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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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Abstract
INTRODUCTION The popularity and success of advanced AI methods like deep neural networks has led to novel ways for exploring chemical space. Their opaque nature poses challenges for model evaluation regarding novelty, uniqueness, and distribution of the chemical space covered. However, these methods also promise to be able to explore uncharted chemical space in novel ways that do not rely directly on structural similarity. AREAS COVERED This review provides an overview of popular deep learning methods for chemical space exploration. Crucial aspects like choice of molecular representation, training for focused chemical space exploration, and criteria for assessing and validating chemical space coverage are discussed. EXPERT OPINION Deep learning offers great potential for chemical space exploration beyond conventional fragment-based methods. Given the rarity of prospective applications and considering the difficulty in assessing representativeness and comprehensiveness of chemical space covered, developing criteria for assessing and validating generative models is of great significance. Latent space models like variational autoencoders are conceptually appealing for inverse QSAR/QSPR approaches as neighborhood relationships in latent space can be trained to reflect property similarities. Future research in understanding and interpreting generative models might lead to a better understanding of biologically relevant properties of molecules.
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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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3
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Wang Q, Zhou Y, Huang J, Huang N. Structure, Function, and Pharmaceutical Ligands of 5-Hydroxytryptamine 2B Receptor. Pharmaceuticals (Basel) 2021; 14:76. [PMID: 33498477 PMCID: PMC7909583 DOI: 10.3390/ph14020076] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 01/18/2021] [Accepted: 01/19/2021] [Indexed: 12/13/2022] Open
Abstract
Since the first characterization of the 5-hydroxytryptamine 2B receptor (5-HT2BR) in 1992, significant progress has been made in 5-HT2BR research. Herein, we summarize the biological function, structure, and small-molecule pharmaceutical ligands of the 5-HT2BR. Emerging evidence has suggested that the 5-HT2BR is implicated in the regulation of the cardiovascular system, fibrosis disorders, cancer, the gastrointestinal (GI) tract, and the nervous system. Eight crystal complex structures of the 5-HT2BR bound with different ligands provided great insights into ligand recognition, activation mechanism, and biased signaling. Numerous 5-HT2BR antagonists have been discovered and developed, and several of them have advanced to clinical trials. It is expected that the novel 5-HT2BR antagonists with high potency and selectivity will lead to the development of first-in-class drugs in various therapeutic areas.
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Affiliation(s)
- Qing Wang
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, China; (Q.W.); (J.H.)
- National Institute of Biological Sciences, No. 7 Science Park Road, Zhongguancun Life Science Park, Beijing 102206, China;
| | - Yu Zhou
- National Institute of Biological Sciences, No. 7 Science Park Road, Zhongguancun Life Science Park, Beijing 102206, China;
| | - Jianhui Huang
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, China; (Q.W.); (J.H.)
| | - Niu Huang
- National Institute of Biological Sciences, No. 7 Science Park Road, Zhongguancun Life Science Park, Beijing 102206, China;
- Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua University, Beijing 102206, China
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4
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Vanhaelen Q, Lin YC, Zhavoronkov A. The Advent of Generative Chemistry. ACS Med Chem Lett 2020; 11:1496-1505. [PMID: 32832015 PMCID: PMC7429972 DOI: 10.1021/acsmedchemlett.0c00088] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 07/14/2020] [Indexed: 12/12/2022] Open
Abstract
Generative adversarial networks (GANs), first published in 2014, are among the most important concepts in modern artificial intelligence (AI). Bridging deep learning and game theory, GANs are used to generate or "imagine" new objects with desired properties. Since 2016, multiple GANs with reinforcement learning (RL) have been successfully applied in pharmacology for de novo molecular design. Those techniques aim at a more efficient use of the data and a better exploration of the chemical space. We review recent advances for the generation of novel molecules with desired properties with a focus on the applications of GANs, RL, and related techniques. We also discuss the current limitations and challenges in the new growing field of generative chemistry.
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Affiliation(s)
- Quentin Vanhaelen
- Insilico
Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| | - Yen-Chu Lin
- Insilico
Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
- Insilico
Taiwan, Taipei City 115, Taiwan, R.O.C
| | - Alex Zhavoronkov
- Insilico
Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
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5
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Muraoka K, Chaikittisilp W, Okubo T. Multi-objective de novo molecular design of organic structure-directing agents for zeolites using nature-inspired ant colony optimization. Chem Sci 2020; 11:8214-8223. [PMID: 34094176 PMCID: PMC8163217 DOI: 10.1039/d0sc03075a] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Organic structure-directing agents (OSDAs) are often employed for synthesis of zeolites with desired frameworks. A priori prediction of such OSDAs has mainly relied on the interaction energies between OSDAs and zeolite frameworks, without cost considerations. For practical purposes, the cost of OSDAs becomes a critical issue. Therefore, the development of a computational de novo prediction methodology that can speed up the trial-and-error cycle in the search for less expensive OSDAs is desired. This study utilized a nature-inspired ant colony optimization method to predict physicochemically and/or economically preferable OSDAs, while also taking molecular similarity and heuristics of zeolite synthesis into consideration. The prediction results included experimentally known OSDAs, candidates having structures closely related to known OSDAs, and novel ones, suggesting the applicability of this approach. Inspired by the exploratory methods of ant colonies, adaptive optimization was employed to explore the chemical space for organic molecules that guide zeolite crystallization, giving both physicochemically and economically promising molecules.![]()
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Affiliation(s)
- Koki Muraoka
- Department of Chemical System Engineering, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku Tokyo 113-8656 Japan
| | - Watcharop Chaikittisilp
- Department of Chemical System Engineering, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku Tokyo 113-8656 Japan
| | - Tatsuya Okubo
- Department of Chemical System Engineering, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku Tokyo 113-8656 Japan
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6
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Schneider P, Walters WP, Plowright AT, Sieroka N, Listgarten J, Goodnow RA, Fisher J, Jansen JM, Duca JS, Rush TS, Zentgraf M, Hill JE, Krutoholow E, Kohler M, Blaney J, Funatsu K, Luebkemann C, Schneider G. Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov 2019. [DOI: 78495111110.1038/s41573-019-0050-3' target='_blank'>'"<>78495111110.1038/s41573-019-0050-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [78495111110.1038/s41573-019-0050-3','', '10.1002/anie.201410201')">Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2022]
78495111110.1038/s41573-019-0050-3" />
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7
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Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov 2019; 19:353-364. [DOI: 10.1038/s41573-019-0050-3] [Citation(s) in RCA: 222] [Impact Index Per Article: 44.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/28/2019] [Indexed: 12/17/2022]
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8
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Machine learning for target discovery in drug development. Curr Opin Chem Biol 2019; 56:16-22. [PMID: 31734566 DOI: 10.1016/j.cbpa.2019.10.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Revised: 10/01/2019] [Accepted: 10/03/2019] [Indexed: 12/15/2022]
Abstract
The discovery of macromolecular targets for bioactive agents is currently a bottleneck for the informed design of chemical probes and drug leads. Typically, activity profiling against genetically manipulated cell lines or chemical proteomics is pursued to shed light on their biology and deconvolute drug-target networks. By taking advantage of the ever-growing wealth of publicly available bioactivity data, learning algorithms now provide an attractive means to generate statistically motivated research hypotheses and thereby prioritize biochemical screens. Here, we highlight recent successes in machine intelligence for target identification and discuss challenges and opportunities for drug discovery.
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9
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de Almeida AF, Moreira R, Rodrigues T. Synthetic organic chemistry driven by artificial intelligence. Nat Rev Chem 2019. [DOI: 10.1038/s41570-019-0124-0] [Citation(s) in RCA: 111] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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10
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Reker D, Bernardes GJL, Rodrigues T. Computational advances in combating colloidal aggregation in drug discovery. Nat Chem 2019; 11:402-418. [PMID: 30988417 DOI: 10.1038/s41557-019-0234-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 02/21/2019] [Indexed: 02/07/2023]
Abstract
Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assay interference compounds may divert lead optimization and screening programmes towards attrition-prone chemical matter. Colloidal aggregates are the prime source of false positive readouts, either through protein sequestration or protein-scaffold mimicry. Nevertheless, assessment of colloidal aggregation remains somewhat overlooked and under-appreciated. In this Review, we discuss the impact of aggregation in drug discovery by analysing select examples from the literature and publicly-available datasets. We also examine and comment on technologies used to experimentally identify these potentially problematic entities. We focus on evidence-based computational filters and machine learning algorithms that may be swiftly deployed to flag chemical matter and mitigate the impact of aggregates in discovery programmes. We highlight the tools that can be used to scrutinize libraries, and identify and eliminate these problematic compounds.
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Affiliation(s)
- Daniel Reker
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. .,MIT-IBM Watson AI Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Gonçalo J L Bernardes
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, UK.,Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
| | - Tiago Rodrigues
- Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal.
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11
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A Toolbox for the Identification of Modes of Action of Natural Products. PROGRESS IN THE CHEMISTRY OF ORGANIC NATURAL PRODUCTS 110 2019; 110:73-97. [DOI: 10.1007/978-3-030-14632-0_3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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12
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Nosova EV, Lipunova GN, Charushin VN, Chupakhin ON. Fluorine-containing indoles: Synthesis and biological activity. J Fluor Chem 2018. [DOI: 10.1016/j.jfluchem.2018.05.012] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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13
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Rodrigues T. Harnessing the potential of natural products in drug discovery from a cheminformatics vantage point. Org Biomol Chem 2018; 15:9275-9282. [PMID: 29085945 DOI: 10.1039/c7ob02193c] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Natural products (NPs) present a privileged source of inspiration for chemical probe and drug design. Despite the biological pre-validation of the underlying molecular architectures and their relevance in drug discovery, the poor accessibility to NPs, complexity of the synthetic routes and scarce knowledge of their macromolecular counterparts in phenotypic screens still hinder their broader exploration. Cheminformatics algorithms now provide a powerful means of circumventing the abovementioned challenges and unlocking the full potential of NPs in a drug discovery context. Herein, I discuss recent advances in the computer-assisted design of NP mimics and how artificial intelligence may accelerate future NP-inspired molecular medicine.
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Affiliation(s)
- Tiago Rodrigues
- Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal.
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14
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Basith S, Cui M, Macalino SJY, Park J, Clavio NAB, Kang S, Choi S. Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design. Front Pharmacol 2018; 9:128. [PMID: 29593527 PMCID: PMC5854945 DOI: 10.3389/fphar.2018.00128] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 02/06/2018] [Indexed: 01/14/2023] Open
Abstract
The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.
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Affiliation(s)
| | | | | | | | | | - Soosung Kang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
| | - Sun Choi
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
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15
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Rodríguez-Espigares I, Kaczor AA, Stepniewski TM, Selent J. Challenges and Opportunities in Drug Discovery of Biased Ligands. Methods Mol Biol 2018; 1705:321-334. [PMID: 29188569 DOI: 10.1007/978-1-4939-7465-8_14] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The observation of biased agonism in G protein-coupled receptors (GPCRs) has provided new approaches for the development of more efficacious and safer drugs. However, in order to rationally design biased drugs, one must understand the molecular basis of this phenomenon. Computational approaches can help in exploring the conformational universe of GPCRs and detecting conformational states with relevance for distinct functional outcomes. This information is extremely valuable for the development of new therapeutic agents that promote desired conformational receptor states and responses while avoiding the ones leading to undesired side-effects.This book chapter intends to introduce the reader to powerful computational approaches for sampling the conformational space of these receptors, focusing first on molecular dynamics and the analysis of the produced data through methods such as dimensionality reduction, Markov State Models and adaptive sampling. Then, we show how to seek for compounds that target distinct conformational states via docking and virtual screening. In addition, we describe how to detect receptor-ligand interactions that drive signaling bias and comment current challenges and opportunities of presented methods.
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Affiliation(s)
- Ismael Rodríguez-Espigares
- Department of Experimental and Health Sciences, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Dr. Aiguader 88, E-08003, Barcelona, Spain
| | - Agnieszka A Kaczor
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Lab, Faculty of Pharmacy with Division of Medical Analytics, Medical University of Lublin, 4A Chodzki St., PL-20093, Lublin, Poland.,Department of Pharmaceutical Chemistry, School of Pharmacy, University of Eastern Finland, Yliopistonranta 1, P.O. Box 1627, FI-70211, Kuopio, Finland
| | - Tomasz Maciej Stepniewski
- Department of Experimental and Health Sciences, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Dr. Aiguader 88, E-08003, Barcelona, Spain
| | - Jana Selent
- Department of Experimental and Health Sciences, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Dr. Aiguader 88, E-08003, Barcelona, Spain.
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16
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Abstract
Small-molecule drug discovery can be viewed as a challenging multidimensional problem in which various characteristics of compounds - including efficacy, pharmacokinetics and safety - need to be optimized in parallel to provide drug candidates. Recent advances in areas such as microfluidics-assisted chemical synthesis and biological testing, as well as artificial intelligence systems that improve a design hypothesis through feedback analysis, are now providing a basis for the introduction of greater automation into aspects of this process. This could potentially accelerate time frames for compound discovery and optimization and enable more effective searches of chemical space. However, such approaches also raise considerable conceptual, technical and organizational challenges, as well as scepticism about the current hype around them. This article aims to identify the approaches and technologies that could be implemented robustly by medicinal chemists in the near future and to critically analyse the opportunities and challenges for their more widespread application.
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Abstract
Designing drugs that can simultaneously interact with multiple targets is a promising approach for treating complicated diseases. Compared to using combinations of single target drugs, multitarget drugs have advantages of higher efficacy, improved safety profile, and simpler administration. Many in silico methods have been developed to approach different aspects of this polypharmacology-guided drug design, particularly for drug repurposing and multitarget drug design. In this review, we summarize recent progress in computational multitarget drug design and discuss their advantages and limitations. Perspectives for future drug development will also be discussed.
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Affiliation(s)
- Weilin Zhang
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies (AAIS), Peking University , Beijing 100871, People's Republic of China
| | - Jianfeng Pei
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies (AAIS), Peking University , Beijing 100871, People's Republic of China
| | - Luhua Lai
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies (AAIS), Peking University , Beijing 100871, People's Republic of China.,Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies (AAIS), Peking University , Beijing 100871, People's Republic of China.,BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University , Beijing 100871, People's Republic of China
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18
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Hwang YJ, Coley CW, Abolhasani M, Marzinzik AL, Koch G, Spanka C, Lehmann H, Jensen KF. A segmented flow platform for on-demand medicinal chemistry and compound synthesis in oscillating droplets. Chem Commun (Camb) 2017; 53:6649-6652. [DOI: 10.1039/c7cc03584e] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
An automated flow chemistry platform performs single/multi-phase and single/multi-step chemistries in 14 μL droplets with online analysis and product collection.
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Affiliation(s)
- Ye-Jin Hwang
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
- Department of Chemical Engineering
| | - Connor W. Coley
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
| | - Milad Abolhasani
- Department of Chemical and Biomolecular Engineering
- North Carolina State University
- Raleigh
- USA
| | | | - Guido Koch
- Novartis Institutes for BioMedical Research
- CH-4056 Basel
- Switzerland
| | - Carsten Spanka
- Novartis Institutes for BioMedical Research
- CH-4056 Basel
- Switzerland
| | | | - Klavs F. Jensen
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
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19
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Schneider G, Schneider P. Macromolecular target prediction by self-organizing feature maps. Expert Opin Drug Discov 2016; 12:271-277. [DOI: 10.1080/17460441.2017.1274727] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Gisbert Schneider
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
| | - Petra Schneider
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
- inSili.com LLC, Zurich, Switzerland
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20
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Tosh DK, Ciancetta A, Warnick E, Crane S, Gao ZG, Jacobson KA. Structure-Based Scaffold Repurposing for G Protein-Coupled Receptors: Transformation of Adenosine Derivatives into 5HT 2B/5HT 2C Serotonin Receptor Antagonists. J Med Chem 2016; 59:11006-11026. [PMID: 27933810 DOI: 10.1021/acs.jmedchem.6b01183] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Adenosine derivatives developed to activate adenosine receptors (ARs) revealed micromolar activity at serotonin 5HT2B and 5HT2C receptors (5HTRs). We explored the structure-activity relationship at 5HT2Rs and modeled receptor interactions in order to optimize affinity and simultaneously reduce AR affinity. Depending on N6 substitution, small 5'-alkylamide modification maintained 5HT2BR affinity, which was enhanced upon ribose substitution with rigid bicyclo[3.1.0]hexane (North (N)-methanocarba), e.g., N6-dicyclopropylmethyl 4'-CH2OH derivative 14 (Ki 11 nM). 5'-Methylamide 23 was 170-fold selective as antagonist for 5HT2BR vs 5HT2CR. 5'-Methyl 25 and ethyl 26 esters potently antagonized 5HT2Rs with moderate selectivity in comparison to ARs; related 6-N,N-dimethylamino analogue 30 was 5HT2R-selective. 5' position flexibility of substitution was indicated in 5HT2BR docking. Both 5'-ester and 5'-amide derivatives displayed in vivo t1/2 of 3-4 h. Thus, we used G protein-coupled receptor modeling to repurpose nucleoside scaffolds in favor of binding at nonpurine receptors as novel 5HT2R antagonists, with potential for cardioprotection, liver protection, or central nervous system activity.
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Affiliation(s)
- Dilip K Tosh
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health , Bethesda, Maryland 20892, United States
| | - Antonella Ciancetta
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health , Bethesda, Maryland 20892, United States
| | - Eugene Warnick
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health , Bethesda, Maryland 20892, United States
| | - Steven Crane
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health , Bethesda, Maryland 20892, United States
| | - Zhan-Guo Gao
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health , Bethesda, Maryland 20892, United States
| | - Kenneth A Jacobson
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health , Bethesda, Maryland 20892, United States
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21
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Button AL, Hiss JA, Schneider P, Schneider G. Scoring of de novo Designed Chemical Entities by Macromolecular Target Prediction. Mol Inform 2016; 36. [PMID: 27643811 DOI: 10.1002/minf.201600110] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 08/27/2016] [Indexed: 11/10/2022]
Abstract
Computational de novo molecular design and macromolecular target prediction have become routine in applied cheminformatics. In this study, we have generated populations of drug template-derived designs using ligand-based building block assembly, and predicted their potential targets. The results of our analysis show that the reaction-based de novo design generated new chemical entities with similar properties and pharmacophores as that of the template drugs as well as up to 44 % of the de novo compounds receiving the correct target predictions. Keeping in mind the probabilistic nature of the methods, such a combination of fast and meaningful computational structure generation by reaction-based design and product scoring by target class prediction may be appropriate for prospective application in medicinal chemistry.
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Affiliation(s)
- Alexander L Button
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-, 8093, Zurich, Switzerland
| | - Jan A Hiss
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-, 8093, Zurich, Switzerland
| | - Petra Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-, 8093, Zurich, Switzerland.,inSili.com LLC, Segantinisteig 3, CH-, 8049, Zurich, Switzerland
| | - Gisbert Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-, 8093, Zurich, Switzerland
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Reker D, Schneider P, Schneider G. Multi-objective active machine learning rapidly improves structure-activity models and reveals new protein-protein interaction inhibitors. Chem Sci 2016; 7:3919-3927. [PMID: 30155037 PMCID: PMC6013791 DOI: 10.1039/c5sc04272k] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Accepted: 02/27/2016] [Indexed: 11/21/2022] Open
Abstract
Active machine learning puts artificial intelligence in charge of a sequential, feedback-driven discovery process. We present the application of a multi-objective active learning scheme for identifying small molecules that inhibit the protein-protein interaction between the anti-cancer target CXC chemokine receptor 4 (CXCR4) and its endogenous ligand CXCL-12 (SDF-1). Experimental design by active learning was used to retrieve informative active compounds that continuously improved the adaptive structure-activity model. The balanced character of the compound selection function rapidly delivered new molecular structures with the desired inhibitory activity and at the same time allowed us to focus on informative compounds for model adjustment. The results of our study validate active learning for prospective ligand finding by adaptive, focused screening of large compound repositories and virtual compound libraries.
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Affiliation(s)
- D Reker
- Department of Chemistry and Applied Biosciences , ETH Zürich , Vladimir-Prelog Weg 4 , 8093 Zürich , Switzerland .
| | - P Schneider
- Department of Chemistry and Applied Biosciences , ETH Zürich , Vladimir-Prelog Weg 4 , 8093 Zürich , Switzerland .
| | - G Schneider
- Department of Chemistry and Applied Biosciences , ETH Zürich , Vladimir-Prelog Weg 4 , 8093 Zürich , Switzerland .
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23
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Drug combination therapy increases successful drug repositioning. Drug Discov Today 2016; 21:1189-95. [PMID: 27240777 DOI: 10.1016/j.drudis.2016.05.015] [Citation(s) in RCA: 213] [Impact Index Per Article: 26.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Revised: 04/28/2016] [Accepted: 05/23/2016] [Indexed: 11/21/2022]
Abstract
Repositioning of approved drugs has recently gained new momentum for rapid identification and development of new therapeutics for diseases that lack effective drug treatment. Reported repurposing screens have increased dramatically in number in the past five years. However, many newly identified compounds have low potency; this limits their immediate clinical applications because the known, tolerated plasma drug concentrations are lower than the required therapeutic drug concentrations. Drug combinations of two or more compounds with different mechanisms of action are an alternative approach to increase the success rate of drug repositioning.
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Rodríguez-Espigares I, Kaczor AA, Selent J. In silico Exploration of the Conformational Universe of GPCRs. Mol Inform 2016; 35:227-37. [PMID: 27492237 DOI: 10.1002/minf.201600012] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Accepted: 04/14/2016] [Indexed: 12/17/2022]
Abstract
The structural plasticity of G protein coupled receptors (GPCRs) leads to a conformational universe going from inactive to active receptor states with several intermediate states. Many of them have not been captured yet and their role for GPCR activation is not well understood. The study of this conformational space and the transition dynamics between different receptor populations is a major challenge in molecular biophysics. The rational design of effector molecules that target such receptor populations allows fine-tuning receptor signalling with higher specificity to produce drugs with safer therapeutic profiles. In this minireview, we outline highly conserved receptor regions which are considered determinant for the establishment of distinct receptor states. We then discuss in-silico approaches such as dimensionality reduction methods and Markov State Models to explore the GPCR conformational universe and exploit the obtained conformations through structure-based drug design.
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Affiliation(s)
- Ismael Rodríguez-Espigares
- Pharmacoinformatics group, Research Programme on Biomedical Informatics (GRIB), Universitat Pompeu Fabra (UPF)-Hospital del Mar Medical Research Institute (IMIM), Parc de Recerca Biomèdica de Barcelona (PRBB), Dr. Aiguader, 88, 08003, Barcelona, Spain
| | - Agnieszka A Kaczor
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modeling Lab, Faculty of Pharmacy with Division for Medical Analytics, Medical University of Lublin, 4A Chodźki St., PL-20059, Lublin, Poland.,School of Pharmacy, University of Eastern Finland, Yliopistonranta 1, P.O. Box 1627, FI-70211, Kuopio, Finland
| | - Jana Selent
- Pharmacoinformatics group, Research Programme on Biomedical Informatics (GRIB), Universitat Pompeu Fabra (UPF)-Hospital del Mar Medical Research Institute (IMIM), Parc de Recerca Biomèdica de Barcelona (PRBB), Dr. Aiguader, 88, 08003, Barcelona, Spain.
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25
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Kaneko H, Funatsu K. Applicability Domains and Consistent Structure Generation. Mol Inform 2016; 36. [DOI: 10.1002/minf.201600032] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Accepted: 04/25/2016] [Indexed: 11/08/2022]
Affiliation(s)
- Hiromasa Kaneko
- Department of Chemical System Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku, Tokyo 113-8656 Japan
| | - Kimito Funatsu
- Department of Chemical System Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku, Tokyo 113-8656 Japan
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26
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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
Computational medicinal chemistry offers viable strategies for finding, characterizing, and optimizing innovative pharmacologically active compounds. Technological advances in both computer hardware and software as well as biological chemistry have enabled a renaissance of computer-assisted "de novo" design of molecules with desired pharmacological properties. Here, we present our current perspective on the concept of automated molecule generation by highlighting chemocentric methods that may capture druglike chemical space, consider ligand promiscuity for hit and lead finding, and provide fresh ideas for the rational design of customized screening of compound libraries.
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Affiliation(s)
- Petra Schneider
- Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology (ETH) , Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland.,inSili.com LLC , Segantinisteig 3, 8049 Zürich, Switzerland
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology (ETH) , Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland
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28
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Rodrigues T, Reker D, Welin M, Caldera M, Brunner C, Gabernet G, Schneider P, Walse B, Schneider G. De Novo Fragment Design for Drug Discovery and Chemical Biology. Angew Chem Int Ed Engl 2015; 54:15079-83. [DOI: 10.1002/anie.201508055] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Indexed: 01/08/2023]
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Rodrigues T, Reker D, Welin M, Caldera M, Brunner C, Gabernet G, Schneider P, Walse B, Schneider G. De-novo-Fragmententwurf für die Wirkstoffforschung und chemische Biologie. Angew Chem Int Ed Engl 2015. [DOI: 10.1002/ange.201508055] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Awale M, Reymond JL. Similarity Mapplet: Interactive Visualization of the Directory of Useful Decoys and ChEMBL in High Dimensional Chemical Spaces. J Chem Inf Model 2015. [PMID: 26207526 DOI: 10.1021/acs.jcim.5b00182] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
An Internet portal accessible at www.gdb.unibe.ch has been set up to automatically generate color-coded similarity maps of the ChEMBL database in relation to up to two sets of active compounds taken from the enhanced Directory of Useful Decoys (eDUD), a random set of molecules, or up to two sets of user-defined reference molecules. These maps visualize the relationships between the selected compounds and ChEMBL in six different high dimensional chemical spaces, namely MQN (42-D molecular quantum numbers), SMIfp (34-D SMILES fingerprint), APfp (20-D shape fingerprint), Xfp (55-D pharmacophore fingerprint), Sfp (1024-bit substructure fingerprint), and ECfp4 (1024-bit extended connectivity fingerprint). The maps are supplied in form of Java based desktop applications called "similarity mapplets" allowing interactive content browsing and linked to a "Multifingerprint Browser for ChEMBL" (also accessible directly at www.gdb.unibe.ch ) to perform nearest neighbor searches. One can obtain six similarity mapplets of ChEMBL relative to random reference compounds, 606 similarity mapplets relative to single eDUD active sets, 30,300 similarity mapplets relative to pairs of eDUD active sets, and any number of similarity mapplets relative to user-defined reference sets to help visualize the structural diversity of compound series in drug optimization projects and their relationship to other known bioactive compounds.
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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
| | - 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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Rastelli G, Pinzi L. Computational polypharmacology comes of age. Front Pharmacol 2015; 6:157. [PMID: 26283966 PMCID: PMC4516879 DOI: 10.3389/fphar.2015.00157] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Accepted: 07/14/2015] [Indexed: 12/18/2022] Open
Affiliation(s)
- Giulio Rastelli
- Molecular Modelling and Drug Design Lab, Life Sciences Department, University of Modena and Reggio Emilia Modena, Italy
| | - Luca Pinzi
- Molecular Modelling and Drug Design Lab, Life Sciences Department, University of Modena and Reggio Emilia Modena, Italy
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Rodrigues T, Reker D, Kunze J, Schneider P, Schneider G. Revealing the Macromolecular Targets of Fragment-Like Natural Products. Angew Chem Int Ed Engl 2015. [DOI: 10.1002/anie.201504241] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Rodrigues T, Reker D, Kunze J, Schneider P, Schneider G. Revealing the Macromolecular Targets of Fragment-Like Natural Products. Angew Chem Int Ed Engl 2015. [DOI: 10.1002/ange.201504241] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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34
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Structure versus function—The impact of computational methods on the discovery of specific GPCR–ligands. Bioorg Med Chem 2015; 23:3907-12. [DOI: 10.1016/j.bmc.2015.03.026] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Revised: 03/06/2015] [Accepted: 03/09/2015] [Indexed: 12/26/2022]
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35
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Perna AM, Rodrigues T, Schmidt TP, Böhm M, Stutz K, Reker D, Pfeiffer B, Altmann KH, Backert S, Wessler S, Schneider G. Fragment-Based De Novo Design Reveals a Small-Molecule Inhibitor ofHelicobacter PyloriHtrA. Angew Chem Int Ed Engl 2015. [DOI: 10.1002/ange.201504035] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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36
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Perna AM, Rodrigues T, Schmidt TP, Böhm M, Stutz K, Reker D, Pfeiffer B, Altmann KH, Backert S, Wessler S, Schneider G. Fragment-Based De Novo Design Reveals a Small-Molecule Inhibitor of Helicobacter Pylori HtrA. Angew Chem Int Ed Engl 2015; 54:10244-8. [PMID: 26069090 DOI: 10.1002/anie.201504035] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2015] [Indexed: 01/22/2023]
Abstract
Sustained identification of innovative chemical entities is key for the success of chemical biology and drug discovery. We report the fragment-based, computer-assisted de novo design of a small molecule inhibiting Helicobacter pylori HtrA protease. Molecular binding of the designed compound to HtrA was confirmed through biophysical methods, supporting its functional activity in vitro. Hit expansion led to the identification of the currently best-in-class HtrA inhibitor. The results obtained reinforce the validity of ligand-based de novo design and binding-kinetics-guided optimization for the efficient discovery of pioneering lead structures and prototyping drug-like chemical probes with tailored bioactivity.
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Affiliation(s)
- Anna M Perna
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zürich (Switzerland)
| | - Tiago Rodrigues
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zürich (Switzerland)
| | | | - Manja Böhm
- Department of Biology, Universität Erlangen-Nürnberg (Germany)
| | - Katharina Stutz
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zürich (Switzerland)
| | - Daniel Reker
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zürich (Switzerland)
| | - Bernhard Pfeiffer
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zürich (Switzerland)
| | - Karl-Heinz Altmann
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zürich (Switzerland)
| | - Steffen Backert
- Department of Biology, Universität Erlangen-Nürnberg (Germany)
| | - Silja Wessler
- Department of Molecular Biology, Universität Salzburg (Austria)
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zürich (Switzerland).
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37
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Rodrigues T, Lin YC, Hartenfeller M, Renner S, Lim YF, Schneider G. Repurposing de novo designed entities reveals phosphodiesterase 3B and cathepsin L modulators. Chem Commun (Camb) 2015; 51:7478-81. [DOI: 10.1039/c5cc01376c] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Scaffold hopping: a computational algorithm correctly predicted the macromolecular target ofde novogenerated small molecular entities.
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Affiliation(s)
- Tiago Rodrigues
- Swiss Federal Institute of Technology (ETH)
- Department of Chemistry and Applied Biosciences
- 8093 Zürich
- Switzerland
| | - Yen-Chu Lin
- Swiss Federal Institute of Technology (ETH)
- Department of Chemistry and Applied Biosciences
- 8093 Zürich
- Switzerland
| | - Markus Hartenfeller
- Swiss Federal Institute of Technology (ETH)
- Department of Chemistry and Applied Biosciences
- 8093 Zürich
- Switzerland
- Novartis Pharma AG
| | | | - Yi Fan Lim
- Swiss Federal Institute of Technology (ETH)
- Department of Chemistry and Applied Biosciences
- 8093 Zürich
- Switzerland
| | - Gisbert Schneider
- Swiss Federal Institute of Technology (ETH)
- Department of Chemistry and Applied Biosciences
- 8093 Zürich
- Switzerland
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