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Huang ETC, Yang JS, Liao KYK, Tseng WCW, Lee CK, Gill M, Compas C, See S, Tsai FJ. Predicting blood-brain barrier permeability of molecules with a large language model and machine learning. Sci Rep 2024; 14:15844. [PMID: 38982309 PMCID: PMC11233737 DOI: 10.1038/s41598-024-66897-y] [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: 03/06/2024] [Accepted: 07/05/2024] [Indexed: 07/11/2024] Open
Abstract
Predicting the blood-brain barrier (BBB) permeability of small-molecule compounds using a novel artificial intelligence platform is necessary for drug discovery. Machine learning and a large language model on artificial intelligence (AI) tools improve the accuracy and shorten the time for new drug development. The primary goal of this research is to develop artificial intelligence (AI) computing models and novel deep learning architectures capable of predicting whether molecules can permeate the human blood-brain barrier (BBB). The in silico (computational) and in vitro (experimental) results were validated by the Natural Products Research Laboratories (NPRL) at China Medical University Hospital (CMUH). The transformer-based MegaMolBART was used as the simplified molecular input line entry system (SMILES) encoder with an XGBoost classifier as an in silico method to check if a molecule could cross through the BBB. We used Morgan or Circular fingerprints to apply the Morgan algorithm to a set of atomic invariants as a baseline encoder also with an XGBoost classifier to compare the results. BBB permeability was assessed in vitro using three-dimensional (3D) human BBB spheroids (human brain microvascular endothelial cells, brain vascular pericytes, and astrocytes). Using multiple BBB databases, the results of the final in silico transformer and XGBoost model achieved an area under the receiver operating characteristic curve of 0.88 on the held-out test dataset. Temozolomide (TMZ) and 21 randomly selected BBB permeable compounds (Pred scores = 1, indicating BBB-permeable) from the NPRL penetrated human BBB spheroid cells. No evidence suggests that ferulic acid or five BBB-impermeable compounds (Pred scores < 1.29423E-05, which designate compounds that pass through the human BBB) can pass through the spheroid cells of the BBB. Our validation of in vitro experiments indicated that the in silico prediction of small-molecule permeation in the BBB model is accurate. Transformer-based models like MegaMolBART, leveraging the SMILES representations of molecules, show great promise for applications in new drug discovery. These models have the potential to accelerate the development of novel targeted treatments for disorders of the central nervous system.
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Affiliation(s)
- Eddie T C Huang
- NVIDIA AI Technology Center, NVIDIA Corporation, Santa Clara, USA
| | - Jai-Sing Yang
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Ken Y K Liao
- NVIDIA AI Technology Center, NVIDIA Corporation, Santa Clara, USA
| | - Warren C W Tseng
- NVIDIA AI Technology Center, NVIDIA Corporation, Santa Clara, USA
| | - C K Lee
- NVIDIA AI Technology Center, NVIDIA Corporation, Santa Clara, USA
| | - Michelle Gill
- NVIDIA AI Technology Center, NVIDIA Corporation, Santa Clara, USA
| | - Colin Compas
- NVIDIA AI Technology Center, NVIDIA Corporation, Santa Clara, USA
| | - Simon See
- NVIDIA AI Technology Center, NVIDIA Corporation, Santa Clara, USA
| | - Fuu-Jen Tsai
- School of Chinese Medicine, College of Chinese Medicine, China Medical University, China Medical University Children's Hospital, No. 2, Yude Road, Taichung, 404332, Taiwan.
- China Medical University Children's Hospital, Taichung, Taiwan.
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2
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Lavecchia A. Advancing drug discovery with deep attention neural networks. Drug Discov Today 2024; 29:104067. [PMID: 38925473 DOI: 10.1016/j.drudis.2024.104067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 06/10/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024]
Abstract
In the dynamic field of drug discovery, deep attention neural networks are revolutionizing our approach to complex data. This review explores the attention mechanism and its extended architectures, including graph attention networks (GATs), transformers, bidirectional encoder representations from transformers (BERT), generative pre-trained transformers (GPTs) and bidirectional and auto-regressive transformers (BART). Delving into their core principles and multifaceted applications, we uncover their pivotal roles in catalyzing de novo drug design, predicting intricate molecular properties and deciphering elusive drug-target interactions. Despite challenges, these attention-based architectures hold unparalleled promise to drive transformative breakthroughs and accelerate progress in pharmaceutical research.
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Affiliation(s)
- Antonio Lavecchia
- Drug Discovery Laboratory, Department of Pharmacy, University of Napoli Federico II, I-80131 Naples, Italy.
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3
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Retchin M, Wang Y, Takaba K, Chodera JD. DrugGym: A testbed for the economics of autonomous drug discovery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.28.596296. [PMID: 38854082 PMCID: PMC11160604 DOI: 10.1101/2024.05.28.596296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Drug discovery is stochastic. The effectiveness of candidate compounds in satisfying design objectives is unknown ahead of time, and the tools used for prioritization-predictive models and assays-are inaccurate and noisy. In a typical discovery campaign, thousands of compounds may be synthesized and tested before design objectives are achieved, with many others ideated but deprioritized. These challenges are well-documented, but assessing potential remedies has been difficult. We introduce DrugGym, a framework for modeling the stochastic process of drug discovery. Emulating biochemical assays with realistic surrogate models, we simulate the progression from weak hits to sub-micromolar leads with viable ADME. We use this testbed to examine how different ideation, scoring, and decision-making strategies impact statistical measures of utility, such as the probability of program success within predefined budgets and the expected costs to achieve target candidate profile (TCP) goals. We also assess the influence of affinity model inaccuracy, chemical creativity, batch size, and multi-step reasoning. Our findings suggest that reducing affinity model inaccuracy from 2 to 0.5 pIC50 units improves budget-constrained success rates tenfold. DrugGym represents a realistic testbed for machine learning methods applied to the hit-to-lead phase. Source code is available at www.drug-gym.org.
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Affiliation(s)
- Michael Retchin
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Yuanqing Wang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Simons Center for Computational Chemistry and Center for Data Science, New York University, New York, NY 10004
| | - Kenichiro Takaba
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Pharmaceutical Research Center, Advanced Drug Discovery, Asahi Kasei Pharma Corporation, Shizuoka 410-2321, Japan
| | - John D. Chodera
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
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4
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Gangwal A, Lavecchia A. Unleashing the power of generative AI in drug discovery. Drug Discov Today 2024; 29:103992. [PMID: 38663579 DOI: 10.1016/j.drudis.2024.103992] [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: 02/09/2024] [Revised: 03/22/2024] [Accepted: 04/18/2024] [Indexed: 05/04/2024]
Abstract
Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de novo drug design, exploring diverse algorithms and their profound impact. It critically analyses the challenges that are intricately interwoven into these technologies, proposing strategies to unlock their full potential. It features case studies of both successes and failures in advancing drugs to clinical trials with AI assistance. Last, it outlines a forward-looking plan for optimizing DGMs in de novo drug design, thereby fostering faster and more cost-effective drug development.
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Affiliation(s)
- Amit Gangwal
- Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal's Institute of Pharmacy, Dhule 424001, Maharashtra, India
| | - Antonio Lavecchia
- "Drug Discovery" Laboratory, Department of Pharmacy, University of Naples Federico II, I-80131 Naples, Italy.
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5
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Malakhov G, Pogodin P. Dataset of drugs, their molecular scaffolds and medical indications with interactive visualization. Data Brief 2024; 54:110417. [PMID: 38698799 PMCID: PMC11063979 DOI: 10.1016/j.dib.2024.110417] [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: 12/29/2023] [Revised: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 05/05/2024] Open
Abstract
Bemis-Murcko scaffolding [1] is a powerful tool for compound clustering and subsequent analysis. Here, using ChEMBL database [2] and RDKit library [3], we have compiled the dataset of known small molecule drugs, their molecular scaffolds and associated medical indications augmented with the interactive interface. We present these data, which can be used by medicinal chemists to find most promising scaffolds for their tasks using an interactive visualization that can help to evaluate both the diversity of known drugs and pharmacological promiscuity of each particular scaffold visually. Our scripts, that are freely available, can help to carry out such scaffold-based analysis and to visualize a compound library in a similar way.
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Affiliation(s)
- Georgii Malakhov
- Institute of Biomedical Chemistry, Pogodinskaya Street, 10, 119121, Moscow, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskie Gory, 1-73, 119991, Moscow, Russia
| | - Pavel Pogodin
- Institute of Biomedical Chemistry, Pogodinskaya Street, 10, 119121, Moscow, Russia
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6
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Alberga D, Lamanna G, Graziano G, Delre P, Lomuscio MC, Corriero N, Ligresti A, Siliqi D, Saviano M, Contino M, Stefanachi A, Mangiatordi GF. DeLA-DrugSelf: Empowering multi-objective de novo design through SELFIES molecular representation. Comput Biol Med 2024; 175:108486. [PMID: 38653065 DOI: 10.1016/j.compbiomed.2024.108486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 04/08/2024] [Accepted: 04/15/2024] [Indexed: 04/25/2024]
Abstract
In this paper, we introduce DeLA-DrugSelf, an upgraded version of DeLA-Drug [J. Chem. Inf. Model. 62 (2022) 1411-1424], which incorporates essential advancements for automated multi-objective de novo design. Unlike its predecessor, which relies on SMILES notation for molecular representation, DeLA-DrugSelf employs a novel and robust molecular representation string named SELFIES (SELF-referencing Embedded String). The generation process in DeLA-DrugSelf not only involves substitutions to the initial string representing the starting query molecule but also incorporates insertions and deletions. This enhancement makes DeLA-DrugSelf significantly more adept at executing data-driven scaffold decoration and lead optimization strategies. Remarkably, DeLA-DrugSelf explicitly addresses the SELFIES-related collapse issue, considering only collapse-free compounds during generation. These compounds undergo a rigorous quality metrics evaluation, highlighting substantial advancements in terms of drug-likeness, uniqueness, and novelty compared to the molecules generated by the previous version of the algorithm. To evaluate the potential of DeLA-DrugSelf as a mutational operator within a genetic algorithm framework for multi-objective optimization, we employed a fitness function based on Pareto dominance. Our objectives focused on target-oriented properties aimed at optimizing known cannabinoid receptor 2 (CB2R) ligands. The results obtained indicate that DeLA-DrugSelf, available as a user-friendly web platform (https://www.ba.ic.cnr.it/softwareic/delaself/), can effectively contribute to the data-driven optimization of starting bioactive molecules based on user-defined parameters.
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Affiliation(s)
- Domenico Alberga
- CNR - Institute of Crystallography, Via Amendola 122/o, 70126, Bari, Italy
| | - Giuseppe Lamanna
- CNR - Institute of Crystallography, Via Amendola 122/o, 70126, Bari, Italy
| | - Giovanni Graziano
- Department of Pharmacy - Pharmaceutical Sciences, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125, Bari, Italy
| | - Pietro Delre
- CNR - Institute of Crystallography, Via Amendola 122/o, 70126, Bari, Italy
| | | | - Nicola Corriero
- CNR - Institute of Crystallography, Via Amendola 122/o, 70126, Bari, Italy
| | - Alessia Ligresti
- CNR - Institute of Biomolecular Chemistry, Via Campi Flegrei 34, 80078, Pozzuoli, Italy
| | - Dritan Siliqi
- CNR - Institute of Crystallography, Via Amendola 122/o, 70126, Bari, Italy
| | - Michele Saviano
- CNR - Institute of Crystallography, Via Vivaldi 43, 81100, Caserta, Italy
| | - Marialessandra Contino
- Department of Pharmacy - Pharmaceutical Sciences, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125, Bari, Italy
| | - Angela Stefanachi
- Department of Pharmacy - Pharmaceutical Sciences, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125, Bari, Italy
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7
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Thomas M, O'Boyle NM, Bender A, De Graaf C. MolScore: a scoring, evaluation and benchmarking framework for generative models in de novo drug design. J Cheminform 2024; 16:64. [PMID: 38816825 PMCID: PMC11141043 DOI: 10.1186/s13321-024-00861-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 05/15/2024] [Indexed: 06/01/2024] Open
Abstract
Generative models are undergoing rapid research and application to de novo drug design. To facilitate their application and evaluation, we present MolScore. MolScore already contains many drug-design-relevant scoring functions commonly used in benchmarks such as, molecular similarity, molecular docking, predictive models, synthesizability, and more. In addition, providing performance metrics to evaluate generative model performance based on the chemistry generated. With this unification of functionality, MolScore re-implements commonly used benchmarks in the field (such as GuacaMol, MOSES, and MolOpt). Moreover, new benchmarks can be created trivially. We demonstrate this by testing a chemical language model with reinforcement learning on three new tasks of increasing complexity related to the design of 5-HT2a ligands that utilise either molecular descriptors, 266 pre-trained QSAR models, or dual molecular docking. Lastly, MolScore can be integrated into an existing Python script with just three lines of code. This framework is a step towards unifying generative model application and evaluation as applied to drug design for both practitioners and researchers. The framework can be found on GitHub and downloaded directly from the Python Package Index.Scientific ContributionMolScore is an open-source platform to facilitate generative molecular design and evaluation thereof for application in drug design. This platform takes important steps towards unifying existing benchmarks, providing a platform to share new benchmarks, and improves customisation, flexibility and usability for practitioners over existing solutions.
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Affiliation(s)
- Morgan Thomas
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
| | - Noel M O'Boyle
- Computational Chemistry, Nxera Pharma, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Chris De Graaf
- Computational Chemistry, Nxera Pharma, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK
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8
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Chandraghatgi R, Ji HF, Rosen GL, Sokhansanj BA. Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening. J Chem Inf Model 2024; 64:3826-3840. [PMID: 38696451 PMCID: PMC11197033 DOI: 10.1021/acs.jcim.4c00234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 04/18/2024] [Accepted: 04/19/2024] [Indexed: 05/04/2024]
Abstract
Recent advances in computational methods provide the promise of dramatically accelerating drug discovery. While mathematical modeling and machine learning have become vital in predicting drug-target interactions and properties, there is untapped potential in computational drug discovery due to the vast and complex chemical space. This paper builds on our recently published computational fragment-based drug discovery (FBDD) method called fragment databases from screened ligand drug discovery (FDSL-DD). FDSL-DD uses in silico screening to identify ligands from a vast library, fragmenting them while attaching specific attributes based on predicted binding affinity and interaction with the target subdomain. In this paper, we further propose a two-stage optimization method that utilizes the information from prescreening to optimize computational ligand synthesis. We hypothesize that using prescreening information for optimization shrinks the search space and focuses on promising regions, thereby improving the optimization for candidate ligands. The first optimization stage assembles these fragments into larger compounds using genetic algorithms, followed by a second stage of iterative refinement to produce compounds with enhanced bioactivity. To demonstrate broad applicability, the methodology is demonstrated on three diverse protein targets found in human solid cancers, bacterial antimicrobial resistance, and the SARS-CoV-2 virus. Combined, the proposed FDSL-DD and a two-stage optimization approach yield high-affinity ligand candidates more efficiently than other state-of-the-art computational FBDD methods. We further show that a multiobjective optimization method accounting for drug-likeness can still produce potential candidate ligands with a high binding affinity. Overall, the results demonstrate that integrating detailed chemical information with a constrained search framework can markedly optimize the initial drug discovery process, offering a more precise and efficient route to developing new therapeutics.
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Affiliation(s)
- Rohan Chandraghatgi
- Department
of Biology, Drexel University, Philadelphia, Pennsylvania 19104, United States
| | - Hai-Feng Ji
- Department
of Chemistry, Drexel University, Philadelphia, Pennsylvania 19104, United States
| | - Gail L. Rosen
- Department
of Electrical & Computer Engineering, Drexel University, Philadelphia, Pennsylvania 19104, United States
| | - Bahrad A. Sokhansanj
- Department
of Electrical & Computer Engineering, Drexel University, Philadelphia, Pennsylvania 19104, United States
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9
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Loeffler HH, He J, Tibo A, Janet JP, Voronov A, Mervin LH, Engkvist O. Reinvent 4: Modern AI-driven generative molecule design. J Cheminform 2024; 16:20. [PMID: 38383444 PMCID: PMC10882833 DOI: 10.1186/s13321-024-00812-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 02/09/2024] [Indexed: 02/23/2024] Open
Abstract
REINVENT 4 is a modern open-source generative AI framework for the design of small molecules. The software utilizes recurrent neural networks and transformer architectures to drive molecule generation. These generators are seamlessly embedded within the general machine learning optimization algorithms, transfer learning, reinforcement learning and curriculum learning. REINVENT 4 enables and facilitates de novo design, R-group replacement, library design, linker design, scaffold hopping and molecule optimization. This contribution gives an overview of the software and describes its design. Algorithms and their applications are discussed in detail. REINVENT 4 is a command line tool which reads a user configuration in either TOML or JSON format. The aim of this release is to provide reference implementations for some of the most common algorithms in AI based molecule generation. An additional goal with the release is to create a framework for education and future innovation in AI based molecular design. The software is available from https://github.com/MolecularAI/REINVENT4 and released under the permissive Apache 2.0 license. Scientific contribution. The software provides an open-source reference implementation for generative molecular design where the software is also being used in production to support in-house drug discovery projects. The publication of the most common machine learning algorithms in one code and full documentation thereof will increase transparency of AI and foster innovation, collaboration and education.
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Affiliation(s)
- Hannes H Loeffler
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden.
| | - Jiazhen He
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Alessandro Tibo
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Jon Paul Janet
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Alexey Voronov
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Lewis H Mervin
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
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10
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Melancon K, Pliushcheuskaya P, Meiler J, Künze G. Targeting ion channels with ultra-large library screening for hit discovery. Front Mol Neurosci 2024; 16:1336004. [PMID: 38249296 PMCID: PMC10796734 DOI: 10.3389/fnmol.2023.1336004] [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: 11/09/2023] [Accepted: 12/05/2023] [Indexed: 01/23/2024] Open
Abstract
Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.
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Affiliation(s)
- Kortney Melancon
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | | | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
- Medical Faculty, Institute for Drug Discovery, Leipzig University, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, Leipzig, Germany
| | - Georg Künze
- Medical Faculty, Institute for Drug Discovery, Leipzig University, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, Leipzig University, Leipzig, Germany
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11
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Kolomenskaya E, Butova V, Poltavskiy A, Soldatov A, Butakova M. Application of Artificial Intelligence at All Stages of Bone Tissue Engineering. Biomedicines 2023; 12:76. [PMID: 38255183 PMCID: PMC10813365 DOI: 10.3390/biomedicines12010076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 12/23/2023] [Accepted: 12/26/2023] [Indexed: 01/24/2024] Open
Abstract
The development of artificial intelligence (AI) has revolutionized medical care in recent years and plays a vital role in a number of areas, such as diagnostics and forecasting. In this review, we discuss the most promising areas of AI application to the field of bone tissue engineering and prosthetics, which can drastically benefit from AI-assisted optimization and patient personalization of implants and scaffolds in ways ranging from visualization and real-time monitoring to the implantation cases prediction, thereby leveraging the compromise between specific architecture decisions, material choice, and synthesis procedure. With the emphasized crucial role of accuracy and robustness of developed AI algorithms, especially in bone tissue engineering, it was shown that rigorous validation and testing, demanding large datasets and extensive clinical trials, are essential, and we discuss how through developing multidisciplinary cooperation among biology, chemistry with materials science, and AI, these challenges can be addressed.
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Affiliation(s)
- Ekaterina Kolomenskaya
- The Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova, 344090 Rostov-on-Don, Russia; (V.B.); (A.P.); (A.S.); (M.B.)
| | - Vera Butova
- The Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova, 344090 Rostov-on-Don, Russia; (V.B.); (A.P.); (A.S.); (M.B.)
- Institute of General and Inorganic Chemistry, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | - Artem Poltavskiy
- The Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova, 344090 Rostov-on-Don, Russia; (V.B.); (A.P.); (A.S.); (M.B.)
| | - Alexander Soldatov
- The Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova, 344090 Rostov-on-Don, Russia; (V.B.); (A.P.); (A.S.); (M.B.)
| | - Maria Butakova
- The Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova, 344090 Rostov-on-Don, Russia; (V.B.); (A.P.); (A.S.); (M.B.)
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12
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Angelo JS, Guedes IA, Barbosa HJC, Dardenne LE. Multi-and many-objective optimization: present and future in de novo drug design. Front Chem 2023; 11:1288626. [PMID: 38192501 PMCID: PMC10773868 DOI: 10.3389/fchem.2023.1288626] [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: 09/04/2023] [Accepted: 11/27/2023] [Indexed: 01/10/2024] Open
Abstract
de novo Drug Design (dnDD) aims to create new molecules that satisfy multiple conflicting objectives. Since several desired properties can be considered in the optimization process, dnDD is naturally categorized as a many-objective optimization problem (ManyOOP), where more than three objectives must be simultaneously optimized. However, a large number of objectives typically pose several challenges that affect the choice and the design of optimization methodologies. Herein, we cover the application of multi- and many-objective optimization methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential application in dnDD. Additionally, we comprehensively analyze how molecular properties used in the optimization process are applied as either objectives or constraints to the problem. Finally, we discuss future research in many-objective optimization for dnDD, highlighting two important possible impacts: i) its integration with the development of multi-target approaches to accelerate the discovery of innovative and more efficacious drug therapies and ii) its role as a catalyst for new developments in more fundamental and general methodological frameworks in the field.
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Affiliation(s)
| | | | | | - Laurent E. Dardenne
- Coordenação de Modelagem Computacional, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
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13
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Lu H, Wei Z, Wang X, Zhang K, Liu H. GraphGPT: A Graph Enhanced Generative Pretrained Transformer for Conditioned Molecular Generation. Int J Mol Sci 2023; 24:16761. [PMID: 38069085 PMCID: PMC10706000 DOI: 10.3390/ijms242316761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 11/16/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023] Open
Abstract
Condition-based molecular generation can generate a large number of molecules with particular properties, expanding the virtual drug screening library, and accelerating the process of drug discovery. In this study, we combined a molecular graph structure and sequential representations using a generative pretrained transformer (GPT) architecture for generating molecules conditionally. The incorporation of graph structure information facilitated a better comprehension of molecular topological features, and the augmentation of a sequential contextual understanding of GPT architecture facilitated molecular generation. The experiments indicate that our model efficiently produces molecules with the desired properties, with valid and unique metrics that are close to 100%. Faced with the typical task of generating molecules based on a scaffold in drug discovery, our model is able to preserve scaffold information and generate molecules with low similarity and specified properties.
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Affiliation(s)
| | | | | | | | - Hao Liu
- College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
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14
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Charles S, Mahapatra RK. Artificial intelligence based de-novo design for novel Plasmodium falciparum plasmepsin (PM) X inhibitors. J Biomol Struct Dyn 2023:1-16. [PMID: 37943000 DOI: 10.1080/07391102.2023.2279700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 10/27/2023] [Indexed: 11/10/2023]
Abstract
Plasmodium falciparum is the leading cause of malaria with 627,000 deaths annually. Invasion and egress are critical stages for successful infection of the host yet depend on proteins that are extensively pre-processed by various maturases. Plasmepsins (Plasmodium pepsins, abbreviated PM, I-X) are pepsin-like aspartic proteases that are involved in almost all stages of the life cycle. The goal of this study was to use de-novo generative modeling techniques to create novel potential PfPMX inhibitors. A total of 4325 compounds were virtually screened by structural-based docking methods. The obtained hits were utilized to refine a structure-based Ligand Neural Network (L-Net) generative model to generate related compounds. The obtained optimal L-Net Compounds with smina scores ≤ -5.00KCalmol-1 and QED ≥ 0.35 were further taken for amplification utilizing Ligand Based Transformer modeling using Deep generative learning (Drug Explorer/DrugEx). The resulting hits were then subjected to XP Glide conventional Molecular docking and QikProp ADMET screening; molecules with XP Docking score ≤ -7.00KCalmol-1 were retained. Based on their Glide ligand efficiency, originality, and uniqueness, 30 compounds were chosen for binding affinity and MM_GBSA energy determination. Following Induced Fit docking (IFD), 7 compounds were taken for 50 ns MD simulations and FEP/MD calculations. This study reported novel potential PfPMX inhibitors with acceptable ADMET profiles and reasonable synthetic accessibility scores, as well as sufficient docking scores against other PMs were generated. The PfPMX inhibitors reported in this article are promising antimalarials for the next stages of drug development, and the first of their kind to be investigated thoroughly.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ssemuyiga Charles
- School of Biotechnology, KIIT Deemed to be University, Bhubaneswar, Odisha, India
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15
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Mullowney MW, Duncan KR, Elsayed SS, Garg N, van der Hooft JJJ, Martin NI, Meijer D, Terlouw BR, Biermann F, Blin K, Durairaj J, Gorostiola González M, Helfrich EJN, Huber F, Leopold-Messer S, Rajan K, de Rond T, van Santen JA, Sorokina M, Balunas MJ, Beniddir MA, van Bergeijk DA, Carroll LM, Clark CM, Clevert DA, Dejong CA, Du C, Ferrinho S, Grisoni F, Hofstetter A, Jespers W, Kalinina OV, Kautsar SA, Kim H, Leao TF, Masschelein J, Rees ER, Reher R, Reker D, Schwaller P, Segler M, Skinnider MA, Walker AS, Willighagen EL, Zdrazil B, Ziemert N, Goss RJM, Guyomard P, Volkamer A, Gerwick WH, Kim HU, Müller R, van Wezel GP, van Westen GJP, Hirsch AKH, Linington RG, Robinson SL, Medema MH. Artificial intelligence for natural product drug discovery. Nat Rev Drug Discov 2023; 22:895-916. [PMID: 37697042 DOI: 10.1038/s41573-023-00774-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/20/2023] [Indexed: 09/13/2023]
Abstract
Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.
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Affiliation(s)
| | - Katherine R Duncan
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Somayah S Elsayed
- Department of Molecular Biotechnology, Institute of Biology, Leiden University, Leiden, The Netherlands
| | - Neha Garg
- School of Chemistry and Biochemistry, Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, GA, USA
| | - Justin J J van der Hooft
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
- Department of Biochemistry, University of Johannesburg, Johannesburg, South Africa
| | - Nathaniel I Martin
- Biological Chemistry Group, Institute of Biology, Leiden University, Leiden, The Netherlands
| | - David Meijer
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
| | - Barbara R Terlouw
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
| | - Friederike Biermann
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
- Institute of Molecular Bio Science, Goethe-University Frankfurt, Frankfurt am Main, Germany
- LOEWE Center for Translational Biodiversity Genomics (TBG), Frankfurt am Main, Germany
| | - Kai Blin
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Marina Gorostiola González
- Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
- ONCODE institute, Leiden, The Netherlands
| | - Eric J N Helfrich
- Institute of Molecular Bio Science, Goethe-University Frankfurt, Frankfurt am Main, Germany
- LOEWE Center for Translational Biodiversity Genomics (TBG), Frankfurt am Main, Germany
| | - Florian Huber
- Center for Digitalization and Digitality, Hochschule Düsseldorf, Düsseldorf, Germany
| | - Stefan Leopold-Messer
- Institut für Mikrobiologie, Eidgenössische Technische Hochschule (ETH) Zürich, Zürich, Switzerland
| | - Kohulan Rajan
- Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University Jena, Jena, Germany
| | - Tristan de Rond
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
| | - Jeffrey A van Santen
- Department of Chemistry, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Maria Sorokina
- Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller University, Jena, Germany
- Pharmaceuticals R&D, Bayer AG, Berlin, Germany
| | - Marcy J Balunas
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - Mehdi A Beniddir
- Équipe "Chimie des Substances Naturelles", Université Paris-Saclay, CNRS, BioCIS, Orsay, France
| | - Doris A van Bergeijk
- Department of Molecular Biotechnology, Institute of Biology, Leiden University, Leiden, The Netherlands
| | - Laura M Carroll
- Structural and Computational Biology Unit, EMBL, Heidelberg, Germany
| | - Chase M Clark
- Division of Pharmaceutical Sciences, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, USA
| | | | | | - Chao Du
- Department of Molecular Biotechnology, Institute of Biology, Leiden University, Leiden, The Netherlands
| | | | - Francesca Grisoni
- Institute for Complex Molecular Systems, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, Utrecht, The Netherlands
| | | | - Willem Jespers
- Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Olga V Kalinina
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarbrücken, Germany
- Drug Bioinformatics, Medical Faculty, Saarland University, Homburg, Germany
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
| | | | - Hyunwoo Kim
- College of Pharmacy and Integrated Research Institute for Drug Development, Dongguk University Seoul, Goyang-si, Republic of Korea
| | - Tiago F Leao
- Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Joleen Masschelein
- Center for Microbiology, VIB-KU Leuven, Heverlee, Belgium
- Department of Biology, KU Leuven, Heverlee, Belgium
| | - Evan R Rees
- Division of Pharmaceutical Sciences, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, USA
| | - Raphael Reher
- Institute of Pharmaceutical Biology and Biotechnology, University of Marburg, Marburg, Germany
- Institute of Pharmacy, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Daniel Reker
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Duke Microbiome Center, Duke University, Durham, NC, USA
| | - Philippe Schwaller
- Laboratory of Artificial Chemical Intelligence, Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Michael A Skinnider
- Adapsyn Bioscience, Hamilton, Ontario, Canada
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Allison S Walker
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
| | - Egon L Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands
| | - Barbara Zdrazil
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, UK
| | - Nadine Ziemert
- Interfaculty Institute for Microbiology and Infection Medicine Tuebingen (IMIT), Institute for Bioinformatics and Medical Informatics (IBMI), University of Tuebingen, Tuebingen, Germany
| | | | - Pierre Guyomard
- Bonsai team, CRIStAL - Centre de Recherche en Informatique Signal et Automatique de Lille, Université de Lille, Villeneuve d'Ascq Cedex, France
| | - Andrea Volkamer
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
- In silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - William H Gerwick
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
| | - Rolf Müller
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarbrücken, Germany
- Department of Pharmacy, Saarland University, Saarbrücken, Germany
- German Center for infection research (DZIF), Braunschweig, Germany
- Helmholtz International Lab for Anti-Infectives, Saarbrücken, Germany
| | - Gilles P van Wezel
- Department of Molecular Biotechnology, Institute of Biology, Leiden University, Leiden, The Netherlands
- Netherlands Institute of Ecology, NIOO-KNAW, Wageningen, The Netherlands
| | - Gerard J P van Westen
- Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden, The Netherlands.
| | - Anna K H Hirsch
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarbrücken, Germany.
- Department of Pharmacy, Saarland University, Saarbrücken, Germany.
- German Center for infection research (DZIF), Braunschweig, Germany.
- Helmholtz International Lab for Anti-Infectives, Saarbrücken, Germany.
| | - Roger G Linington
- Department of Chemistry, Simon Fraser University, Burnaby, British Columbia, Canada.
| | - Serina L Robinson
- Department of Environmental Microbiology, Eawag: Swiss Federal Institute for Aquatic Science and Technology, Dübendorf, Switzerland.
| | - Marnix H Medema
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.
- Institute of Biology, Leiden University, Leiden, The Netherlands.
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16
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Nguyen ATN, Tran QL, Baltos JA, McNeill SM, Nguyen DTN, May LT. Small molecule allosteric modulation of the adenosine A 1 receptor. Front Endocrinol (Lausanne) 2023; 14:1184360. [PMID: 37435481 PMCID: PMC10331460 DOI: 10.3389/fendo.2023.1184360] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 05/23/2023] [Indexed: 07/13/2023] Open
Abstract
G protein-coupled receptors (GPCRs) represent the target for approximately a third of FDA-approved small molecule drugs. The adenosine A1 receptor (A1R), one of four adenosine GPCR subtypes, has important (patho)physiological roles in humans. A1R has well-established roles in the regulation of the cardiovascular and nervous systems, where it has been identified as a potential therapeutic target for a number of conditions, including cardiac ischemia-reperfusion injury, cognition, epilepsy, and neuropathic pain. A1R small molecule drugs, typically orthosteric ligands, have undergone clinical trials. To date, none have progressed into the clinic, predominantly due to dose-limiting unwanted effects. The development of A1R allosteric modulators that target a topographically distinct binding site represent a promising approach to overcome current limitations. Pharmacological parameters of allosteric ligands, including affinity, efficacy and cooperativity, can be optimized to regulate A1R activity with high subtype, spatial and temporal selectivity. This review aims to offer insights into the A1R as a potential therapeutic target and highlight recent advances in the structural understanding of A1R allosteric modulation.
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Affiliation(s)
- Anh T. N. Nguyen
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia
| | - Quan L. Tran
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia
| | - Jo-Anne Baltos
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia
| | - Samantha M. McNeill
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia
| | - Diep T. N. Nguyen
- Department of Information Technology, Faculty of Engineering and Technology, Vietnam National University, Hanoi, Vietnam
| | - Lauren T. May
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia
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