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Vogt M. Chemoinformatic approaches for navigating large chemical spaces. Expert Opin Drug Discov 2024; 19:403-414. [PMID: 38300511 DOI: 10.1080/17460441.2024.2313475] [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: 12/12/2023] [Accepted: 01/30/2024] [Indexed: 02/02/2024]
Abstract
INTRODUCTION Large chemical spaces (CSs) include traditional large compound collections, combinatorial libraries covering billions to trillions of molecules, DNA-encoded chemical libraries comprising complete combinatorial CSs in a single mixture, and virtual CSs explored by generative models. The diverse nature of these types of CSs require different chemoinformatic approaches for navigation. AREAS COVERED An overview of different types of large CSs is provided. Molecular representations and similarity metrics suitable for large CS exploration are discussed. A summary of navigation of CSs in generative models is provided. Methods for characterizing and comparing CSs are discussed. EXPERT OPINION The size of large CSs might restrict navigation to specialized algorithms and limit it to considering neighborhoods of structurally similar molecules. Efficient navigation of large CSs not only requires methods that scale with size but also requires smart approaches that focus on better but not necessarily larger molecule selections. Deep generative models aim to provide such approaches by implicitly learning features relevant for targeted biological properties. It is unclear whether these models can fulfill this ideal as validation is difficult as long as the covered CSs remain mainly virtual without experimental verification.
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Affiliation(s)
- Martin Vogt
- 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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2
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Orsi M, Probst D, Schwaller P, Reymond JL. Alchemical analysis of FDA approved drugs. DIGITAL DISCOVERY 2023; 2:1289-1296. [PMID: 38013905 PMCID: PMC10561545 DOI: 10.1039/d3dd00039g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/29/2023] [Indexed: 11/29/2023]
Abstract
Chemical space maps help visualize similarities within molecular sets. However, there are many different molecular similarity measures resulting in a confusing number of possible comparisons. To overcome this limitation, we exploit the fact that tools designed for reaction informatics also work for alchemical processes that do not obey Lavoisier's principle, such as the transmutation of lead into gold. We start by using the differential reaction fingerprint (DRFP) to create tree-maps (TMAPs) representing the chemical space of pairs of drugs selected as being similar according to various molecular fingerprints. We then use the Transformer-based RXNMapper model to understand structural relationships between drugs, and its confidence score to distinguish between pairs related by chemically feasible transformations and pairs related by alchemical transmutations. This analysis reveals a diversity of structural similarity relationships that are otherwise difficult to analyze simultaneously. We exemplify this approach by visualizing FDA-approved drugs, EGFR inhibitors, and polymyxin B analogs.
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Affiliation(s)
- Markus Orsi
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland
| | - Daniel Probst
- Ecole Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
| | | | - Jean-Louis Reymond
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland
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3
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López-Pérez K, López-López E, Medina-Franco JL, Miranda-Quintana RA. Sampling and Mapping Chemical Space with Extended Similarity Indices. Molecules 2023; 28:6333. [PMID: 37687162 PMCID: PMC10489020 DOI: 10.3390/molecules28176333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/24/2023] [Accepted: 08/26/2023] [Indexed: 09/10/2023] Open
Abstract
Visualization of the chemical space is useful in many aspects of chemistry, including compound library design, diversity analysis, and exploring structure-property relationships, to name a few. Examples of notable research areas where the visualization of chemical space has strong applications are drug discovery and natural product research. However, the sheer volume of even comparatively small sub-sections of chemical space implies that we need to use approximations at the time of navigating through chemical space. ChemMaps is a visualization methodology that approximates the distribution of compounds in large datasets based on the selection of satellite compounds that yield a similar mapping of the whole dataset when principal component analysis on a similarity matrix is performed. Here, we show how the recently proposed extended similarity indices can help find regions that are relevant to sample satellites and reduce the amount of high-dimensional data needed to describe a library's chemical space.
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Affiliation(s)
- Kenneth López-Pérez
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL 32611, USA;
| | - Edgar López-López
- DIFACQUIM Research Group, Department of Pharmacy, National Autonomous University of Mexico, Mexico City 04510, Mexico;
- Department of Chemistry and Graduate Program in Pharmacology, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07000, Mexico
| | - José L. Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, National Autonomous University of Mexico, Mexico City 04510, Mexico;
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4
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Yang T, Li Z, Chen Y, Feng D, Wang G, Fu Z, Ding X, Tan X, Zhao J, Luo X, Chen K, Jiang H, Zheng M. DrugSpaceX: a large screenable and synthetically tractable database extending drug space. Nucleic Acids Res 2021; 49:D1170-D1178. [PMID: 33104791 PMCID: PMC7778939 DOI: 10.1093/nar/gkaa920] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 09/11/2020] [Accepted: 10/05/2020] [Indexed: 02/07/2023] Open
Abstract
One of the most prominent topics in drug discovery is efficient exploration of the vast drug-like chemical space to find synthesizable and novel chemical structures with desired biological properties. To address this challenge, we created the DrugSpaceX (https://drugspacex.simm.ac.cn/) database based on expert-defined transformations of approved drug molecules. The current version of DrugSpaceX contains >100 million transformed chemical products for virtual screening, with outstanding characteristics in terms of structural novelty, diversity and large three-dimensional chemical space coverage. To illustrate its practical application in drug discovery, we used a case study of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, to show DrugSpaceX performing a quick search of initial hit compounds. Additionally, for ligand identification and optimization purposes, DrugSpaceX also provides several subsets for download, including a 10% diversity subset, an extended drug-like subset, a drug-like subset, a lead-like subset, and a fragment-like subset. In addition to chemical properties and transformation instructions, DrugSpaceX can locate the position of transformation, which will enable medicinal chemists to easily integrate strategy planning and protection design.
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Affiliation(s)
- Tianbiao Yang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- Department of Pharmacy, University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
| | - Zhaojun Li
- School of Information Management, Dezhou University, No. 566 University Rd. West, Dezhou 253023, Shandong, China
| | - Yingjia Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- Department of Pharmacy, University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | - Dan Feng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- Department of Chemistry, College of Sciences, Shanghai University, Shanghai, China
| | - Guangchao Wang
- School of Information Management, Dezhou University, No. 566 University Rd. West, Dezhou 253023, Shandong, China
| | - Zunyun Fu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- Nanjing University of Chinese Medicine, 138 Xianlin Road, Jiangsu, Nanjing 210023, China
| | - Xiaoyu Ding
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- Department of Pharmacy, University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | - Xiaoqin Tan
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- Department of Pharmacy, University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | - Jihui Zhao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- Department of Pharmacy, University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | - Xiaomin Luo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- Department of Pharmacy, University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | - Kaixian Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- Department of Pharmacy, University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | - Hualiang Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- Department of Pharmacy, University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
- School of Life Science and Technology, ShanghaiTech University, 393 Huaxiazhong Road, Shanghai 200031, China
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- Department of Pharmacy, University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
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5
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Gazgalis D, Zaka M, Abbasi BH, Logothetis DE, Mezei M, Cui M. Protein Binding Pocket Optimization for Virtual High-Throughput Screening (vHTS) Drug Discovery. ACS OMEGA 2020; 5:14297-14307. [PMID: 32596567 PMCID: PMC7315428 DOI: 10.1021/acsomega.0c00522] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 05/28/2020] [Indexed: 06/11/2023]
Abstract
The virtual high-throughput screening (vHTS) approach has been widely used for large database screening to identify potential lead compounds for drug discovery. Due to its high computational demands, docking that allows receptor flexibility has been a challenging problem for virtual screening. Therefore, the selection of protein target conformations is crucial to produce useful vHTS results. Since only a single protein structure is used to screen large databases in most vHTS studies, the main challenge is to reduce false negative rates in selecting compounds for in vitro tests. False negatives are most likely to occur when using apo structures or homology models of protein targets due to the small volume of the binding pocket formed by incorrect side-chain conformations. Even holo protein structures can exhibit high false negative rates due to ligand-induced fit effects, since the shape of the binding pocket highly depends on its bound ligand. To reduce false negative rates and improve success rates for vHTS in drug discovery, we have developed a new Monte Carlo-based approach that optimizes the binding pocket of protein targets. This newly developed Monte Carlo pocket optimization (MCPO) approach was assessed on several datasets showing promising results. The binding pocket optimization approach could be a useful tool for vHTS-based drug discovery, especially in cases when only apo structures or homology models are available.
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Affiliation(s)
- Dimitris Gazgalis
- Department
of Pharmaceutical Sciences, Northeastern
University School of Pharmacy, Boston, Massachusetts 02115, United States
| | - Mehreen Zaka
- Department
of Pharmaceutical Sciences, Northeastern
University School of Pharmacy, Boston, Massachusetts 02115, United States
- Department
of Biotechnology, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Bilal Haider Abbasi
- Department
of Biotechnology, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Diomedes E. Logothetis
- Department
of Pharmaceutical Sciences, Northeastern
University School of Pharmacy, Boston, Massachusetts 02115, United States
| | - Mihaly Mezei
- Department
of Pharmacological Sciences, Icahn School
of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Meng Cui
- Department
of Pharmaceutical Sciences, Northeastern
University School of Pharmacy, Boston, Massachusetts 02115, United States
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6
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Probst D, Reymond JL. Visualization of very large high-dimensional data sets as minimum spanning trees. J Cheminform 2020; 12:12. [PMID: 33431043 PMCID: PMC7015965 DOI: 10.1186/s13321-020-0416-x] [Citation(s) in RCA: 116] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 02/04/2020] [Indexed: 01/10/2023] Open
Abstract
The chemical sciences are producing an unprecedented amount of large, high-dimensional data sets containing chemical structures and associated properties. However, there are currently no algorithms to visualize such data while preserving both global and local features with a sufficient level of detail to allow for human inspection and interpretation. Here, we propose a solution to this problem with a new data visualization method, TMAP, capable of representing data sets of up to millions of data points and arbitrary high dimensionality as a two-dimensional tree (http://tmap.gdb.tools). Visualizations based on TMAP are better suited than t-SNE or UMAP for the exploration and interpretation of large data sets due to their tree-like nature, increased local and global neighborhood and structure preservation, and the transparency of the methods the algorithm is based on. We apply TMAP to the most used chemistry data sets including databases of molecules such as ChEMBL, FDB17, the Natural Products Atlas, DSSTox, as well as to the MoleculeNet benchmark collection of data sets. We also show its broad applicability with further examples from biology, particle physics, and literature.![]()
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Affiliation(s)
- Daniel Probst
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland.
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland.
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7
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Bühlmann S, Reymond JL. ChEMBL-Likeness Score and Database GDBChEMBL. Front Chem 2020; 8:46. [PMID: 32117874 PMCID: PMC7010641 DOI: 10.3389/fchem.2020.00046] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 01/15/2020] [Indexed: 01/02/2023] Open
Abstract
The generated database GDB17 enumerates 166.4 billion molecules up to 17 atoms of C, N, O, S and halogens following simple rules of chemical stability and synthetic feasibility. However, most molecules in GDB17 are too complex to be considered for chemical synthesis. To address this limitation, we report GDBChEMBL as a subset of GDB17 featuring 10 million molecules selected according to a ChEMBL-likeness score (CLscore) calculated from the frequency of occurrence of circular substructures in ChEMBL, followed by uniform sampling across molecular size, stereocenters and heteroatoms. Compared to the previously reported subsets FDB17 and GDBMedChem selected from GDB17 by fragment-likeness, respectively, medicinal chemistry criteria, our new subset features molecules with higher synthetic accessibility and possibly bioactivity yet retains a broad and continuous coverage of chemical space typical of the entire GDB17. GDBChEMBL is accessible at http://gdb.unibe.ch for download and for browsing using an interactive chemical space map at http://faerun.gdb.tools.
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Affiliation(s)
- Sven Bühlmann
- Department of Chemistry and Biochemistry, University of Bern, Bern, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, University of Bern, Bern, Switzerland
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8
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Capecchi A, Awale M, Probst D, Reymond JL. PubChem and ChEMBL beyond Lipinski. Mol Inform 2019; 38:e1900016. [PMID: 30844149 DOI: 10.1002/minf.201900016] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 02/18/2019] [Indexed: 12/13/2022]
Abstract
Seven million of the currently 94 million entries in the PubChem database break at least one of the four Lipinski constraints for oral bioavailability, 183,185 of which are also found in the ChEMBL database. These non-Lipinski PubChem (NLP) and ChEMBL (NLC) subsets are interesting because they contain new modalities that can display biological properties not accessible to small molecule drugs. Unfortunately, the current search tools in PubChem and ChEMBL are designed for small molecules and are not well suited to explore these subsets, which therefore remain poorly appreciated. Herein we report MXFP (macromolecule extended atom-pair fingerprint), a 217-D fingerprint tailored to analyze large molecules in terms of molecular shape and pharmacophores. We implement MXFP in two web-based applications, the first one to visualize NLP and NLC interactively using Faerun (http://faerun.gdb.tools/), the second one to perform MXFP nearest neighbor searches in NLP and NLC (http://similaritysearch.gdb.tools/). We show that these tools provide a meaningful insight into the diversity of large molecules in NLP and NLC. The interactive tools presented here are publicly available at http://gdb.unibe.ch and can be used freely to explore and better understand the diversity of non-Lipinski molecules in PubChem and ChEMBL.
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Affiliation(s)
- Alice Capecchi
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
| | - Mahendra Awale
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
| | - Daniel Probst
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
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9
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Delalande C, Awale M, Rubin M, Probst D, Ozhathil LC, Gertsch J, Abriel H, Reymond JL. Optimizing TRPM4 inhibitors in the MHFP6 chemical space. Eur J Med Chem 2019; 166:167-177. [DOI: 10.1016/j.ejmech.2019.01.048] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 12/18/2018] [Accepted: 01/19/2019] [Indexed: 12/12/2022]
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10
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Poirier M, Awale M, Roelli MA, Giuffredi GT, Ruddigkeit L, Evensen L, Stooss A, Calarco S, Lorens JB, Charles RP, Reymond JL. Identifying Lysophosphatidic Acid Acyltransferase β (LPAAT-β) as the Target of a Nanomolar Angiogenesis Inhibitor from a Phenotypic Screen Using the Polypharmacology Browser PPB2. ChemMedChem 2018; 14:224-236. [PMID: 30520265 DOI: 10.1002/cmdc.201800554] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Indexed: 12/11/2022]
Abstract
By screening a focused library of kinase inhibitor analogues in a phenotypic co-culture assay for angiogenesis inhibition, we identified an aminotriazine that acts as a cytostatic nanomolar inhibitor. However, this aminotriazine was found to be completely inactive in a whole-kinome profiling assay. To decipher its mechanism of action, we used the online target prediction tool PPB2 (http://ppb2.gdb.tools), which suggested lysophosphatidic acid acyltransferase β (LPAAT-β) as a possible target for this aminotriazine as well as several analogues identified by structure-activity relationship profiling. LPAAT-β inhibition (IC50 ≈15 nm) was confirmed in a biochemical assay and by its effects on cell proliferation in comparison with a known LPAAT-β inhibitor. These experiments illustrate the value of target-prediction tools to guide target identification for phenotypic screening hits and significantly expand the rather limited pharmacology of LPAAT-β inhibitors.
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Affiliation(s)
- Marion Poirier
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
| | - Mahendra Awale
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
| | - Matthias A Roelli
- Institute of Biochemistry and Molecular Medicine, National Center of Competence in Research NCCR TransCure, University of Bern, Bühlstrasse 28, 3000, Bern 9, Switzerland
| | - Guy T Giuffredi
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
| | - Lars Ruddigkeit
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
| | - Lasse Evensen
- Department of Biomedicine, Centre for Cancer Biomarkers (CCBIO), University of Bergen, Jonas Lies vei 91, 5009, Bergen, Norway
| | - Amandine Stooss
- Institute of Biochemistry and Molecular Medicine, National Center of Competence in Research NCCR TransCure, University of Bern, Bühlstrasse 28, 3000, Bern 9, Switzerland
| | - Serafina Calarco
- Institute of Biochemistry and Molecular Medicine, National Center of Competence in Research NCCR TransCure, University of Bern, Bühlstrasse 28, 3000, Bern 9, Switzerland
| | - James B Lorens
- Department of Biomedicine, Centre for Cancer Biomarkers (CCBIO), University of Bergen, Jonas Lies vei 91, 5009, Bergen, Norway
| | - Roch-Philippe Charles
- Institute of Biochemistry and Molecular Medicine, National Center of Competence in Research NCCR TransCure, University of Bern, Bühlstrasse 28, 3000, Bern 9, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
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11
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Ozhathil LC, Delalande C, Bianchi B, Nemeth G, Kappel S, Thomet U, Ross‐Kaschitza D, Simonin C, Rubin M, Gertsch J, Lochner M, Peinelt C, Reymond J, Abriel H. Identification of potent and selective small molecule inhibitors of the cation channel TRPM4. Br J Pharmacol 2018; 175:2504-2519. [PMID: 29579323 PMCID: PMC6002741 DOI: 10.1111/bph.14220] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 03/08/2018] [Accepted: 03/16/2018] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND AND PURPOSE TRPM4 is a calcium-activated non-selective cation channel expressed in many tissues and implicated in several diseases, and has not yet been validated as a therapeutic target due to the lack of potent and selective inhibitors. We sought to discover a novel series of small-molecule inhibitors by combining in silico methods and cell-based screening assay, with sub-micromolar potency and improved selectivity from previously reported TRPM4 inhibitors. EXPERIMENTAL APPROACH Here, we developed a high throughput screening compatible assay to record TRPM4-mediated Na+ influx in cells using a Na+ -sensitive dye and used this assay to screen a small set of compounds selected by ligand-based virtual screening using previously known weakly active and non-selective TRPM4 inhibitors as seed molecules. Conventional electrophysiological methods were used to validate the potency and selectivity of the hit compounds in HEK293 cells overexpressing TRPM4 and in endogenously expressing prostate cancer cell line LNCaP. Chemical chaperone property of compound 5 was studied using Western blots and electrophysiology experiments. KEY RESULTS A series of halogenated anthranilic amides were identified with TRPM4 inhibitory properties with sub-micromolar potency and adequate selectivity. We also showed for the first time that a naturally occurring variant of TRPM4, which displays loss-of-expression and function, is rescued by the most promising compound 5 identified in this study. CONCLUSIONS AND IMPLICATIONS The discovery of compound 5, a potent and selective inhibitor of TRPM4 with an additional chemical chaperone feature, revealed new opportunities for studying the role of TRPM4 in human diseases and developing clinical drug candidates.
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Affiliation(s)
- Lijo Cherian Ozhathil
- Institute of Biochemistry and Molecular Medicine, National Center of Competence in Research NCCR TransCureUniversity of BernBernSwitzerland
| | - Clémence Delalande
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCureUniversity of BernBernSwitzerland
| | - Beatrice Bianchi
- Institute of Biochemistry and Molecular Medicine, National Center of Competence in Research NCCR TransCureUniversity of BernBernSwitzerland
| | - Gabor Nemeth
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCureUniversity of BernBernSwitzerland
| | - Sven Kappel
- Institute of Biochemistry and Molecular Medicine, National Center of Competence in Research NCCR TransCureUniversity of BernBernSwitzerland
| | - Urs Thomet
- Institute of Biochemistry and Molecular Medicine, National Center of Competence in Research NCCR TransCureUniversity of BernBernSwitzerland
| | - Daniela Ross‐Kaschitza
- Institute of Biochemistry and Molecular Medicine, National Center of Competence in Research NCCR TransCureUniversity of BernBernSwitzerland
| | - Céline Simonin
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCureUniversity of BernBernSwitzerland
| | - Matthias Rubin
- Institute of Biochemistry and Molecular Medicine, National Center of Competence in Research NCCR TransCureUniversity of BernBernSwitzerland
| | - Jürg Gertsch
- Institute of Biochemistry and Molecular Medicine, National Center of Competence in Research NCCR TransCureUniversity of BernBernSwitzerland
| | - Martin Lochner
- Institute of Biochemistry and Molecular Medicine, National Center of Competence in Research NCCR TransCureUniversity of BernBernSwitzerland
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCureUniversity of BernBernSwitzerland
| | - Christine Peinelt
- Institute of Biochemistry and Molecular Medicine, National Center of Competence in Research NCCR TransCureUniversity of BernBernSwitzerland
| | - Jean‐Louis Reymond
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCureUniversity of BernBernSwitzerland
| | - Hugues Abriel
- Institute of Biochemistry and Molecular Medicine, National Center of Competence in Research NCCR TransCureUniversity of BernBernSwitzerland
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12
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Probst D, Reymond JL. SmilesDrawer: Parsing and Drawing SMILES-Encoded Molecular Structures Using Client-Side JavaScript. J Chem Inf Model 2018; 58:1-7. [PMID: 29257869 DOI: 10.1021/acs.jcim.7b00425] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Here we present SmilesDrawer, a dependency-free JavaScript component capable of both parsing and drawing SMILES-encoded molecular structures client-side, developed to be easily integrated into web projects and to display organic molecules in large numbers and fast succession. SmilesDrawer can draw structurally and stereochemically complex structures such as maitotoxin and C60 without using templates, yet has an exceptionally small computational footprint and low memory usage without the requirement for loading images or any other form of client-server communication, making it easy to integrate even in secure (intranet, firewalled) or offline applications. These features allow the rendering of thousands of molecular structure drawings on a single web page within seconds on a wide range of hardware supporting modern browsers. The source code as well as the most recent build of SmilesDrawer is available on Github ( http://doc.gdb.tools/smilesDrawer/ ). Both yarn and npm packages are also available.
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Affiliation(s)
- Daniel Probst
- Department of Chemistry and Biochemistry, National Center for Competence in Research NCCR TransCure, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, National Center for Competence in Research NCCR TransCure, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
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Visini R, Arús-Pous J, Awale M, Reymond JL. Virtual Exploration of the Ring Systems Chemical Universe. J Chem Inf Model 2017; 57:2707-2718. [PMID: 29019686 DOI: 10.1021/acs.jcim.7b00457] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Here, we explore the chemical space of all virtually possible organic molecules focusing on ring systems, which represent the cyclic cores of organic molecules obtained by removing all acyclic bonds and converting all remaining atoms to carbon. This approach circumvents the combinatorial explosion encountered when enumerating the molecules themselves. We report the chemical universe database GDB4c containing 916 130 ring systems up to four saturated or aromatic rings and maximum ring size of 14 atoms and GDB4c3D containing the corresponding 6 555 929 stereoisomers. Almost all (98.6%) of these ring systems are unknown and represent chiral 3D-shaped macrocycles containing small rings and quaternary centers reminiscent of polycyclic natural products. We envision that GDB4c can serve to select new ring systems from which to design analogs of such natural products. The database is available for download at www.gdb.unibe.ch together with interactive visualization and search tools as a resource for molecular design.
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Affiliation(s)
- Ricardo Visini
- Department of Chemistry and Biochemistry, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
| | - Josep Arús-Pous
- Department of Chemistry and Biochemistry, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
| | - Mahendra Awale
- Department of Chemistry and Biochemistry, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
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14
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Naveja JJ, Medina-Franco JL. ChemMaps: Towards an approach for visualizing the chemical space based on adaptive satellite compounds. F1000Res 2017; 6. [PMID: 28794856 PMCID: PMC5538041 DOI: 10.12688/f1000research.12095.2] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/03/2017] [Indexed: 01/22/2023] Open
Abstract
We present a novel approach called ChemMaps for visualizing chemical space based on the similarity matrix of compound datasets generated with molecular fingerprints’ similarity. The method uses a ‘satellites’ approach, where satellites are, in principle, molecules whose similarity to the rest of the molecules in the database provides sufficient information for generating a visualization of the chemical space. Such an approach could help make chemical space visualizations more efficient. We hereby describe a proof-of-principle application of the method to various databases that have different diversity measures. Unsurprisingly, we found the method works better with databases that have low 2D diversity. 3D diversity played a secondary role, although it seems to be more relevant as 2D diversity increases. For less diverse datasets, taking as few as 25% satellites seems to be sufficient for a fair depiction of the chemical space. We propose to iteratively increase the satellites number by a factor of 5% relative to the whole database, and stop when the new and the prior chemical space correlate highly. This Research Note represents a first exploratory step, prior to the full application of this method for several datasets.
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Affiliation(s)
- J Jesús Naveja
- Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City, 04510, Mexico.,PECEM, Faculty of Medicine, Universidad Nacional Autónoma de México, Mexico City, 04510, Mexico
| | - José L Medina-Franco
- Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City, 04510, Mexico
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15
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Abstract
To better understand chemical space we recently enumerated the database GDB-17 containing 166.4 billion possible molecules up to 17 atoms of C, N, O, S and halogen following the simple rules of chemical stability and synthetic feasibility. However, due to the combinatorial explosion caused by systematic enumeration GDB-17 is strongly biased toward the largest, functionally and stereochemically most complex molecules and far too large for most virtual screening tools. Herein we selected a much smaller subset of GDB-17, called the fragment database FDB-17, which contains 10 million fragmentlike molecules evenly covering a broad value range for molecular size, polarity, and stereochemical complexity. The database is available at www.gdb.unibe.ch for download and free use, together with an interactive visualization application and a Web-based nearest neighbor search tool to facilitate the selection of new fragment-sized molecules for chemical synthesis.
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Affiliation(s)
- Ricardo Visini
- Department of Chemistry and Biochemistry, University of Bern , Freiestrasse 3, 3012 Berne, Switzerland
| | - Mahendra Awale
- Department of Chemistry and Biochemistry, University of Bern , Freiestrasse 3, 3012 Berne, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, University of Bern , Freiestrasse 3, 3012 Berne, Switzerland
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16
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Awale M, Probst D, Reymond JL. WebMolCS: A Web-Based Interface for Visualizing Molecules in Three-Dimensional Chemical Spaces. J Chem Inf Model 2017; 57:643-649. [PMID: 28316236 DOI: 10.1021/acs.jcim.6b00690] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The concept of chemical space provides a convenient framework to analyze large collections of molecules by placing them in property spaces where distances represent similarities. Here we report webMolCS, a new type of web-based interface visualizing up to 5000 user-defined molecules in six different three-dimensional (3D) chemical spaces obtained by principal component analysis or similarity mapping of multidimensional property spaces describing composition (MQN: 42D molecular quantum numbers, SMIfp: 34D SMILES fingerprint), shapes and pharmacophores (APfp: 20D atom pair fingerprint, Xfp: 55D category extended atom pair fingerprint), and substructures (Sfp: 1024D binary substructure fingerprint, ECfp4:1024D extended connectivity fingerprint). Each molecule is shown as a sphere, and its structure appears on mouse over. The sphere is color-coded by similarity to the first compound in the list, by the list rank, or by a user-defined value, which reveals the relationship between any property encoded by these values and structural similarities. WebMolCS is freely available at www.gdb.unibe.ch .
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Affiliation(s)
- Mahendra Awale
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
| | - Daniel Probst
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
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Tetko IV, Engkvist O, Koch U, Reymond JL, Chen H. BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry. Mol Inform 2016; 35:615-621. [PMID: 27464907 PMCID: PMC5129546 DOI: 10.1002/minf.201600073] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Accepted: 07/06/2016] [Indexed: 01/19/2023]
Abstract
The increasing volume of biomedical data in chemistry and life sciences requires the development of new methods and approaches for their handling. Here, we briefly discuss some challenges and opportunities of this fast growing area of research with a focus on those to be addressed within the BIGCHEM project. The article starts with a brief description of some available resources for “Big Data” in chemistry and a discussion of the importance of data quality. We then discuss challenges with visualization of millions of compounds by combining chemical and biological data, the expectations from mining the “Big Data” using advanced machine‐learning methods, and their applications in polypharmacology prediction and target de‐convolution in phenotypic screening. We show that the efficient exploration of billions of molecules requires the development of smart strategies. We also address the issue of secure information sharing without disclosing chemical structures, which is critical to enable bi‐party or multi‐party data sharing. Data sharing is important in the context of the recent trend of “open innovation” in pharmaceutical industry, which has led to not only more information sharing among academics and pharma industries but also the so‐called “precompetitive” collaboration between pharma companies. At the end we highlight the importance of education in “Big Data” for further progress of this area.
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Affiliation(s)
- Igor V Tetko
- Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Institute of Structural Biology, Ingolstädter Landstraße 1, b. 60w, D-85764, Neuherberg, Germany.,BIGCHEM GmbH, Ingolstädter Landstraße 1, b. 60w, D-85764, Neuherberg, Germany
| | - Ola Engkvist
- Discovery Sciences, AstraZeneca R&D Gothenburg, Pepparedsleden 1, Mölndal, SE-43183, Sweden
| | - Uwe Koch
- Lead Discovery Center GmbH, Otto-Hahn Strasse 15, Dortmund, 44227, Germany
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
| | - Hongming Chen
- Discovery Sciences, AstraZeneca R&D Gothenburg, Pepparedsleden 1, Mölndal, SE-43183, Sweden
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18
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Awale M, Reymond JL. Web-based 3D-visualization of the DrugBank chemical space. J Cheminform 2016; 8:25. [PMID: 27148409 PMCID: PMC4855437 DOI: 10.1186/s13321-016-0138-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Accepted: 04/27/2016] [Indexed: 12/14/2022] Open
Abstract
Background Similarly to the periodic table for elements, chemical space offers an organizing principle for representing the diversity of organic molecules, usually in the form of multi-dimensional property spaces that are subjected to dimensionality reduction methods to obtain 3D-spaces or 2D-maps suitable for visual inspection. Unfortunately, tools to look at chemical space on the internet are currently very limited. Results Herein we present webDrugCS, a web application freely available at www.gdb.unibe.ch to visualize DrugBank (www.drugbank.ca, containing over 6000 investigational and approved drugs) in five different property spaces. WebDrugCS displays 3D-clouds of color-coded grid points representing molecules, whose structural formula is displayed on mouse over with an option to link to the corresponding molecule page at the DrugBank website. The 3D-clouds are obtained by principal component analysis of high dimensional property spaces describing constitution and topology (42D molecular quantum numbers MQN), structural features (34D SMILES fingerprint SMIfp), molecular shape (20D atom pair fingerprint APfp), pharmacophores (55D atom category extended atom pair fingerprint Xfp) and substructures (1024D binary substructure fingerprint Sfp). User defined molecules can be uploaded as SMILES lists and displayed together with DrugBank. In contrast to 2D-maps where many compounds fold onto each other, these 3D-spaces have a comparable resolution to their parent high-dimensional chemical space. Conclusion To the best of our knowledge webDrugCS is the first publicly available web tool for interactive visualization and exploration of the DrugBank chemical space in 3D. WebDrugCS works on computers, tablets and phones, and facilitates the visual exploration of DrugBank to rapidly learn about the structural diversity of small molecule drugs.webDrugCS visualization of DrugBank projected in 3D MQN space color-coded by ring count, with pointer showing the drug 5-fluorouracil. ![]()
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Affiliation(s)
- Mahendra Awale
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland
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Abstract
Shown is a section of an SAR network. Nodes represent compounds and are colored by potency and edges indicate pair-wise similarity relationships.
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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 Bonn
- D-53113 Bonn
| | - Jürgen Bajorath
- Department of Life Science Informatics
- B-IT
- LIMES Program Unit Chemical Biology and Medicinal Chemistry
- Rheinische Friedrich-Wilhelms-Universität Bonn
- D-53113 Bonn
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Jin X, Awale M, Zasso M, Kostro D, Patiny L, Reymond JL. PDB-Explorer: a web-based interactive map of the protein data bank in shape space. BMC Bioinformatics 2015; 16:339. [PMID: 26493835 PMCID: PMC4619230 DOI: 10.1186/s12859-015-0776-9] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 10/14/2015] [Indexed: 11/17/2022] Open
Abstract
Background The RCSB Protein Data Bank (PDB) provides public access to experimentally determined 3D-structures of biological macromolecules (proteins, peptides and nucleic acids). While various tools are available to explore the PDB, options to access the global structural diversity of the entire PDB and to perceive relationships between PDB structures remain very limited. Methods A 136-dimensional atom pair 3D-fingerprint for proteins (3DP) counting categorized atom pairs at increasing through-space distances was designed to represent the molecular shape of PDB-entries. Nearest neighbor searches examples were reported exemplifying the ability of 3DP-similarity to identify closely related biomolecules from small peptides to enzyme and large multiprotein complexes such as virus particles. The principle component analysis was used to obtain the visualization of PDB in 3DP-space. Results The 3DP property space groups proteins and protein assemblies according to their 3D-shape similarity, yet shows exquisite ability to distinguish between closely related structures. An interactive website called PDB-Explorer is presented featuring a color-coded interactive map of PDB in 3DP-space. Each pixel of the map contains one or more PDB-entries which are directly visualized as ribbon diagrams when the pixel is selected. The PDB-Explorer website allows performing 3DP-nearest neighbor searches of any PDB-entry or of any structure uploaded as protein-type PDB file. All functionalities on the website are implemented in JavaScript in a platform-independent manner and draw data from a server that is updated daily with the latest PDB additions, ensuring complete and up-to-date coverage. The essentially instantaneous 3DP-similarity search with the PDB-Explorer provides results comparable to those of much slower 3D-alignment algorithms, and automatically clusters proteins from the same superfamilies in tight groups. Conclusion A chemical space classification of PDB based on molecular shape was obtained using a new atom-pair 3D-fingerprint for proteins and implemented in a web-based database exploration tool comprising an interactive color-coded map of the PDB chemical space and a nearest neighbor search tool. The PDB-Explorer website is freely available at www.cheminfo.org/pdbexplorer and represents an unprecedented opportunity to interactively visualize and explore the structural diversity of the PDB. ᅟ ᅟMaps of PDB in 3DP-space color-coded by heavy atom count and shape. ![]()
Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0776-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xian Jin
- Department of Chemistry and Biochemistry, University of Berne, Freiestrasse 3, 3012, Berne, Switzerland.
| | - Mahendra Awale
- Department of Chemistry and Biochemistry, University of Berne, Freiestrasse 3, 3012, Berne, Switzerland.
| | - Michaël Zasso
- Ecole Polytechnique Fédérale de Lausanne (EPFL), Institute of Chemical Sciences and Engineering (ISIC), Lausanne, 1015, Switzerland.
| | - Daniel Kostro
- Ecole Polytechnique Fédérale de Lausanne (EPFL), Institute of Chemical Sciences and Engineering (ISIC), Lausanne, 1015, Switzerland.
| | - Luc Patiny
- Ecole Polytechnique Fédérale de Lausanne (EPFL), Institute of Chemical Sciences and Engineering (ISIC), Lausanne, 1015, Switzerland.
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, University of Berne, Freiestrasse 3, 3012, Berne, Switzerland.
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