1
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Hoffer L, Charifi-Hoareau G, Barelier S, Betzi S, Miller T, Morelli X, Roche P. ChemoDOTS: a web server to design chemistry-driven focused libraries. Nucleic Acids Res 2024; 52:W461-W468. [PMID: 38686808 PMCID: PMC11223810 DOI: 10.1093/nar/gkae326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/08/2024] [Accepted: 04/16/2024] [Indexed: 05/02/2024] Open
Abstract
In drug discovery, the successful optimization of an initial hit compound into a lead molecule requires multiple cycles of chemical modification. Consequently, there is a need to efficiently generate synthesizable chemical libraries to navigate the chemical space surrounding the primary hit. To address this need, we introduce ChemoDOTS, an easy-to-use web server for hit-to-lead chemical optimization freely available at https://chemodots.marseille.inserm.fr/. With this tool, users enter an activated form of the initial hit molecule then choose from automatically detected reactive functions. The server proposes compatible chemical transformations via an ensemble of encoded chemical reactions widely used in the pharmaceutical industry during hit-to-lead optimization. After selection of the desired reactions, all compatible chemical building blocks are automatically coupled to the initial hit to generate a raw chemical library. Post-processing filters can be applied to extract a subset of compounds with specific physicochemical properties. Finally, explicit stereoisomers and tautomers are computed, and a 3D conformer is generated for each molecule. The resulting virtual library is compatible with most docking software for virtual screening campaigns. ChemoDOTS rapidly generates synthetically feasible, hit-focused, large, diverse chemical libraries with finely-tuned physicochemical properties via a user-friendly interface providing a powerful resource for researchers engaged in hit-to-lead optimization.
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Affiliation(s)
- Laurent Hoffer
- CRCM, CNRS, Inserm, Institut Paoli-Calmettes, Aix-Marseille Univ, Marseille 13273, France
| | | | - Sarah Barelier
- CRCM, CNRS, Inserm, Institut Paoli-Calmettes, Aix-Marseille Univ, Marseille 13273, France
| | - Stéphane Betzi
- CRCM, CNRS, Inserm, Institut Paoli-Calmettes, Aix-Marseille Univ, Marseille 13273, France
| | - Thomas Miller
- CRCM, CNRS, Inserm, Institut Paoli-Calmettes, Aix-Marseille Univ, Marseille 13273, France
| | - Xavier Morelli
- CRCM, CNRS, Inserm, Institut Paoli-Calmettes, Aix-Marseille Univ, Marseille 13273, France
| | - Philippe Roche
- CRCM, CNRS, Inserm, Institut Paoli-Calmettes, Aix-Marseille Univ, Marseille 13273, France
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2
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Zhang Y, Zhang Z, Ke D, Pan X, Wang X, Xiao X, Ji C. FragGrow: A Web Server for Structure-Based Drug Design by Fragment Growing within Constraints. J Chem Inf Model 2024; 64:3970-3976. [PMID: 38725251 DOI: 10.1021/acs.jcim.4c00154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2024]
Abstract
Fragment growing is an important ligand design strategy in drug discovery. In this study, we present FragGrow, a web server that facilitates structure-based drug design by fragment growing. FragGrow offers two working modes: one for growing molecules through the direct replacement of hydrogen atoms or substructures and the other for growing via virtual synthesis. FragGrow works by searching for suitable fragments that meet a set of constraints from an indexed 3D fragment database and using them to create new compounds in 3D space. The users can set a range of constraints when searching for their desired fragment, including the fragment's ability to interact with specific protein sites; its size, topology, and physicochemical properties; and the presence of particular heteroatoms and functional groups within the fragment. We hope that FragGrow will serve as a useful tool for medicinal chemists in ligand design. The FragGrow server is freely available to researchers and can be accessed at https://fraggrow.xundrug.cn.
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Affiliation(s)
- Yueqing Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China
| | - Zhihan Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China
| | - Dongliang Ke
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China
| | - Xiaolin Pan
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China
| | - Xingyu Wang
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China
| | - Xudong Xiao
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China
| | - Changge Ji
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China
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3
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Sindt F, Seyller A, Eguida M, Rognan D. Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces. ACS CENTRAL SCIENCE 2024; 10:615-627. [PMID: 38559302 PMCID: PMC10979501 DOI: 10.1021/acscentsci.3c01521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/25/2024] [Accepted: 01/29/2024] [Indexed: 04/04/2024]
Abstract
Ultralarge chemical spaces describing several billion compounds are revolutionizing hit identification in early drug discovery. Because of their size, such chemical spaces cannot be fully enumerated and require ad-hoc computational tools to navigate them and pick potentially interesting hits. We here propose a structure-based approach to ultralarge chemical space screening in which commercial chemical reagents are first docked to the target of interest and then directly connected according to organic chemistry and topological rules, to enumerate drug-like compounds under three-dimensional constraints of the target. When applied to bespoke chemical spaces of different sizes and chemical complexity targeting two receptors of pharmaceutical interest (estrogen β receptor, dopamine D3 receptor), the computational method was able to quickly enumerate hits that were either known ligands (or very close analogs) of targeted receptors as well as chemically novel candidates that could be experimentally confirmed by in vitro binding assays. The proposed approach is generic, can be applied to any docking algorithm, and requires few computational resources to prioritize easily synthesizable hits from billion-sized chemical spaces.
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Affiliation(s)
- François Sindt
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
| | - Anthony Seyller
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
| | | | - Didier Rognan
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
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4
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Tang Y, Moretti R, Meiler J. Recent Advances in Automated Structure-Based De Novo Drug Design. J Chem Inf Model 2024; 64:1794-1805. [PMID: 38485516 PMCID: PMC10966644 DOI: 10.1021/acs.jcim.4c00247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 02/26/2024] [Accepted: 02/29/2024] [Indexed: 03/26/2024]
Abstract
As the number of determined and predicted protein structures and the size of druglike 'make-on-demand' libraries soar, the time-consuming nature of structure-based computer-aided drug design calls for innovative computational algorithms. De novo drug design introduces in silico heuristics to accelerate searching in the vast chemical space. This review focuses on recent advances in structure-based de novo drug design, ranging from conventional fragment-based methods, evolutionary algorithms, and Metropolis Monte Carlo methods to deep generative models. Due to the historical limitation of de novo drug design generating readily available drug-like molecules, we highlight the synthetic accessibility efforts in each category and the benchmarking strategies taken to validate the proposed framework.
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Affiliation(s)
- Yidan Tang
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Rocco Moretti
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240, United States
| | - Jens Meiler
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240, United States
- Institute
of Drug Discovery, Faculty of Medicine, University of Leipzig, 04103 Leipzig, Germany
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5
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Dolfus U, Briem H, Gutermuth T, Rarey M. Full Modification Control over Retrosynthetic Routes for Guided Optimization of Lead Structures. J Chem Inf Model 2023; 63:6587-6597. [PMID: 37910814 DOI: 10.1021/acs.jcim.3c01155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
Synthesizability is essential for compounds designed in silico. Regardless, synthetic accessibility is often considered only as an afterthought in the design and optimization process. In addition, the trend with modern computer-aided drug design methods is going toward full automation and away from the possibility of incorporating user knowledge. With this work, we present the second major release of our software tool, Synthesia, for synthesis-aware lead structure modification, where the user's expertise is now fully utilized. A provided retrosynthetic route is used as a pathway to guide structural modifications that introduce desired structural changes in the target compound. Moreover, the approach allows the user to define the exact position or component in the retrosynthetic route, which should be modified, further integrating the user's expert knowledge. This paper describes the functionality of Synthesia, its basic concepts, and several application scenarios ranging from simple examples to a comparison of the effects of the different exchange functions to an analysis of a set of bioisosteric linker structures, highlighting potential synthetically feasible replacements.
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Affiliation(s)
- Uschi Dolfus
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraβe 43, 20146 Hamburg, Germany
| | - Hans Briem
- Bayer AG, Research & Development, Pharmaceuticals, Computational Molecular Design Berlin, Building S110, 711, 13342 Berlin, Germany
| | - Torben Gutermuth
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraβe 43, 20146 Hamburg, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraβe 43, 20146 Hamburg, Germany
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6
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Meyenburg C, Dolfus U, Briem H, Rarey M. Galileo: Three-dimensional searching in large combinatorial fragment spaces on the example of pharmacophores. J Comput Aided Mol Des 2023; 37:1-16. [PMID: 36418668 PMCID: PMC10032335 DOI: 10.1007/s10822-022-00485-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 10/17/2022] [Indexed: 11/25/2022]
Abstract
Fragment spaces are an efficient way to model large chemical spaces using a handful of small fragments and a few connection rules. The development of Enamine's REAL Space has shown that large spaces of readily available compounds may be created this way. These are several orders of magnitude larger than previous libraries. So far, searching and navigating these spaces is mostly limited to topological approaches. A way to overcome this limitation is optimization via metaheuristics which can be combined with arbitrary scoring functions. Here we present Galileo, a novel Genetic Algorithm to sample fragment spaces. We showcase Galileo in combination with a novel pharmacophore mapping approach, called Phariety, enabling 3D searches in fragment spaces. We estimate the effectiveness of the approach with a small fragment space. Furthermore, we apply Galileo to two pharmacophore searches in the REAL Space, detecting hundreds of compounds fulfilling a HSP90 and a FXIa pharmacophore.
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Affiliation(s)
- Christian Meyenburg
- Universität Hamburg, ZBH - Center for Bioinformatics, Universität Hamburg, Bundesstraße 43, 20146, Hamburg, Germany
| | - Uschi Dolfus
- Universität Hamburg, ZBH - Center for Bioinformatics, Universität Hamburg, Bundesstraße 43, 20146, Hamburg, Germany
| | - Hans Briem
- Research & Development, Pharmaceuticals, Computational Molecular Design Berlin, Bayer AG, Building S110, 711, 13342, Berlin, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, Universität Hamburg, Bundesstraße 43, 20146, Hamburg, Germany.
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7
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Penner P, Martiny V, Bellmann L, Flachsenberg F, Gastreich M, Theret I, Meyer C, Rarey M. FastGrow: on-the-fly growing and its application to DYRK1A. J Comput Aided Mol Des 2022; 36:639-651. [PMID: 35989379 PMCID: PMC9512872 DOI: 10.1007/s10822-022-00469-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 07/26/2022] [Indexed: 11/27/2022]
Abstract
Fragment-based drug design is an established routine approach in both experimental and computational spheres. Growing fragment hits into viable ligands has increasingly shifted into the spotlight. FastGrow is an application based on a shape search algorithm that addresses this challenge at high speeds of a few milliseconds per fragment. It further features a pharmacophoric interaction description, ensemble flexibility, as well as geometry optimization to become a fully fledged structure-based modeling tool. All features were evaluated in detail on a previously reported collection of fragment growing scenarios extracted from crystallographic data. FastGrow was also shown to perform competitively versus established docking software. A case study on the DYRK1A kinase, using recently reported new chemotypes, illustrates FastGrow's features in practice and its ability to identify active fragments. FastGrow is freely available to the public as a web server at https://fastgrow.plus/ and is part of the SeeSAR 3D software package.
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Affiliation(s)
- Patrick Penner
- ZBH - Center for Bioinformatics, Universität Hamburg, Bundesstr. 43, 20146, Hamburg, Germany
| | - Virginie Martiny
- Institut de Recherches Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Louis Bellmann
- ZBH - Center for Bioinformatics, Universität Hamburg, Bundesstr. 43, 20146, Hamburg, Germany
| | - Florian Flachsenberg
- ZBH - Center for Bioinformatics, Universität Hamburg, Bundesstr. 43, 20146, Hamburg, Germany
- BioSolveIT GmbH, An der Ziegelei 79, 53757, Sankt Augustin, Germany
| | - Marcus Gastreich
- BioSolveIT GmbH, An der Ziegelei 79, 53757, Sankt Augustin, Germany
| | - Isabelle Theret
- Institut de Recherches Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Christophe Meyer
- Institut de Recherches Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Matthias Rarey
- ZBH - Center for Bioinformatics, Universität Hamburg, Bundesstr. 43, 20146, Hamburg, Germany.
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8
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Piticchio SG, Martínez-Cartró M, Scaffidi S, Rachman M, Rodriguez-Arevalo S, Sanchez-Arfelis A, Escolano C, Picaud S, Krojer T, Filippakopoulos P, von Delft F, Galdeano C, Barril X. Discovery of Novel BRD4 Ligand Scaffolds by Automated Navigation of the Fragment Chemical Space. J Med Chem 2021; 64:17887-17900. [PMID: 34898210 DOI: 10.1021/acs.jmedchem.1c01108] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Fragment-based drug discovery (FBDD) is a very effective hit identification method. However, the evolution of fragment hits into suitable leads remains challenging and largely artisanal. Fragment evolution is often scaffold-centric, meaning that its outcome depends crucially on the chemical structure of the starting fragment. Considering that fragment screening libraries cover only a small proportion of the corresponding chemical space, hits should be seen as probes highlighting privileged areas of the chemical space rather than actual starting points. We have developed an automated computational pipeline to mine the chemical space around any specific fragment hit, rapidly finding analogues that share a common interaction motif but are structurally novel and diverse. On a prospective application on the bromodomain-containing protein 4 (BRD4), starting from a known fragment, the platform yields active molecules with nonobvious scaffold changes. The procedure is fast and inexpensive and has the potential to uncover many hidden opportunities in FBDD.
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Affiliation(s)
- Serena G Piticchio
- Departament de Farmacia i Tecnología Farmacèutica, i Fisicoquímica, Institut de Biomedicina (IBUB), Universitat de Barcelona, Av. Joan XXIII, 27-31, E-08028 Barcelona, Spain
| | - Míriam Martínez-Cartró
- Departament de Farmacia i Tecnología Farmacèutica, i Fisicoquímica, Institut de Biomedicina (IBUB), Universitat de Barcelona, Av. Joan XXIII, 27-31, E-08028 Barcelona, Spain
| | - Salvatore Scaffidi
- Departament de Farmacia i Tecnología Farmacèutica, i Fisicoquímica, Institut de Biomedicina (IBUB), Universitat de Barcelona, Av. Joan XXIII, 27-31, E-08028 Barcelona, Spain
| | - Moira Rachman
- Departament de Farmacia i Tecnología Farmacèutica, i Fisicoquímica, Institut de Biomedicina (IBUB), Universitat de Barcelona, Av. Joan XXIII, 27-31, E-08028 Barcelona, Spain
| | - Sergio Rodriguez-Arevalo
- Laboratory of Medicinal Chemistry (Associated Unit to CSIC), Department of Pharmacology, Toxicology and Medicinal Chemistry, Faculty of Pharmacy and Food Sciences, and Institute of Biomedicine (IBUB), University of Barcelona, Av. Joan XXIII, 27-31, E-08028 Barcelona, Spain
| | - Ainoa Sanchez-Arfelis
- Laboratory of Medicinal Chemistry (Associated Unit to CSIC), Department of Pharmacology, Toxicology and Medicinal Chemistry, Faculty of Pharmacy and Food Sciences, and Institute of Biomedicine (IBUB), University of Barcelona, Av. Joan XXIII, 27-31, E-08028 Barcelona, Spain
| | - Carmen Escolano
- Laboratory of Medicinal Chemistry (Associated Unit to CSIC), Department of Pharmacology, Toxicology and Medicinal Chemistry, Faculty of Pharmacy and Food Sciences, and Institute of Biomedicine (IBUB), University of Barcelona, Av. Joan XXIII, 27-31, E-08028 Barcelona, Spain
| | - Sarah Picaud
- Structural Genomics Consortium, Nuffield Department of Medicine, Oxford University, Old Road Campus Research Building, Roosevelt Drive, OX3 7DQ Oxford, United Kingdom
| | - Tobias Krojer
- Structural Genomics Consortium, Nuffield Department of Medicine, Oxford University, Old Road Campus Research Building, Roosevelt Drive, OX3 7DQ Oxford, United Kingdom
| | - Panagis Filippakopoulos
- Structural Genomics Consortium, Nuffield Department of Medicine, Oxford University, Old Road Campus Research Building, Roosevelt Drive, OX3 7DQ Oxford, United Kingdom
| | - Frank von Delft
- Structural Genomics Consortium, Nuffield Department of Medicine, Oxford University, Old Road Campus Research Building, Roosevelt Drive, OX3 7DQ Oxford, United Kingdom.,Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot OX11 0QX, United Kingdom.,Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, United Kingdom.,Centre for Medicines Discovery, University of Oxford, Oxford OX1 3QU, United Kingdom.,Department of Biochemistry, University of Johannesburg, Auckland Park 2006, South Africa
| | - Carles Galdeano
- Departament de Farmacia i Tecnología Farmacèutica, i Fisicoquímica, Institut de Biomedicina (IBUB), Universitat de Barcelona, Av. Joan XXIII, 27-31, E-08028 Barcelona, Spain
| | - Xavier Barril
- Departament de Farmacia i Tecnología Farmacèutica, i Fisicoquímica, Institut de Biomedicina (IBUB), Universitat de Barcelona, Av. Joan XXIII, 27-31, E-08028 Barcelona, Spain.,Catalan Institution for Research and Advanced Studies (ICREA), Barcelona 08010, Spain
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9
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Penner P, Martiny V, Gohier A, Gastreich M, Ducrot P, Brown D, Rarey M. Shape-Based Descriptors for Efficient Structure-Based Fragment Growing. J Chem Inf Model 2020; 60:6269-6281. [PMID: 33196169 DOI: 10.1021/acs.jcim.0c00920] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Structure-based fragment growing is one of the key techniques in fragment-based drug design. Fragment growing is commonly practiced based on structural and biophysical data. Computational workflows are employed to predict which fragment elaborations could lead to high-affinity binders. Several such workflows exist but many are designed to be long running noninteractive systems. Shape-based descriptors have been proven to be fast and perform well at virtual-screening tasks. They could, therefore, be applied to the fragment-growing problem to enable an interactive fragment-growing workflow. In this work, we describe and analyze the use of specific shape-based directional descriptors for the task of fragment growing. The performance of these descriptors that we call ray volume matrices (RVMs) is evaluated on two data sets containing protein-ligand complexes. While the first set focuses on self-growing, the second measures practical performance in a cross-growing scenario. The runtime of screenings using RVMs as well as their robustness to three dimensional perturbations is also investigated. Overall, it can be shown that RVMs are useful to prefilter fragment candidates. For up to 84% of the 3299 generated self-growing cases and for up to 66% of the 326 generated cross-growing cases, RVMs could create poses with less than 2 Å root-mean-square deviation to the crystal structure with average query speeds of around 30,000 conformations per second. This opens the door for fast explorative screenings of fragment libraries.
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Affiliation(s)
- Patrick Penner
- ZBH-Center for Bioinformatics, Universität Hamburg, Bundesstr. 43, 20146 Hamburg, Germany
| | - Virginie Martiny
- Institut Recherches de Servier, 125 Chemin de Ronde, 78290 Croissy, France
| | - Arnaud Gohier
- Institut Recherches de Servier, 125 Chemin de Ronde, 78290 Croissy, France
| | - Marcus Gastreich
- BioSolveIT GmbH, An der Ziegelei 79, 53757 Sankt Augustin, Germany
| | - Pierre Ducrot
- Institut Recherches de Servier, 125 Chemin de Ronde, 78290 Croissy, France
| | - David Brown
- Institut Recherches de Servier, 125 Chemin de Ronde, 78290 Croissy, France
| | - Matthias Rarey
- ZBH-Center for Bioinformatics, Universität Hamburg, Bundesstr. 43, 20146 Hamburg, Germany
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10
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Li Q. Application of Fragment-Based Drug Discovery to Versatile Targets. Front Mol Biosci 2020; 7:180. [PMID: 32850968 PMCID: PMC7419598 DOI: 10.3389/fmolb.2020.00180] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 07/10/2020] [Indexed: 12/14/2022] Open
Abstract
Fragment-based drug discovery (FBDD) is a powerful method to develop potent small-molecule compounds starting from fragments binding weakly to targets. As FBDD exhibits several advantages over high-throughput screening campaigns, it becomes an attractive strategy in target-based drug discovery. Many potent compounds/inhibitors of diverse targets have been developed using this approach. Methods used in fragment screening and understanding fragment-binding modes are critical in FBDD. This review elucidates fragment libraries, methods utilized in fragment identification/confirmation, strategies applied in growing the identified fragments into drug-like lead compounds, and applications of FBDD to different targets. As FBDD can be readily carried out through different biophysical and computer-based methods, it will play more important roles in drug discovery.
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Affiliation(s)
- Qingxin Li
- Guangdong Provincial Engineering Laboratory of Biomass High Value Utilization, Guangdong Provincial Bioengineering Institute, Guangzhou Sugarcane Industry Research Institute, Guangdong Academy of Sciences, Guangzhou, China
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11
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Perez C, Soler D, Soliva R, Guallar V. FragPELE: Dynamic Ligand Growing within a Binding Site. A Novel Tool for Hit-To-Lead Drug Design. J Chem Inf Model 2020; 60:1728-1736. [PMID: 32027130 DOI: 10.1021/acs.jcim.9b00938] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The early stages of drug discovery rely on hit-to-lead programs, where initial hits undergo partial optimization to improve binding affinities for their biological target. This is an expensive and time-consuming process, requiring multiple iterations of trial and error designs, an ideal scenario for applying computer simulation. However, most state-of-the-art modeling techniques fail to provide a fast and reliable answer to the Induced-Fit protein-ligand problem. To aid in this matter, we present FragPELE, a new tool for in silico hit-to-lead drug design, capable of growing a fragment from a bound core while exploring the protein-ligand conformational space. We tested the ability of FragPELE to predict crystallographic data, even in cases where cryptic sub-pockets open because of the presence of particular R-groups. Additionally, we evaluated the potential of the software on growing and scoring five congeneric series from the 2015 FEP+ dataset, comparing them to FEP+, SP and Induced-Fit Glide, and MMGBSA simulations. Results show that FragPELE could be useful not only for finding new cavities and novel binding modes in cases where standard docking tools cannot but also to rank ligand activities in a reasonable amount of time and with acceptable precision.
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Affiliation(s)
- Carles Perez
- Life Sciences Department, Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Daniel Soler
- Nostrum Biodiscovery, Carrer Jordi Girona 29, Nexus II D128, 08034 Barcelona, Spain
| | - Robert Soliva
- Nostrum Biodiscovery, Carrer Jordi Girona 29, Nexus II D128, 08034 Barcelona, Spain
| | - Victor Guallar
- Life Sciences Department, Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain.,ICREA: Institució Catalana de Recerca i Estudis Avançats, Passeig Lluís Companys 23, 08010 Barcelona, Spain
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12
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Kawai K, Karuo Y, Tarui A, Sato K, Omote M. Effect of Structural Descriptors on the Design of Cyclin Dependent Kinase Inhibitors Using Similarity‐based Molecular Evolution. Mol Inform 2020; 39:e1900126. [DOI: 10.1002/minf.201900126] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Accepted: 01/14/2020] [Indexed: 12/22/2022]
Affiliation(s)
- Kentaro Kawai
- Faculty of Pharmaceutical SciencesSetsunan University 45-1 Nagaotoge-cho, Hirakata Osaka 573-0101 Japan
| | - Yukiko Karuo
- Faculty of Pharmaceutical SciencesSetsunan University 45-1 Nagaotoge-cho, Hirakata Osaka 573-0101 Japan
| | - Atsushi Tarui
- Faculty of Pharmaceutical SciencesSetsunan University 45-1 Nagaotoge-cho, Hirakata Osaka 573-0101 Japan
| | - Kazuyuki Sato
- Faculty of Pharmaceutical SciencesSetsunan University 45-1 Nagaotoge-cho, Hirakata Osaka 573-0101 Japan
| | - Masaaki Omote
- Faculty of Pharmaceutical SciencesSetsunan University 45-1 Nagaotoge-cho, Hirakata Osaka 573-0101 Japan
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