
- Institute of Chemistry, University of Silesia, Szkolna 9, 40007 Katowice, Poland.
| | - Jerzy Silberring
- Department of Biochemistry and Neurobiology, Faculty of Materials Science and Ceramics, AGH University of Science and Technology, Mickiewicza 30, 30059 Krakow, Poland.
| | - Przemyslaw Mielczarek
- Department of Biochemistry and Neurobiology, Faculty of Materials Science and Ceramics, AGH University of Science and Technology, Mickiewicza 30, 30059 Krakow, Poland.
| | - Marek Smoluch
- Department of Biochemistry and Neurobiology, Faculty of Materials Science and Ceramics, AGH University of Science and Technology, Mickiewicza 30, 30059 Krakow, Poland.
| | - Izabela Jendrzejewska
- Department of Crystallography, Faculty of Mathematics, Physics and Chemistry, University of Silesia, Bankowa 12, 40006 Katowice, Poland.
| | - Jaroslaw Polanski
- Institute of Chemistry, University of Silesia, Szkolna 9, 40007 Katowice, Poland.
| | - Josef Jampilek
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Comenius University, Odbojarov 10, 83232 Bratislava, Slovakia.
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Abstract
Broadly defined, chemical information science (CIS) covers chemical structure and data analysis including biological activity data as well as processing, organization, and retrieval of any form of chemical information. The CIS Gateway (CISG) of F1000Research was created to communicate research involving the entire spectrum of chemical information, including chem(o)informatics. CISG provides a forum for high-quality publications and a meaningful alternative to conventional journals. This gateway is supported by leading experts in the field recognizing the need for open science and a flexible publication platform enabling off-the-beaten path contributions. This editorial aims to further rationalize the scope of CISG, position it within its scientific environment, and open it up to a wider audience. Chemical information science is an interdisciplinary field with high potential to interface with experimental work.
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Affiliation(s)
- Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany
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Gilberg E, Stumpfe D, Bajorath J. Towards a systematic assessment of assay interference: Identification of extensively tested compounds with high assay promiscuity. F1000Res 2017; 6. [PMID: 28928939 PMCID: PMC5596351 DOI: 10.12688/f1000research.12370.2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/11/2017] [Indexed: 12/16/2022] Open
Abstract
A large-scale statistical analysis of hit rates of extensively assayed compounds is presented to provide a basis for a further assessment of assay interference potential and multi-target activities. A special feature of this investigation has been the inclusion of compound series information in activity analysis and the characterization of analog series using different parameters derived from assay statistics. No prior knowledge of compounds or targets was taken into consideration in the data-driven study of analog series. It was anticipated that taking large volumes of activity data, assay frequency, and assay overlap information into account would lead to statistically sound and chemically meaningful results. More than 6000 unique series of analogs with high hit rates were identified, more than 5000 of which did not contain known interference candidates, hence providing ample opportunities for follow-up analyses from a medicinal chemistry perspective.
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Affiliation(s)
- Erik Gilberg
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, D-53113, Germany
| | - Dagmar Stumpfe
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, D-53113, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, D-53113, Germany
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41
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Gilberg E, Stumpfe D, Bajorath J. Towards a systematic assessment of assay interference: Identification of extensively tested compounds with high assay promiscuity. F1000Res 2017; 6. [PMID: 28928939 DOI: 10.12688/f1000research.12370.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/16/2017] [Indexed: 11/20/2022] Open
Abstract
A large-scale statistical analysis of hit rates of extensively assayed compounds is presented to provide a basis for a further assessment of assay interference potential and multi-target activities. A special feature of this investigation has been the inclusion of compound series information in activity analysis and the characterization of analog series using different parameters derived from assay statistics. No prior knowledge of compounds or targets was taken into consideration in the data-driven study of analog series. It was anticipated that taking large volumes of activity data, assay frequency, and assay overlap information into account would lead to statistically sound and chemically meaningful results. More than 6000 unique series of analogs with high hit rates were identified, more than 5000 of which did not contain known interference candidates, hence providing ample opportunities for follow-up analyses from a medicinal chemistry perspective.
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Affiliation(s)
- Erik Gilberg
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, D-53113, Germany
| | - Dagmar Stumpfe
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, D-53113, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, D-53113, Germany
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42
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Identifying relationships between unrelated pharmaceutical target proteins on the basis of shared active compounds. Future Sci OA 2017; 3:FSO212. [PMID: 28884009 PMCID: PMC5583696 DOI: 10.4155/fsoa-2017-0037] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Accepted: 04/26/2017] [Indexed: 12/31/2022] Open
Abstract
Aim: Computational exploration of small-molecule-based relationships between target proteins from different families. Materials & methods: Target annotations of drugs and other bioactive compounds were systematically analyzed on the basis of high-confidence activity data. Results: A total of 286 novel chemical links were established between distantly related or unrelated target proteins. These relationships involved a total of 1859 bioactive compounds including 147 drugs and 141 targets. Conclusion: Computational analysis of large amounts of compounds and activity data has revealed unexpected relationships between diverse target proteins on the basis of compounds they share. These relationships are relevant for drug discovery efforts. Target pairs that we have identified and associated compound information are made freely available. Relationships between proteins are usually studied by comparing their sequences and functions. However, in addition to biological relationships, chemical links between proteins can also be established by searching for active compounds they share. If proteins have active compounds in common, they are likely to interact with small molecules in similar ways, which provides important clues for drug discovery. Therefore, we have systematically searched for unexpected compound-based relationships between proteins. Shown here are exemplary small molecules that are active against two targets with different functions. Thus, these compounds establish an unexpected chemical/ligand-binding relationship between these targets.
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