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Lavecchia A. Navigating the frontier of drug-like chemical space with cutting-edge generative AI models. Drug Discov Today 2024; 29:104133. [PMID: 39103144 DOI: 10.1016/j.drudis.2024.104133] [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: 02/21/2024] [Revised: 07/20/2024] [Accepted: 07/31/2024] [Indexed: 08/07/2024]
Abstract
Deep generative models (GMs) have transformed the exploration of drug-like chemical space (CS) by generating novel molecules through complex, nontransparent processes, bypassing direct structural similarity. This review examines five key architectures for CS exploration: recurrent neural networks (RNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), normalizing flows (NF), and Transformers. It discusses molecular representation choices, training strategies for focused CS exploration, evaluation criteria for CS coverage, and related challenges. Future directions include refining models, exploring new notations, improving benchmarks, and enhancing interpretability to better understand biologically relevant molecular properties.
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Affiliation(s)
- Antonio Lavecchia
- 'Drug Discovery' Laboratory, Department of Pharmacy, University of Naples Federico II, I-80131 Naples, Italy.
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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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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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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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Wang Q, Fu X, Yan Y, Liu T, Xie Y, Song X, Zhou Y, Xu M, Wang P, Fu P, Huang J, Huang N. Structure-Based Identification of Organoruthenium Compounds as Nanomolar Antagonists of Cannabinoid Receptors. J Chem Inf Model 2024; 64:761-774. [PMID: 38215394 DOI: 10.1021/acs.jcim.3c01282] [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: 01/14/2024]
Abstract
Metal complexes exhibit a diverse range of coordination geometries, representing novel privileged scaffolds with convenient click types of preparation inaccessible for typical carbon-centered organic compounds. Herein, we explored the opportunity to identify biologically active organometallic complexes by reverse docking of a rigid, minimum-size octahedral organoruthenium scaffold against thousands of protein-binding pockets. Interestingly, cannabinoid receptor type 1 (CB1) was identified based on the docking scores and the degree of overlap between the docked organoruthenium scaffold and the hydrophobic scaffold of the cocrystallized ligand. Further structure-based optimization led to the discovery of organoruthenium complexes with nanomolar binding affinities and high selectivity toward CB2. Our work indicates that octahedral organoruthenium scaffolds may be advantageous for targeting the large and hydrophobic binding pockets and that the reverse docking approach may facilitate the discovery of novel privileged scaffolds, such as organometallic complexes, for exploring chemical space in lead discovery.
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Affiliation(s)
- Qing Wang
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, China
- National Institute of Biological Sciences, No. 7 Science Park Road, Zhongguancun Life Science Park, Beijing 102206, China
| | - Xuegang Fu
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, China
| | - Yuting Yan
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, China
| | - Tao Liu
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, China
- National Institute of Biological Sciences, No. 7 Science Park Road, Zhongguancun Life Science Park, Beijing 102206, China
| | - Yuting Xie
- National Institute of Biological Sciences, No. 7 Science Park Road, Zhongguancun Life Science Park, Beijing 102206, China
| | - Xiaoqing Song
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, China
| | - Yu Zhou
- National Institute of Biological Sciences, No. 7 Science Park Road, Zhongguancun Life Science Park, Beijing 102206, China
| | - Min Xu
- National Institute of Biological Sciences, No. 7 Science Park Road, Zhongguancun Life Science Park, Beijing 102206, China
| | - Ping Wang
- National Institute of Biological Sciences, No. 7 Science Park Road, Zhongguancun Life Science Park, Beijing 102206, China
| | - Peng Fu
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, China
| | - Jianhui Huang
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, China
| | - 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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6
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Huang CH, Lin ST. MARS Plus: An Improved Molecular Design Tool for Complex Compounds Involving Ionic, Stereo, and Cis-Trans Isomeric Structures. J Chem Inf Model 2023; 63:7711-7728. [PMID: 38100117 DOI: 10.1021/acs.jcim.3c01745] [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: 12/26/2023]
Abstract
MARS (Molecular Assembling and Representation Suite) (Hsu et al. J. Chem. Inf. Model. 2019, 59, 3703-3713) is a toolbox for the molecular design of organic molecules. MARS uses integer arrays to represent the elements and connectivity between elements of a molecule. It provides a collection of operations to manipulate the elemental composition and connectivity of a molecule (or a pair of molecules), enabling the creation of novel chemical compounds. In this work, the original MARS is extended to handle complex molecular structures, including geometric (cis-trans) isomers, stereo isomers, cyclic compounds, and ionic species. The extended version of MARS, referred to as MARS+, has a more comprehensive coverage of the chemical space and therefore can explore molecules with a greater chemical and physical diversity. Compared to other molecular design tools, MARS+ is designed to perform all possible manipulations on a given molecule or a pair of molecules. Molecular structure manipulation can be conducted in either a controlled or a random fashion. Furthermore, every structure manipulation has a counterpart so that the operation can be reversed. Nearly any possible chemical structure can be generated with MARS+ via a combination of molecular operations. The capabilities of MARS+ are examined by the design of new ionic liquids (ILs). The results show that MARS+ is a useful tool for computer-aided molecular design (CAMD) and molecular structure enumeration.
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Affiliation(s)
- Chen-Hsuan Huang
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Shiang-Tai Lin
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
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7
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Du H, Jiang D, Zhang O, Wu Z, Gao J, Zhang X, Wang X, Deng Y, Kang Y, Li D, Pan P, Hsieh CY, Hou T. A flexible data-free framework for structure-based de novo drug design with reinforcement learning. Chem Sci 2023; 14:12166-12181. [PMID: 37969589 PMCID: PMC10631243 DOI: 10.1039/d3sc04091g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 10/11/2023] [Indexed: 11/17/2023] Open
Abstract
Contemporary structure-based molecular generative methods have demonstrated their potential to model the geometric and energetic complementarity between ligands and receptors, thereby facilitating the design of molecules with favorable binding affinity and target specificity. Despite the introduction of deep generative models for molecular generation, the atom-wise generation paradigm that partially contradicts chemical intuition limits the validity and synthetic accessibility of the generated molecules. Additionally, the dependence of deep learning models on large-scale structural data has hindered their adaptability across different targets. To overcome these challenges, we present a novel search-based framework, 3D-MCTS, for structure-based de novo drug design. Distinct from prevailing atom-centric methods, 3D-MCTS employs a fragment-based molecular editing strategy. The fragments decomposed from small-molecule drugs are recombined under predefined retrosynthetic rules, offering improved drug-likeness and synthesizability, overcoming the inherent limitations of atom-based approaches. Leveraging multi-threaded parallel simulations combined with a real-time energy constraint-based pruning strategy, 3D-MCTS achieves remarkable efficiency. At a fixed computational cost, it outperforms other state-of-the-art (SOTA) methods by producing molecules with enhanced binding affinity. Furthermore, its fragment-based approach ensures the generation of more dependable binding conformations, exhibiting a success rate 43.6% higher than that of other SOTAs. This advantage becomes even more pronounced when handling targets that significantly deviate from the training dataset. 3D-MCTS is capable of achieving thirty times more hits with high binding affinity than traditional virtual screening methods, which demonstrates the superior ability of 3D-MCTS to explore chemical space. Moreover, the flexibility of our framework makes it easy to incorporate domain knowledge during the process, thereby enabling the generation of molecules with desirable pharmacophores and enhanced binding affinity. The adaptability of 3D-MCTS is further showcased in metalloprotein applications, highlighting its potential across various drug design scenarios.
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Affiliation(s)
- Hongyan Du
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Dejun Jiang
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Odin Zhang
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Zhenxing Wu
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Junbo Gao
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Xujun Zhang
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Xiaorui Wang
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology Macao 999078 China
| | - Yafeng Deng
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Dan Li
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Peichen Pan
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Chang-Yu Hsieh
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
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8
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Matos GDR, Pak S, Rizzo RC. Descriptor-Driven de Novo Design Algorithms for DOCK6 Using RDKit. J Chem Inf Model 2023; 63:5803-5822. [PMID: 37698425 PMCID: PMC10694857 DOI: 10.1021/acs.jcim.3c01031] [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] [Indexed: 09/13/2023]
Abstract
Structure-based methods that employ principles of de novo design can be used to construct small organic molecules from scratch using pre-existing fragment libraries to sample chemical space and are an important class of computational algorithms for drug-lead discovery. Here, we present a powerful new design method for DOCK6 that employs a Descriptor-Driven De Novo strategy (termed D3N) in which user-defined cheminformatics descriptors (and their target ranges) are calculated at each layer of growth using the open-source toolkit RDKit. The objective is to tailor ligand growth toward desirable regions of chemical space. The approach was extensively validated through: (1) comparison of cheminformatics descriptors computed using the new DOCK6/RDKit interface versus the standard Python/RDKit installation, (2) examination of descriptor distributions generated using D3N growth under different conditions (target ranges and environments), and (3) construction of ligands with very tight (pinpoint) descriptor ranges using clinically relevant compounds as a reference. Our testing confirms that the new DOCK6/RDKit integration is robust, showcases how the new D3N routines can be used to direct sampling around user-defined chemical spaces, and highlights the utility of on-the-fly descriptor calculations for ligand design to important drug targets.
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Affiliation(s)
- Guilherme Duarte Ramos Matos
- Department of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, New York 11794, USA
- Instituto de Química, Universidade de Brasília, Brasília, Distrito Federal, 70910-900, Brazil
| | - Steven Pak
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, New York, 11794, USA
| | - Robert C. Rizzo
- Department of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, New York 11794, USA
- Institute of Chemical Biology & Drug Discovery, Stony Brook University, Stony Brook, New York 11794, USA
- Laufer Center for Physical & Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA
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9
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Stegmann DP, Steuber J, Fritz G, Wojdyla JA, Sharpe ME. Fast fragment and compound screening pipeline at the Swiss Light Source. Methods Enzymol 2023; 690:235-284. [PMID: 37858531 DOI: 10.1016/bs.mie.2023.08.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Crystallography-based fragment screening is a highly effective technique employed in structure-based drug discovery to expand the range of lead development opportunities. It allows screening and sorting of weakly binding, low molecular mass fragments, which can be developed into larger high-affinity lead compounds. Technical improvements at synchrotron beamlines, design of innovative libraries mapping chemical space efficiently, effective soaking methods and enhanced data analysis have enabled the implementation of high-throughput fragment screening pipelines at multiple synchrotron facilities. This widened access to CBFS beyond the pharma industry has allowed academic users to rapidly screen large quantities of fragment-soaked protein crystals. The positive outcome of a CBFS campaign is a set of structures that present the three-dimensional arrangement of fragment-protein complexes in detail, thereby providing information on the location and the mode of interaction of bound fragments. Through this review, we provide users with a comprehensive guide that sets clear expectations before embarking on a crystallography-based fragment screening campaign. We present a list of essential pre-requirements that must be assessed, including the suitability of your current crystal system for a fragment screening campaign. Furthermore, we extensively discuss the available methodological options, addressing their limitations and providing strategies to overcome them. Additionally, we provide a brief perspective on how to proceed once hits are obtained. Notably, we emphasize the solutions we have implemented for instrumentation and software development within our Fast Fragment and Compound Screening pipeline. We also highlight third-party software options that can be utilized for rapid refinement and hit assessment.
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Affiliation(s)
| | - Julia Steuber
- Institute of Biology, Department of Cellular Microbiology, University of Hohenheim, Stuttgart, Germany
| | - Günter Fritz
- Institute of Biology, Department of Cellular Microbiology, University of Hohenheim, Stuttgart, Germany.
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10
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Zhai S, Tan Y, Zhang C, Hipolito CJ, Song L, Zhu C, Zhang Y, Duan H, Yin Y. PepScaf: Harnessing Machine Learning with In Vitro Selection toward De Novo Macrocyclic Peptides against IL-17C/IL-17RE Interaction. J Med Chem 2023; 66:11187-11200. [PMID: 37480587 DOI: 10.1021/acs.jmedchem.3c00627] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
The combination of library-based screening and artificial intelligence (AI) has been accelerating the discovery and optimization of hit ligands. However, the potential of AI to assist in de novo macrocyclic peptide ligand discovery has yet to be fully explored. In this study, an integrated AI framework called PepScaf was developed to extract the critical scaffold relative to bioactivity based on a vast dataset from an initial in vitro selection campaign against a model protein target, interleukin-17C (IL-17C). Taking the generated scaffold, a focused macrocyclic peptide library was rationally constructed to target IL-17C, yielding over 20 potent peptides that effectively inhibited IL-17C/IL-17RE interaction. Notably, the top two peptides displayed exceptional potency with IC50 values of 1.4 nM. This approach presents a viable methodology for more efficient macrocyclic peptide discovery, offering potential time and cost savings. Additionally, this is also the first report regarding the discovery of macrocyclic peptides against IL-17C/IL-17RE interaction.
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Affiliation(s)
- Silong Zhai
- School of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Yahong Tan
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao 266237, China
| | - Chengyun Zhang
- School of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Christopher John Hipolito
- Screening & Compound Profiling, Quantitative Biosciences, Merck & Co., Inc., Kenilworth, New Jersey 07033, United States
| | - Lulu Song
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao 266237, China
| | - Cheng Zhu
- School of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Youming Zhang
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao 266237, China
| | - Hongliang Duan
- School of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Yizhen Yin
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao 266237, China
- Shandong Research Institute of Industrial Technology, Jinan 250101, China
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11
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Torielli L, Serapian SA, Mussolin L, Moroni E, Colombo G. Integrating Protein Interaction Surface Prediction with a Fragment-Based Drug Design: Automatic Design of New Leads with Fragments on Energy Surfaces. J Chem Inf Model 2023; 63:343-353. [PMID: 36574607 PMCID: PMC9832486 DOI: 10.1021/acs.jcim.2c01408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Protein-protein interactions (PPIs) have emerged in the past years as significant pharmacological targets in the development of new therapeutics due to their key roles in determining pathological pathways. Herein, we present fragments on energy surfaces, a simple and general design strategy that integrates the analysis of the dynamic and energetic signatures of proteins to unveil the substructures involved in PPIs, with docking, selection, and combination of drug-like fragments to generate new PPI inhibitor candidates. Specifically, structural representatives of the target protein are used as inputs for the blind physics-based prediction of potential protein interaction surfaces using the matrix of low coupling energy decomposition method. The predicted interaction surfaces are subdivided into overlapping windows that are used as templates to direct the docking and combination of fragments representative of moieties typically found in active drugs. This protocol is then applied and validated using structurally diverse, important PPI targets as test systems. We demonstrate that our approach facilitates the exploration of the molecular diversity space of potential ligands, with no requirement of prior information on the location and properties of interaction surfaces or on the structures of potential lead compounds. Importantly, the hit molecules that emerge from our ab initio design share high chemical similarity with experimentally tested active PPI inhibitors. We propose that the protocol we describe here represents a valuable means of generating initial leads against difficult targets for further development and refinement.
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Affiliation(s)
- Luca Torielli
- Department
of Chemistry, University of Pavia, Via Taramelli 12, Pavia27100, Italy
| | - Stefano A. Serapian
- Department
of Chemistry, University of Pavia, Via Taramelli 12, Pavia27100, Italy
| | - Lara Mussolin
- Department
of Woman’s and Child’s Health, Pediatric Hematology,
Oncology and Stem Cell Transplant Center, University of Padua, Via Giustiniani, 3, Padua35128, Italy,Istituto
di Ricerca Pediatrica Città della Speranza, Corso Stati Uniti, 4 F, Padova35127, Italy
| | | | - Giorgio Colombo
- Department
of Chemistry, University of Pavia, Via Taramelli 12, Pavia27100, Italy,
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12
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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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13
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Prentis LE, Singleton CD, Bickel JD, Allen WJ, Rizzo RC. A molecular evolution algorithm for ligand design in DOCK. J Comput Chem 2022; 43:1942-1963. [PMID: 36073674 PMCID: PMC9623574 DOI: 10.1002/jcc.26993] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 06/13/2022] [Accepted: 08/03/2022] [Indexed: 01/11/2023]
Abstract
As a complement to virtual screening, de novo design of small molecules is an alternative approach for identifying potential drug candidates. Here, we present a new 3D genetic algorithm to evolve molecules through breeding, mutation, fitness pressure, and selection. The method, termed DOCK_GA, builds upon and leverages powerful sampling, scoring, and searching routines previously implemented into DOCK6. Three primary experiments were used during development: Single-molecule evolution evaluated three selection methods (elitism, tournament, and roulette), in four clinically relevant systems, in terms of mutation type and crossover success, chemical properties, ensemble diversity, and fitness convergence, among others. Large scale benchmarking assessed performance across 651 different protein-ligand systems. Ensemble-based evolution demonstrated using multiple inhibitors simultaneously to seed growth in a SARS-CoV-2 target. Key takeaways include: (1) The algorithm is robust as demonstrated by the successful evolution of molecules across a large diverse dataset. (2) Users have flexibility with regards to parent input, selection method, fitness function, and molecular descriptors. (3) The program is straightforward to run and only requires a single executable and input file at run-time. (4) The elitism selection method yields more tightly clustered molecules in terms of 2D/3D similarity, with more favorable fitness, followed by tournament and roulette.
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Affiliation(s)
- Lauren E. Prentis
- Department of Biochemistry & Cell BiologyStony Brook UniversityStony BrookNew YorkUSA
| | | | - John D. Bickel
- Department of ChemistryStony Brook UniversityStony BrookNew YorkUSA
| | - William J. Allen
- Department of Applied Mathematics & StatisticsStony Brook UniversityStony BrookNew YorkUSA
| | - Robert C. Rizzo
- Department of Applied Mathematics & StatisticsStony Brook UniversityStony BrookNew YorkUSA
- Institute of Chemical Biology & Drug DiscoveryStony Brook UniversityStony BrookNew YorkUSA
- Laufer Center for Physical & Quantitative BiologyStony Brook UniversityStony BrookNew YorkUSA
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14
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Bieniek MK, Cree B, Pirie R, Horton JT, Tatum NJ, Cole DJ. An open-source molecular builder and free energy preparation workflow. Commun Chem 2022; 5:136. [PMID: 36320862 PMCID: PMC9607723 DOI: 10.1038/s42004-022-00754-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 10/11/2022] [Indexed: 01/27/2023] Open
Abstract
Automated free energy calculations for the prediction of binding free energies of congeneric series of ligands to a protein target are growing in popularity, but building reliable initial binding poses for the ligands is challenging. Here, we introduce the open-source FEgrow workflow for building user-defined congeneric series of ligands in protein binding pockets for input to free energy calculations. For a given ligand core and receptor structure, FEgrow enumerates and optimises the bioactive conformations of the grown functional group(s), making use of hybrid machine learning/molecular mechanics potential energy functions where possible. Low energy structures are optionally scored using the gnina convolutional neural network scoring function, and output for more rigorous protein-ligand binding free energy predictions. We illustrate use of the workflow by building and scoring binding poses for ten congeneric series of ligands bound to targets from a standard, high quality dataset of protein-ligand complexes. Furthermore, we build a set of 13 inhibitors of the SARS-CoV-2 main protease from the literature, and use free energy calculations to retrospectively compute their relative binding free energies. FEgrow is freely available at https://github.com/cole-group/FEgrow, along with a tutorial.
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Affiliation(s)
- Mateusz K. Bieniek
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
| | - Ben Cree
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
| | - Rachael Pirie
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
| | - Joshua T. Horton
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
| | - Natalie J. Tatum
- Newcastle University Centre for Cancer, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE2 4HH UK
| | - Daniel J. Cole
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
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15
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Ishitani R, Kataoka T, Rikimaru K. Molecular Design Method Using a Reversible Tree Representation of Chemical Compounds and Deep Reinforcement Learning. J Chem Inf Model 2022; 62:4032-4048. [PMID: 35960209 PMCID: PMC9472278 DOI: 10.1021/acs.jcim.2c00366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
Automatic design of molecules with specific chemical
and biochemical
properties is an important process in material informatics and computational
drug discovery. In this study, we designed a novel coarse-grained
tree representation of molecules (Reversible Junction Tree; “RJT”)
for the aforementioned purposes, which is reversely convertible to
the original molecule without external information. By leveraging
this representation, we further formulated the molecular design and
optimization problem as a tree-structure construction using deep reinforcement
learning (“RJT-RL”). In this method, all of the intermediate
and final states of reinforcement learning are convertible to valid
molecules, which could efficiently guide the optimization process
in simple benchmark tasks. We further examined the multiobjective
optimization and fine-tuning of the reinforcement learning models
using RJT-RL, demonstrating the applicability of our method to more
realistic tasks in drug discovery.
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Affiliation(s)
- Ryuichiro Ishitani
- Preferred Networks, Inc., 1-6-1 Otemachi, Chiyoda-ku, Tokyo 100-0004, Japan
| | - Toshiki Kataoka
- Preferred Networks, Inc., 1-6-1 Otemachi, Chiyoda-ku, Tokyo 100-0004, Japan
| | - Kentaro Rikimaru
- Preferred Networks, Inc., 1-6-1 Otemachi, Chiyoda-ku, Tokyo 100-0004, Japan
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16
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Yu C, Yu Y, Sun L, Li X, Liu Z, Ke M, Chen F. Highly diastereo- and enantioselective synthesis of multisubstituted allylic amino acid derivatives by allylic alkylation of a chiral glycine-based nickel complex and vinylethylene carbonates. Org Biomol Chem 2022; 20:4894-4899. [PMID: 35678149 DOI: 10.1039/d2ob00726f] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The asymmetric synthesis of multisubstituted allylic amino acid derivatives was accomplished by the allylic alkylation of a chiral glycine-based nickel complex with vinylethylene carbonates. High enantioselectivities and diastereoselectivities were obtained under mild reaction conditions. The gram-scale synthesis was carried out with a good yield and high enantioselectivity, indicating that the method is a highly efficient route to chiral multisubstituted allylic amino acid derivatives.
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Affiliation(s)
- Chao Yu
- College of Chemistry, Fuzhou University, Fuzhou 350108, People's Republic of China
| | - Yuyan Yu
- Institute of Pharmaceutical Science and Technology, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou 310014, People's Republic of China.
| | - Longwu Sun
- Institute of Pharmaceutical Science and Technology, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou 310014, People's Republic of China.
| | - Xinzhi Li
- Institute of Pharmaceutical Science and Technology, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou 310014, People's Republic of China.
| | - Zhigang Liu
- Engineering Center of Catalysis and Synthesis for Chiral Molecules, Department of Chemistry, Fudan University, 220 Handan Road, Shanghai 200433, People's Republic of China. .,Shanghai Engineering Center of Industrial Asymmetric Catalysis for Chiral Drugs, Shanghai 200433, People's Republic of China
| | - Miaolin Ke
- Institute of Pharmaceutical Science and Technology, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou 310014, People's Republic of China.
| | - Fener Chen
- College of Chemistry, Fuzhou University, Fuzhou 350108, People's Republic of China.,Institute of Pharmaceutical Science and Technology, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou 310014, People's Republic of China. .,Engineering Center of Catalysis and Synthesis for Chiral Molecules, Department of Chemistry, Fudan University, 220 Handan Road, Shanghai 200433, People's Republic of China. .,Shanghai Engineering Center of Industrial Asymmetric Catalysis for Chiral Drugs, Shanghai 200433, People's Republic of China
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17
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Bai Q, Liu S, Tian Y, Xu T, Banegas‐Luna AJ, Pérez‐Sánchez H, Huang J, Liu H, Yao X. Application advances of deep learning methods for de novo drug design and molecular dynamics simulation. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1581] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Qifeng Bai
- Key Lab of Preclinical Study for New Drugs of Gansu Province Institute of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Lanzhou University Lanzhou Gansu China
| | - Shuo Liu
- School of Pharmacy Lanzhou University Lanzhou Gansu China
| | - Yanan Tian
- School of Pharmacy Lanzhou University Lanzhou Gansu China
| | - Tingyang Xu
- Tencent AI Lab, Shenzhen Tencent Computer Ltd Shenzhen China
| | - Antonio Jesús Banegas‐Luna
- Structural Bioinformatics and High Performance Computing Research Group (BIO‐HPC), Computer Engineering Department UCAM Universidad Católica de Murcia Murcia Spain
| | - Horacio Pérez‐Sánchez
- Structural Bioinformatics and High Performance Computing Research Group (BIO‐HPC), Computer Engineering Department UCAM Universidad Católica de Murcia Murcia Spain
| | - Junzhou Huang
- Tencent AI Lab, Shenzhen Tencent Computer Ltd Shenzhen China
| | - Huanxiang Liu
- School of Pharmacy Lanzhou University Lanzhou Gansu China
| | - Xiaojun Yao
- College of Chemistry and Chemical Engineering Lanzhou University Lanzhou Gansu China
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18
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Lu C, Liu S, Shi W, Yu J, Zhou Z, Zhang X, Lu X, Cai F, Xia N, Wang Y. Systemic evolutionary chemical space exploration for drug discovery. J Cheminform 2022; 14:19. [PMID: 35365231 PMCID: PMC8973791 DOI: 10.1186/s13321-022-00598-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 03/11/2022] [Indexed: 11/29/2022] Open
Abstract
Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities. Compared with other screening technologies, computational de novo design has become a popular approach to overcome the limitation of current chemical libraries. Here, we reported a de novo design platform named systemic evolutionary chemical space explorer (SECSE). The platform was conceptually inspired by fragment-based drug design, that miniaturized a “lego-building” process within the pocket of a certain target. The key to virtual hits generation was then turned into a computational search problem. To enhance search and optimization, human intelligence and deep learning were integrated. Application of SECSE against phosphoglycerate dehydrogenase (PHGDH), proved its potential in finding novel and diverse small molecules that are attractive starting points for further validation. This platform is open-sourced and the code is available at http://github.com/KeenThera/SECSE.
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Affiliation(s)
- Chong Lu
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Shien Liu
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Weihua Shi
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Jun Yu
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Zhou Zhou
- Keen Therapeutics Co., Ltd., Shanghai, China
| | | | - Xiaoli Lu
- Keen Therapeutics Co., Ltd., Shanghai, China
| | - Faji Cai
- Keen Therapeutics Co., Ltd., Shanghai, China
| | | | - Yikai Wang
- Keen Therapeutics Co., Ltd., Shanghai, China.
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19
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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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20
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Xu Z, Wauchope OR, Frank AT. Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking. J Chem Inf Model 2021; 61:5589-5600. [PMID: 34633194 DOI: 10.1021/acs.jcim.1c00746] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Here, we report the implementation and application of a simple, structure-aware framework to generate target-specific screening libraries. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking to explore chemical space conditioned on the unique physicochemical properties of the active site of a biomolecular target. As a demonstration, we used our framework, which we refer to as sample-and-dock, to construct focused libraries for cyclin-dependent kinase type-2 (CDK2) and the active site of the main protease (Mpro) of the SARS-CoV-2 virus. We envision that the sample-and-dock framework could be used to generate theoretical maps of the chemical space specific to a given target and so provide information about its molecular recognition characteristics.
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Affiliation(s)
- Ziqiao Xu
- Chemistry Department, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
| | - Orrette R Wauchope
- Department of Natural Sciences, City University of New York, Baruch College, New York, New York 10010, United States
| | - Aaron T Frank
- Biophysics Program, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
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21
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Gasymov OK, Celik S, Agaeva G, Akyuz S, Kecel-Gunduz S, Qocayev NM, Ozel AE, Agaeva U, Bakhishova M, Aliyev JA. Evaluation of anti-cancer and anti-covid-19 properties of cationic pentapeptide Glu-Gln-Arg-Pro-Arg, from rice bran protein and its d-isomer analogs through molecular docking simulations. J Mol Graph Model 2021; 108:107999. [PMID: 34352727 PMCID: PMC8325105 DOI: 10.1016/j.jmgm.2021.107999] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 07/29/2021] [Accepted: 07/29/2021] [Indexed: 01/10/2023]
Abstract
Bioactive peptides derived from food proteins are becoming increasingly popular due to the growing awareness of their health-promoting properties. The structure and mechanism of anti-cancer action of pentapeptide Glu-Gln-Arg-Pro-Arg (EQRPR) derived from a rice bran protein are not known. Theoretical and experimental methods were employed to fill this gap. The conformation analysis of the EQRPR pentapeptide was performed first and the obtained lowest energy conformer was optimized. The experimental structural data obtained by FTIR and CD spectroscopies agree well with the theoretical results. d-isomer introduced one-by-one to each position and all D-isomers of the peptide were also examined for its possible anti-proteolytic and activity enhancement properties. The molecular docking revealed avid binding of the pentapeptide to the integrins α5β1 and αIIbβ3, with Kd values of 90 nM and 180 nM, respectively. Moreover, the EQRPR and its D-isomers showed strong binding affinities to apo- and holo-forms of Mpro, spike glycoprotein, ACE2, and dACE2. The predicted results indicate that the pentapeptide may significantly inhibit SARS-CoV-2 infection. Thus, the peptide has the potential to be the leading molecule in the drug discovery process as having multifunctional with diverse biological activities.
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Affiliation(s)
- Oktay K Gasymov
- Laboratory of Structure, Dynamics and Functions of Biomolecules, Institute of Biophysics of ANAS, 117 Z. Khalilov, Baku, AZ1171, Azerbaijan.
| | - Sefa Celik
- Physics Department, Science Faculty, Istanbul University, Vezneciler, 34134, Istanbul, Turkey
| | - Gulshen Agaeva
- Department of Biophysics, Institute for Physical Problems, Baku State University, Z.Khalilov, 23, Baku, AZ1148, Azerbaijan
| | - Sevim Akyuz
- Physics Department, Science and Letters Faculty, Istanbul Kultur University, Atakoy Campus, Bakirkoy 34156, Istanbul, Turkey
| | - Serda Kecel-Gunduz
- Physics Department, Science Faculty, Istanbul University, Vezneciler, 34134, Istanbul, Turkey
| | - Niftali M Qocayev
- Department of Physics, Baku State University, Z.Khalilov, 23, Baku, AZ1148, Azerbaijan
| | - Ayşen E Ozel
- Physics Department, Science Faculty, Istanbul University, Vezneciler, 34134, Istanbul, Turkey
| | - Ulker Agaeva
- Department of Biophysics, Institute for Physical Problems, Baku State University, Z.Khalilov, 23, Baku, AZ1148, Azerbaijan
| | - Matanat Bakhishova
- Laboratory of Structure, Dynamics and Functions of Biomolecules, Institute of Biophysics of ANAS, 117 Z. Khalilov, Baku, AZ1171, Azerbaijan
| | - Jamil A Aliyev
- National Center of Oncology, Azerbaijan Republic Ministry of Health, H.Zardabi, 79B, Baku, AZ1012, Azerbaijan
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22
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Chen J, Liu N, Huang Y, Wang Y, Sun Y, Wu Q, Li D, Gao S, Wang HW, Huang N, Qi X, Wang X. Structure of PDE3A-SLFN12 complex and structure-based design for a potent apoptosis inducer of tumor cells. Nat Commun 2021; 12:6204. [PMID: 34707099 PMCID: PMC8551160 DOI: 10.1038/s41467-021-26546-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 10/05/2021] [Indexed: 11/08/2022] Open
Abstract
Molecular glues are a class of small molecular drugs that mediate protein-protein interactions, that induce either the degradation or stabilization of target protein. A structurally diverse group of chemicals, including 17-β-estradiol (E2), anagrelide, nauclefine, and DNMDP, induces apoptosis by forming complexes with phosphodiesterase 3A (PDE3A) and Schlafen 12 protein (SLFN12). They do so by binding to the PDE3A enzymatic pocket that allows the compound-bound PDE3A to recruit and stabilize SLFN12, which in turn blocks protein translation, leading to apoptosis. In this work, we report the high-resolution cryo-electron microscopy structure of PDE3A-SLFN12 complexes isolated from cultured HeLa cells pre-treated with either anagrelide, or nauclefine, or DNMDP. The PDE3A-SLFN12 complexes exhibit a butterfly-like shape, forming a heterotetramer with these small molecules, which are packed in a shallow pocket in the catalytic domain of PDE3A. The resulting small molecule-modified interface binds to the short helix (E552-I558) of SLFN12 through hydrophobic interactions, thus "gluing" the two proteins together. Based on the complex structure, we designed and synthesized analogs of anagrelide, a known drug used for the treatment of thrombocytosis, to enhance their interactions with SLFN12, and achieved superior efficacy in inducing apoptosis in cultured cells as well as in tumor xenografts.
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Affiliation(s)
- Jie Chen
- National Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science Park, Beijing, 102206, China
| | - Nan Liu
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Advanced Innovation Center for Structural Biology, Beijing Frontier Research Center for Biological Structures, School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Yinpin Huang
- Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua University, 100084, Beijing, China
| | - Yuanxun Wang
- National Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science Park, Beijing, 102206, China
| | - Yuxing Sun
- National Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science Park, Beijing, 102206, China
| | - Qingcui Wu
- National Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science Park, Beijing, 102206, China
| | - Dianrong Li
- National Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science Park, Beijing, 102206, China
| | - Shuanhu Gao
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, East China Normal University, 3663N Zhongshan Road, Shanghai, 200062, China
| | - Hong-Wei Wang
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Advanced Innovation Center for Structural Biology, Beijing Frontier Research Center for Biological Structures, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
| | - Niu Huang
- National Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science Park, Beijing, 102206, China.
- Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua University, 100084, Beijing, China.
| | - Xiangbing Qi
- National Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science Park, Beijing, 102206, China.
- Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua University, 100084, Beijing, China.
| | - Xiaodong Wang
- National Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science Park, Beijing, 102206, China.
- Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua University, 100084, Beijing, China.
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23
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Bai Q, Tan S, Xu T, Liu H, Huang J, Yao X. MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm. Brief Bioinform 2021; 22:5890512. [PMID: 32778891 PMCID: PMC7454275 DOI: 10.1093/bib/bbaa161] [Citation(s) in RCA: 121] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 06/23/2020] [Accepted: 06/26/2020] [Indexed: 12/27/2022] Open
Abstract
Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning. Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. The MolAICal software mainly contains two modules for 3D drug design. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. Besides, the Lipinski's rule of five, Pan-assay interference compounds (PAINS), synthetic accessibility (SA) and other user-defined rules are introduced for filtering out unwanted ligands in MolAICal. To show the drug design modules of MolAICal, the membrane protein glucagon receptor and non-membrane protein SARS-CoV-2 main protease are chosen as the investigative drug targets. The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values. We believe that MolAICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket. MolAICal is freely for any nonprofit purpose and accessible at https://molaical.github.io.
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24
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Ke M, Liu Z, Zhang K, Zuo S, Chen F. Synergistic Pd/Cu catalysis for stereoselective allylation of vinylethylene carbonates with glycine iminoesters: Enantioselective access to diverse trisubstituted allylic amino acid derivatives. GREEN SYNTHESIS AND CATALYSIS 2021. [DOI: 10.1016/j.gresc.2021.04.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
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25
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De Fusco C, Schimpl M, Börjesson U, Cheung T, Collie I, Evans L, Narasimhan P, Stubbs C, Vazquez-Chantada M, Wagner DJ, Grondine M, Sanders MG, Tentarelli S, Underwood E, Argyrou A, Smith JM, Lynch JT, Chiarparin E, Robb G, Bagal SK, Scott JS. Fragment-Based Design of a Potent MAT2a Inhibitor and in Vivo Evaluation in an MTAP Null Xenograft Model. J Med Chem 2021; 64:6814-6826. [PMID: 33900758 DOI: 10.1021/acs.jmedchem.1c00067] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
MAT2a is a methionine adenosyltransferase that synthesizes the essential metabolite S-adenosylmethionine (SAM) from methionine and ATP. Tumors bearing the co-deletion of p16 and MTAP genes have been shown to be sensitive to MAT2a inhibition, making it an attractive target for treatment of MTAP-deleted cancers. A fragment-based lead generation campaign identified weak but efficient hits binding in a known allosteric site. By use of structure-guided design and systematic SAR exploration, the hits were elaborated through a merging and growing strategy into an arylquinazolinone series of potent MAT2a inhibitors. The selected in vivo tool compound 28 reduced SAM-dependent methylation events in cells and inhibited proliferation of MTAP-null cells in vitro. In vivo studies showed that 28 was able to induce antitumor response in an MTAP knockout HCT116 xenograft model.
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Affiliation(s)
- Claudia De Fusco
- Discovery Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Marianne Schimpl
- Discovery Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Ulf Börjesson
- Discovery Sciences, R&D, AstraZeneca, Gothenburg SE-431 83, Sweden
| | - Tony Cheung
- Oncology R&D, AstraZeneca, Boston, Massachusetts 02451, United States
| | - Iain Collie
- Discovery Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Laura Evans
- Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | | | | | | | - David J Wagner
- Oncology R&D, AstraZeneca, Boston, Massachusetts 02451, United States
| | - Michael Grondine
- Oncology R&D, AstraZeneca, Boston, Massachusetts 02451, United States
| | | | - Sharon Tentarelli
- Oncology R&D, AstraZeneca, Boston, Massachusetts 02451, United States
| | | | - Argyrides Argyrou
- Discovery Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - James M Smith
- Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - James T Lynch
- Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | | | - Graeme Robb
- Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Sharan K Bagal
- Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - James S Scott
- Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
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26
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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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27
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Prieto-Martínez FD, Medina-Franco JL. Current advances on the development of BET inhibitors: insights from computational methods. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2020; 122:127-180. [PMID: 32951810 DOI: 10.1016/bs.apcsb.2020.06.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Epigenetics was coined almost 70 years ago for the description of heritable phenotype without altering DNA sequences. Research on the field has uncovered significant roles of such mechanisms, that account for the biogenesis of several diseases. Further studies have led the way for drug development which targets epi-enzymes, mainly for cancer treatment. Of the numerous epi-targets involved with histone acetylation, bromodomains have captured the spotlight of drug discovery focused on novel therapies. However, due to high sequence identity, the development of potent and selective inhibitors poses a significant challenge. Herein, we discuss recent computational developments on BET inhibitors and other methods that may be applied for drug discovery in general. As a proof-of-concept, we discuss a virtual screening to identify novel BET inhibitors based on coumarin derivatives. From public data, we identified putative structure-activity relationships of coumarin scaffold and propose R-group modifications for BET selectivity. Results showed that the optimization and design of novel coumarins could be further explored.
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Affiliation(s)
- Fernando D Prieto-Martínez
- Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, Mexico City, Mexico
| | - José L Medina-Franco
- Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, Mexico City, Mexico
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28
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Wu F, Zhuo L, Wang F, Huang W, Hao G, Yang G. Auto In Silico Ligand Directing Evolution to Facilitate the Rapid and Efficient Discovery of Drug Lead. iScience 2020; 23:101179. [PMID: 32498019 PMCID: PMC7267738 DOI: 10.1016/j.isci.2020.101179] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 04/25/2020] [Accepted: 05/13/2020] [Indexed: 12/21/2022] Open
Abstract
Motivated by the growing demand for reducing the chemical optimization burden of H2L, we developed auto in silico ligand directing evolution (AILDE, http://chemyang.ccnu.edu.cn/ccb/server/AILDE), an efficient and general approach for the rapid identification of drug leads in accessible chemical space. This computational strategy relies on minor chemical modifications on the scaffold of a hit compound, and it is primarily intended for identifying new lead compounds with minimal losses or, in some cases, even increases in ligand efficiency. We also described how AILDE greatly reduces the chemical optimization burden in the design of mesenchymal-epithelial transition factor (c-Met) kinase inhibitors. We only synthesized eight compounds and found highly efficient compound 5g, which showed an ∼1,000-fold improvement in in vitro activity compared with the hit compound. 5g also displayed excellent in vivo antitumor efficacy as a drug lead. We believe that AILDE may be applied to a large number of studies for rapid design and identification of drug leads. AILDE was developed for the rapid identification of drug leads A potent drug lead targeted to c-Met was found by synthesizing only eight compounds
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Affiliation(s)
- Fengxu Wu
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China; International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China
| | - Linsheng Zhuo
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China; International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China
| | - Fan Wang
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China; International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China
| | - Wei Huang
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China; International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China.
| | - Gefei Hao
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China; International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China.
| | - Guangfu Yang
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, P. R. China; International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China; Collaborative Innovation Center of Chemical Science and Engineering, Tianjin 300072, P. R. China.
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29
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Ke M, Liu Z, Huang G, Wang J, Tao Y, Chen F. Palladium-Catalyzed Regio- and Stereoselective Cross-Coupling of Vinylethylene Carbonates with Ketimine Esters to Generate ( Z)-Tri- and Tetra-substituted Allylic Amino Acid Derivatives. Org Lett 2020; 22:4135-4140. [PMID: 32383610 DOI: 10.1021/acs.orglett.0c01211] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Herein we report the palladium-catalyzed regio- and stereoselective cross-coupling of vinylethylene carbonates with ketimine esters to construct allylic amino acid scaffolds. This operationally simple protocol furnished (Z)-tri- and tetra-substituted allylic amino acid derivatives in good to excellent yields with distinguished geometric control under mild reaction conditions and proved to be sufficient in large-scale synthesis while retaining excellent reactivity and stereoselectivity, highlighting the practical value of this transformation.
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Affiliation(s)
- Miaolin Ke
- Engineering Center of Catalysis and Synthesis for Chiral Molecules, Department of Chemistry, Fudan University, 220 Handan Road, Shanghai 200433, People's Republic of China.,Shanghai Engineering Center of Industrial Asymmetric Catalysis for Chiral Drugs, Shanghai 200433, People's Republic of China
| | - Zhigang Liu
- School of Environment and Chemical Engineering, Xi'an Polytechnic University, 19 Jinhua South Road, Xi'an, Shanxi 710048, People's Republic of China
| | - Guanxin Huang
- Engineering Center of Catalysis and Synthesis for Chiral Molecules, Department of Chemistry, Fudan University, 220 Handan Road, Shanghai 200433, People's Republic of China.,Shanghai Engineering Center of Industrial Asymmetric Catalysis for Chiral Drugs, Shanghai 200433, People's Republic of China
| | - Jiaqi Wang
- Engineering Center of Catalysis and Synthesis for Chiral Molecules, Department of Chemistry, Fudan University, 220 Handan Road, Shanghai 200433, People's Republic of China.,Shanghai Engineering Center of Industrial Asymmetric Catalysis for Chiral Drugs, Shanghai 200433, People's Republic of China
| | - Yuan Tao
- Engineering Center of Catalysis and Synthesis for Chiral Molecules, Department of Chemistry, Fudan University, 220 Handan Road, Shanghai 200433, People's Republic of China.,Shanghai Engineering Center of Industrial Asymmetric Catalysis for Chiral Drugs, Shanghai 200433, People's Republic of China
| | - Fener Chen
- Engineering Center of Catalysis and Synthesis for Chiral Molecules, Department of Chemistry, Fudan University, 220 Handan Road, Shanghai 200433, People's Republic of China.,Shanghai Engineering Center of Industrial Asymmetric Catalysis for Chiral Drugs, Shanghai 200433, People's Republic of China
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30
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Polishchuk P. CReM: chemically reasonable mutations framework for structure generation. J Cheminform 2020; 12:28. [PMID: 33430959 PMCID: PMC7178718 DOI: 10.1186/s13321-020-00431-w] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 04/15/2020] [Indexed: 12/12/2022] Open
Abstract
Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on the deep learning models and conventional atom-based approaches may result in invalid structures and fail to address their synthetic feasibility issues. On the other hand, conventional reaction-based approaches result in synthetically feasible compounds but novelty and diversity of generated compounds may be limited. Fragment-based approaches can provide both better novelty and diversity of generated compounds but the issue of synthetic complexity of generated structure was not explicitly addressed before. Here we developed a new framework of fragment-based structure generation that, by design, results in the chemically valid structures and provides flexible control over diversity, novelty, synthetic complexity and chemotypes of generated compounds. The framework was implemented as an open-source Python module and can be used to create custom workflows for the exploration of chemical space.
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Affiliation(s)
- Pavel Polishchuk
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University and University Hospital in Olomouc, Hnevotinska 5, 77900, Olomouc, Czech Republic.
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31
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Wei L, Wen W, Rao L, Huang Y, Lei M, Liu K, Hu S, Song R, Ren Y, Wan J. Cov_FB3D: A De Novo Covalent Drug Design Protocol Integrating the BA-SAMP Strategy and Machine-Learning-Based Synthetic Tractability Evaluation. J Chem Inf Model 2020; 60:4388-4402. [PMID: 32233478 DOI: 10.1021/acs.jcim.9b01197] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
De novo drug design actively seeks to use sets of chemical rules for the fast and efficient identification of structurally new chemotypes with the desired set of biological properties. Fragment-based de novo design tools have been successfully applied in the discovery of noncovalent inhibitors. Nevertheless, these tools are rarely applied in the field of covalent inhibitor design. Herein, we present a new protocol, called Cov_FB3D, which involves the in silico assembly of potential novel covalent inhibitors by identifying the active fragments in the covalently binding site of the target protein. In this protocol, we propose a BA-SAMP strategy, which combines the noncovalent moiety score with the X-Score as the molecular mechanism (MM) level, and the covalent candidate score with the PM7 as the QM level. The synthetic accessibility of each suggested compound could be further evaluated with machine-learning-based synthetic complexity evaluation (SCScore). An in-depth test of this protocol against the crystal structures of 15 covalent complexes consisting of BTK inhibitors, KRAS inhibitors, EGFR inhibitors, EphB1 inhibitors, MAGL inhibitors, and MAPK inhibitors revealed that most of these inhibitors could be de novo reproduced from the fragments by Cov_FB3D. The binding modes of most generated reference poses could accurately reproduce the known binding mode of most of the reference covalent adduct in the binding site (RMSD ≤ 2 Å). In particular, most of these inhibitors were ranked in the top 2%, using the BA-SAMP strategy. Notably, the novel human ALDOA inhibitor (T1) with potent inhibitory activity (0.34 ± 0.03 μM) and greater synthetic accessibility was successfully de novo designed by this protocol. The positive results confirm the abilities of Cov_FB3D protocol.
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Affiliation(s)
- Lin Wei
- International Cooperation Base of Pesticide and Green Synthesis (Hubei), Key Laboratory of Pesticide & Chemical Biology (CCNU), Ministry of Education, Department of Chemistry, Central China Normal University, Wuhan 430079, China
| | - Wuqiang Wen
- International Cooperation Base of Pesticide and Green Synthesis (Hubei), Key Laboratory of Pesticide & Chemical Biology (CCNU), Ministry of Education, Department of Chemistry, Central China Normal University, Wuhan 430079, China
| | - Li Rao
- International Cooperation Base of Pesticide and Green Synthesis (Hubei), Key Laboratory of Pesticide & Chemical Biology (CCNU), Ministry of Education, Department of Chemistry, Central China Normal University, Wuhan 430079, China
| | - Yunyuan Huang
- International Cooperation Base of Pesticide and Green Synthesis (Hubei), Key Laboratory of Pesticide & Chemical Biology (CCNU), Ministry of Education, Department of Chemistry, Central China Normal University, Wuhan 430079, China
| | - Mengting Lei
- International Cooperation Base of Pesticide and Green Synthesis (Hubei), Key Laboratory of Pesticide & Chemical Biology (CCNU), Ministry of Education, Department of Chemistry, Central China Normal University, Wuhan 430079, China
| | - Kai Liu
- Institute of Marine Drugs, Guangxi University of Chinese Medicine, Nanning, 530200, People's Republic of China
| | - Saiya Hu
- International Cooperation Base of Pesticide and Green Synthesis (Hubei), Key Laboratory of Pesticide & Chemical Biology (CCNU), Ministry of Education, Department of Chemistry, Central China Normal University, Wuhan 430079, China
| | - Rongrong Song
- International Cooperation Base of Pesticide and Green Synthesis (Hubei), Key Laboratory of Pesticide & Chemical Biology (CCNU), Ministry of Education, Department of Chemistry, Central China Normal University, Wuhan 430079, China
| | - Yanliang Ren
- International Cooperation Base of Pesticide and Green Synthesis (Hubei), Key Laboratory of Pesticide & Chemical Biology (CCNU), Ministry of Education, Department of Chemistry, Central China Normal University, Wuhan 430079, China
| | - Jian Wan
- International Cooperation Base of Pesticide and Green Synthesis (Hubei), Key Laboratory of Pesticide & Chemical Biology (CCNU), Ministry of Education, Department of Chemistry, Central China Normal University, Wuhan 430079, China
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32
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Öztürk H, Özgür A, Schwaller P, Laino T, Ozkirimli E. Exploring chemical space using natural language processing methodologies for drug discovery. Drug Discov Today 2020; 25:689-705. [DOI: 10.1016/j.drudis.2020.01.020] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 12/20/2019] [Accepted: 01/28/2020] [Indexed: 01/06/2023]
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33
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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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34
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Wang J, Dai Z, Xiong C, Zhu J, Lu J, Zhou Q. Palladium‐Catalyzed Allylic Alkylation of Aldimine Esters with Vinyl‐Cyclopropanes to Yield α,α‐Disubstituted α‐Amino Acid Derivatives. Adv Synth Catal 2019. [DOI: 10.1002/adsc.201901031] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Jiahua Wang
- State Key Laboratory of Natural Medicines, Department of ChemistryChina Pharmaceutical University Nanjing 211198 People's Republic of China
| | - Zonghao Dai
- State Key Laboratory of Natural Medicines, Department of ChemistryChina Pharmaceutical University Nanjing 211198 People's Republic of China
| | - Cheng Xiong
- State Key Laboratory of Natural Medicines, Department of ChemistryChina Pharmaceutical University Nanjing 211198 People's Republic of China
| | - Jin Zhu
- State Key Laboratory of Natural Medicines, Department of ChemistryChina Pharmaceutical University Nanjing 211198 People's Republic of China
| | - Jinrong Lu
- State Key Laboratory of Natural Medicines, Department of ChemistryChina Pharmaceutical University Nanjing 211198 People's Republic of China
| | - Qingfa Zhou
- State Key Laboratory of Natural Medicines, Department of ChemistryChina Pharmaceutical University Nanjing 211198 People's Republic of China
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35
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Mikhael S, Abrol R. Chiral Graphs: Reduced Representations of Ligand Scaffolds for Stereoselective Biomolecular Recognition, Drug Design, and Enhanced Exploration of Chemical Structure Space. ChemMedChem 2019; 14:798-809. [PMID: 30821046 DOI: 10.1002/cmdc.201800761] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 02/26/2019] [Indexed: 11/11/2022]
Abstract
Rational structure-based drug design relies on a detailed, atomic-level understanding of protein-ligand interactions. The chiral nature of drug binding sites in proteins has led to the discovery of predominantly chiral drugs. A mechanistic understanding of stereoselectivity (which governs how one stereoisomer of a drug might bind stronger than the others to a protein) depends on the topology of stereocenters in the chiral molecule. Chiral graphs and reduced chiral graphs, introduced here, are new topological representations of chiral ligands using graph theory, to facilitate a detailed understanding of chiral recognition of ligands/drugs by proteins. These representations are demonstrated by application to all ≈14 000+ chiral ligands in the Protein Data Bank (PDB), which will facilitate an understanding of protein-ligand stereoselectivity mechanisms. Ligand modifications during drug development can be easily incorporated into these chiral graphs. In addition, these chiral graphs present an efficient tool for a deep dive into the enormous chemical structure space to enable sampling of unexplored structural scaffolds.
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Affiliation(s)
- Simoun Mikhael
- Department of Chemistry and Biochemistry, College of Science and Mathematics, California State University, Northridge, CA, 91330, USA
| | - Ravinder Abrol
- Department of Chemistry and Biochemistry, College of Science and Mathematics, California State University, Northridge, CA, 91330, USA
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36
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Sommer K, Flachsenberg F, Rarey M. NAOMInext – Synthetically feasible fragment growing in a structure-based design context. Eur J Med Chem 2019; 163:747-762. [DOI: 10.1016/j.ejmech.2018.11.075] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 11/27/2018] [Accepted: 11/30/2018] [Indexed: 12/31/2022]
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37
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Gutiérrez M, Vallejos GA, Cortés MP, Bustos C. Bennett acceptance ratio method to calculate the binding free energy of BACE1 inhibitors: Theoretical model and design of new ligands of the enzyme. Chem Biol Drug Des 2019; 93:1117-1128. [PMID: 30693676 DOI: 10.1111/cbdd.13456] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 11/02/2018] [Accepted: 11/24/2018] [Indexed: 11/29/2022]
Abstract
In recent years, the design, development, and evaluation of several inhibitors of the BACE1 enzyme, as part of Alzheimer's treatment, have gathered the scientific community's interest. Here, a linear regression model was built using binding free energy calculations through the Bennett acceptance ratio method for 20 known inhibitors of the BACE1 enzyme, with a Pearson coefficient of R = 0.88 and R2 = 0.78. The validation of this model was verified employing eight additional random inhibitors, which also gave a linear correlation with R = 0.97 and R2 = 0.93. Furthermore, this linear regression model was also used for proposing the structure of four potential BACE1 inhibitors, and the most active of them gave a theoretical Kd = 10 nM. However, these molecules have not been synthesized yet. Our team used a total time of more than 800 ns for the Molecular Dynamics to carry out this study, and all the software used were freely available.
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Affiliation(s)
- Margarita Gutiérrez
- Laboratorio de Síntesis Orgánica y Actividades Biológicas (LSO-Act-Bio), Instituto de Química de los Recursos Naturales, Universidad de Talca, Talca, Chile
| | - Gabriel A Vallejos
- Facultad de Ciencias, Instituto de Ciencias Químicas, Universidad Austral de Chile, Valdivia, Chile
| | - Magdalena P Cortés
- Escuela de Química y Farmacia, Facultad de Farmacia, Universidad de Valparaíso, Valparaíso, Chile
| | - Carlos Bustos
- Facultad de Ciencias, Instituto de Ciencias Químicas, Universidad Austral de Chile, Valdivia, Chile
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38
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Hoffer L, Muller C, Roche P, Morelli X. Chemistry-driven Hit-to-lead Optimization Guided by Structure-based Approaches. Mol Inform 2018; 37:e1800059. [PMID: 30051601 DOI: 10.1002/minf.201800059] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Accepted: 06/24/2018] [Indexed: 12/17/2022]
Abstract
For several decades, hit identification for drug discovery has been facilitated by developments in both fragment-based and high-throughput screening technologies. However, a major bottleneck in drug discovery projects continues to be the optimization of primary hits from screening campaigns in order to derive lead compounds. Computational chemistry or molecular modeling can play an important role during this hit-to-lead (H2L) stage by both suggesting putative optimizations and decreasing the number of compounds to be experimentally synthesized and evaluated. However, it is also crucial to consider the feasibility of organically synthesizing these virtually designed compounds. Furthermore, the generated molecules should have reasonable physicochemical properties and be medicinally relevant. This review focuses on chemistry-driven and structure-based computational methods that can be used to tackle the difficult problem of H2L optimization, with emphasis being placed on the strategy developed in our laboratory.
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Affiliation(s)
- Laurent Hoffer
- CNRS, Inserm, Institut Paoli-Calmettes, Aix-Marseille Univ, CRCM, Marseille, France
| | | | - Philippe Roche
- CNRS, Inserm, Institut Paoli-Calmettes, Aix-Marseille Univ, CRCM, Marseille, France
| | - Xavier Morelli
- CNRS, Inserm, Institut Paoli-Calmettes, Aix-Marseille Univ, CRCM, Marseille, France.,Institut Paoli-Calmettes, IPC Drug Discovery, Marseille, France
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39
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Batiste L, Unzue A, Dolbois A, Hassler F, Wang X, Deerain N, Zhu J, Spiliotopoulos D, Nevado C, Caflisch A. Chemical Space Expansion of Bromodomain Ligands Guided by in Silico Virtual Couplings (AutoCouple). ACS CENTRAL SCIENCE 2018; 4:180-188. [PMID: 29532017 PMCID: PMC5833004 DOI: 10.1021/acscentsci.7b00401] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Indexed: 10/24/2023]
Abstract
Expanding the chemical space and simultaneously ensuring synthetic accessibility is of upmost importance, not only for the discovery of effective binders for novel protein classes but, more importantly, for the development of compounds against hard-to-drug proteins. Here, we present AutoCouple, a de novo approach to computational ligand design focused on the diversity-oriented generation of chemical entities via virtual couplings. In a benchmark application, chemically diverse compounds with low-nanomolar potency for the CBP bromodomain and high selectivity against the BRD4(1) bromodomain were achieved by the synthesis of about 50 derivatives of the original fragment. The binding mode was confirmed by X-ray crystallography, target engagement in cells was demonstrated, and antiproliferative activity was showcased in three cancer cell lines. These results reveal AutoCouple as a useful in silico coupling method to expand the chemical space in hit optimization campaigns resulting in potent, selective, and cell permeable bromodomain ligands.
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Affiliation(s)
- Laurent Batiste
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Andrea Unzue
- Department
of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Aymeric Dolbois
- Department
of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Fabrice Hassler
- Department
of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Xuan Wang
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
- Department
of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Nicholas Deerain
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Jian Zhu
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Dimitrios Spiliotopoulos
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Cristina Nevado
- Department
of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
| | - Amedeo Caflisch
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057, Zürich, Switzerland
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40
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Zhang Y, Chen Y, Zhang D, Wang L, Lu T, Jiao Y. Discovery of Novel Potent VEGFR-2 Inhibitors Exerting Significant Antiproliferative Activity against Cancer Cell Lines. J Med Chem 2017; 61:140-157. [DOI: 10.1021/acs.jmedchem.7b01091] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Yanmin Zhang
- Laboratory
of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Yadong Chen
- Laboratory
of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Danfeng Zhang
- School
of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Lu Wang
- School
of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Tao Lu
- Laboratory
of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
- School
of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
- State
Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China
| | - Yu Jiao
- School
of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
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Allen WJ, Fochtman BC, Balius TE, Rizzo RC. Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets. J Comput Chem 2017; 38:2641-2663. [PMID: 28940386 DOI: 10.1002/jcc.25052] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Accepted: 08/03/2017] [Indexed: 12/12/2022]
Abstract
De novo design can be used to explore vast areas of chemical space in computational lead discovery. As a complement to virtual screening, from-scratch construction of molecules is not limited to compounds in pre-existing vendor catalogs. Here, we present an iterative fragment growth method, integrated into the program DOCK, in which new molecules are built using rules for allowable connections based on known molecules. The method leverages DOCK's advanced scoring and pruning approaches and users can define very specific criteria in terms of properties or features to customize growth toward a particular region of chemical space. The code was validated using three increasingly difficult classes of calculations: (1) Rebuilding known X-ray ligands taken from 663 complexes using only their component parts (focused libraries), (2) construction of new ligands in 57 drug target sites using a library derived from ∼13M drug-like compounds (generic libraries), and (3) application to a challenging protein-protein interface on the viral drug target HIVgp41. The computational testing confirms that the de novo DOCK routines are robust and working as envisioned, and the compelling results highlight the potential utility for designing new molecules against a wide variety of important protein targets. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- William J Allen
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, 11794
| | - Brian C Fochtman
- Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, New York, 11794
| | - Trent E Balius
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, 94158
| | - Robert C Rizzo
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, 11794.,Institute of Chemical Biology and Drug Discovery, Stony Brook University, Stony Brook, New York, 11794.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, 11794
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42
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Chéron N, Shakhnovich EI. Effect of sampling on BACE-1 ligands binding free energy predictions via MM-PBSA calculations. J Comput Chem 2017; 38:1941-1951. [PMID: 28568844 DOI: 10.1002/jcc.24839] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Revised: 05/02/2017] [Accepted: 05/04/2017] [Indexed: 01/04/2023]
Abstract
The BACE-1 enzyme is a prime target to find a cure to Alzheimer's disease. In this article, we used the MM-PBSA approach to compute the binding free energies of 46 reported ligands to this enzyme. After showing that the most probable protonation state of the catalytic dyad is mono-protonated (on ASP32), we performed a thorough analysis of the parameters influencing the sampling of the conformational space (in total, more than 35 μs of simulations were performed). We show that ten simulations of 2 ns gives better results than one of 50 ns. We also investigated the influence of the protein force field, the water model, the periodic boundary conditions artifacts (box size), as well as the ionic strength. Amber03 with TIP3P, a minimal distance of 1.0 nm between the protein and the box edges and a ionic strength of I = 0.2 M provides the optimal correlation with experiments. Overall, when using these parameters, a Pearson correlation coefficient of R = 0.84 (R2 = 0.71) is obtained for the 46 ligands, spanning eight orders of magnitude of Kd (from 0.017 nm to 2000 μM, i.e., from -14.7 to -3.7 kcal/mol), with a ligand size from 22 to 136 atoms (from 138 to 937 g/mol). After a two-parameter fit of the binding affinities for 12 of the ligands, an error of RMSD = 1.7 kcal/mol was obtained for the remaining ligands. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Nicolas Chéron
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, 02138.,Département de Chimie, UMR 8640 PASTEUR, Ecole Normale Supérieure, PSL Research University, UPMC Univ. Paris 06, CNRS, 24 rue Lhomond, Paris, 75005, France
| | - Eugene I Shakhnovich
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, 02138
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43
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Zhang Y, Wang L, Zhang Q, Zhu G, Zhang Z, Zhou X, Chen Y, Lu T, Tang W. Potent Pan-Raf and Receptor Tyrosine Kinase Inhibitors Based on a Cyclopropyl Formamide Fragment Overcome Resistance. J Chem Inf Model 2017; 57:1439-1452. [DOI: 10.1021/acs.jcim.6b00795] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Yanmin Zhang
- School
of Basic Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Lu Wang
- School
of Basic Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Qing Zhang
- School
of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Gaoyuan Zhu
- School
of Basic Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Zhimin Zhang
- School
of Basic Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Xiang Zhou
- School
of Basic Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Yadong Chen
- School
of Basic Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Tao Lu
- School
of Basic Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
- State
Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, Jiangsu 210009, China
| | - Weifang Tang
- School
of Basic Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
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44
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Design, synthesis and evaluation of derivatives based on pyrimidine scaffold as potent Pan-Raf inhibitors to overcome resistance. Eur J Med Chem 2017; 130:86-106. [DOI: 10.1016/j.ejmech.2017.02.041] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 02/14/2017] [Accepted: 02/16/2017] [Indexed: 12/19/2022]
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45
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Helgren TR, Hagen TJ. Demonstration of AutoDock as an Educational Tool for Drug Discovery. JOURNAL OF CHEMICAL EDUCATION 2017; 94:345-349. [PMID: 28670004 PMCID: PMC5488801 DOI: 10.1021/acs.jchemed.6b00555] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Drug design and discovery remains a popular topic of study to many students interested in visible, real-world applications of the chemical sciences. It is important that laboratory experiments detailing the early stages of drug discovery incorporate both compound design and an exploration of ligand/receptor interactions. Molecular modeling is widely employed in research endeavors seeking to predict the activity of potential compounds prior to synthesis and can therefore be used to illustrate these concepts. The following activity therefore details the use of AutoDock to predict the binding affinity and docked pose of a series of CDK2 inhibitors. Students can then compare their docking output to experimentally determined inhibitory activities and crystal structures. Finally, the AutoDock workflow detailed in this activity can be used in research settings, provided the receptor crystal structure is known.
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Debroise T, Shakhnovich EI, Chéron N. A Hybrid Knowledge-Based and Empirical Scoring Function for Protein–Ligand Interaction: SMoG2016. J Chem Inf Model 2017; 57:584-593. [DOI: 10.1021/acs.jcim.6b00610] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Théau Debroise
- Department
of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Eugene I. Shakhnovich
- Department
of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Nicolas Chéron
- Department
of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
- Ecole Normale Supérieure, PSL Research University, UPMC Univ. Paris 06, CNRS, Département de Chimie,
UMR 8640 PASTEUR, 24 rue
Lhomond, 75005 Paris, France
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Abstract
The success of molecular modeling and computational chemistry efforts are, by definition, dependent on quality software applications. Open source software development provides many advantages to users of modeling applications, not the least of which is that the software is free and completely extendable. In this review we categorize, enumerate, and describe available open source software packages for molecular modeling and computational chemistry. An updated online version of this catalog can be found at https://opensourcemolecularmodeling.github.io.
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