1
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Gui H, Omiye JA, Chang CT, Daneshjou R. The Promises and Perils of Foundation Models in Dermatology. J Invest Dermatol 2024; 144:1440-1448. [PMID: 38441507 DOI: 10.1016/j.jid.2023.12.019] [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] [Received: 10/30/2023] [Revised: 12/19/2023] [Accepted: 12/20/2023] [Indexed: 06/24/2024]
Abstract
Foundation models (FM), which are large-scale artificial intelligence (AI) models that can complete a range of tasks, represent a paradigm shift in AI. These versatile models encompass large language models, vision-language models, and multimodal models. Although these models are often trained for broad tasks, they have been applied either out of the box or after additional fine tuning to tasks in medicine, including dermatology. From addressing administrative tasks to answering dermatology questions, these models are poised to have an impact on dermatology care delivery. As FMs become more ubiquitous in health care, it is important for clinicians and dermatologists to have a basic understanding of how these models are developed, what they are capable of, and what pitfalls exist. In this paper, we present a comprehensive yet accessible overview of the current state of FMs and summarize their current applications in dermatology, highlight their limitations, and discuss future developments in the field.
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Affiliation(s)
- Haiwen Gui
- Department of Dermatology, Stanford University, Stanford, California, USA.
| | - Jesutofunmi A Omiye
- Department of Dermatology, Stanford University, Stanford, California, USA; Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Crystal T Chang
- Department of Dermatology, Stanford University, Stanford, California, USA; Clinical Excellence Research Center, School of Medicine, Stanford University, Palo Alto, California, USA
| | - Roxana Daneshjou
- Department of Dermatology, Stanford University, Stanford, California, USA; Department of Biomedical Data Science, Stanford University, Stanford, California, USA
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2
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Hazemann J, Kimmerlin T, Lange R, Sweeney AM, Bourquin G, Ritz D, Czodrowski P. Identification of SARS-CoV-2 Mpro inhibitors through deep reinforcement learning for de novo drug design and computational chemistry approaches. RSC Med Chem 2024; 15:2146-2159. [PMID: 38911172 PMCID: PMC11187573 DOI: 10.1039/d4md00106k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 04/20/2024] [Indexed: 06/25/2024] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of coronavirus disease (COVID-19) since its emergence in December 2019. As of January 2024, there has been over 774 million reported cases and 7 million deaths worldwide. While vaccination efforts have been successful in reducing the severity of the disease and decreasing the transmission rate, the development of effective therapeutics against SARS-CoV-2 remains a critical need. The main protease (Mpro) of SARS-CoV-2 is an essential enzyme required for viral replication and has been identified as a promising target for drug development. In this study, we report the identification of novel Mpro inhibitors, using a combination of deep reinforcement learning for de novo drug design with 3D pharmacophore/shape-based alignment and privileged fragment match count scoring components followed by hit expansions and molecular docking approaches. Our experimentally validated results show that 3 novel series exhibit potent inhibitory activity against SARS-CoV-2 Mpro, with IC50 values ranging from 1.3 μM to 2.3 μM and a high degree of selectivity. These findings represent promising starting points for the development of new antiviral therapies against COVID-19.
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Affiliation(s)
- Julien Hazemann
- Physical Chemistry, Chemistry Department, Johannes Gutenberg University Duesbergweg 10-14 55128 Mainz Germany
- Drug Discovery Chemistry, Idorsia Pharmaceuticals Ltd. Hegenheimermattweg 91 4123 Allschwil Switzerland
| | - Thierry Kimmerlin
- Drug Discovery Chemistry, Idorsia Pharmaceuticals Ltd. Hegenheimermattweg 91 4123 Allschwil Switzerland
| | - Roland Lange
- Drug Discovery Chemistry, Idorsia Pharmaceuticals Ltd. Hegenheimermattweg 91 4123 Allschwil Switzerland
| | - Aengus Mac Sweeney
- Drug Discovery Chemistry, Idorsia Pharmaceuticals Ltd. Hegenheimermattweg 91 4123 Allschwil Switzerland
| | - Geoffroy Bourquin
- Drug Discovery Chemistry, Idorsia Pharmaceuticals Ltd. Hegenheimermattweg 91 4123 Allschwil Switzerland
| | - Daniel Ritz
- Drug Discovery Chemistry, Idorsia Pharmaceuticals Ltd. Hegenheimermattweg 91 4123 Allschwil Switzerland
| | - Paul Czodrowski
- Physical Chemistry, Chemistry Department, Johannes Gutenberg University Duesbergweg 10-14 55128 Mainz Germany
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3
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Altincekic N, Jores N, Löhr F, Richter C, Ehrhardt C, Blommers MJJ, Berg H, Öztürk S, Gande SL, Linhard V, Orts J, Abi Saad MJ, Bütikofer M, Kaderli J, Karlsson BG, Brath U, Hedenström M, Gröbner G, Sauer UH, Perrakis A, Langer J, Banci L, Cantini F, Fragai M, Grifagni D, Barthel T, Wollenhaupt J, Weiss MS, Robertson A, Bax A, Sreeramulu S, Schwalbe H. Targeting the Main Protease (M pro, nsp5) by Growth of Fragment Scaffolds Exploiting Structure-Based Methodologies. ACS Chem Biol 2024; 19:563-574. [PMID: 38232960 PMCID: PMC10877576 DOI: 10.1021/acschembio.3c00720] [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: 11/27/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 01/19/2024]
Abstract
The main protease Mpro, nsp5, of SARS-CoV-2 (SCoV2) is one of its most attractive drug targets. Here, we report primary screening data using nuclear magnetic resonance spectroscopy (NMR) of four different libraries and detailed follow-up synthesis on the promising uracil-containing fragment Z604 derived from these libraries. Z604 shows time-dependent binding. Its inhibitory effect is sensitive to reducing conditions. Starting with Z604, we synthesized and characterized 13 compounds designed by fragment growth strategies. Each compound was characterized by NMR and/or activity assays to investigate their interaction with Mpro. These investigations resulted in the four-armed compound 35b that binds directly to Mpro. 35b could be cocrystallized with Mpro revealing its noncovalent binding mode, which fills all four active site subpockets. Herein, we describe the NMR-derived fragment-to-hit pipeline and its application for the development of promising starting points for inhibitors of the main protease of SCoV2.
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Affiliation(s)
- Nadide Altincekic
- Institute
for Organic Chemistry and Chemical Biology, Goethe University Frankfurt am Main, D-60438 Frankfurt, Germany
- Center
of Biomolecular Magnetic Resonance (BMRZ), Goethe University Frankfurt am Main, D-60438 Frankfurt, Germany
| | - Nathalie Jores
- Institute
for Organic Chemistry and Chemical Biology, Goethe University Frankfurt am Main, D-60438 Frankfurt, Germany
- Center
of Biomolecular Magnetic Resonance (BMRZ), Goethe University Frankfurt am Main, D-60438 Frankfurt, Germany
| | - Frank Löhr
- Center
of Biomolecular Magnetic Resonance (BMRZ), Goethe University Frankfurt am Main, D-60438 Frankfurt, Germany
- Institute
of Biophysical Chemistry, Goethe University
Frankfurt am Main, D-60438 Frankfurt, Germany
| | - Christian Richter
- Institute
for Organic Chemistry and Chemical Biology, Goethe University Frankfurt am Main, D-60438 Frankfurt, Germany
- Center
of Biomolecular Magnetic Resonance (BMRZ), Goethe University Frankfurt am Main, D-60438 Frankfurt, Germany
| | - Claus Ehrhardt
- Department
of Biochemistry, University of Zurich, 8093 Zurich, Switzerland
| | | | - Hannes Berg
- Institute
for Organic Chemistry and Chemical Biology, Goethe University Frankfurt am Main, D-60438 Frankfurt, Germany
- Center
of Biomolecular Magnetic Resonance (BMRZ), Goethe University Frankfurt am Main, D-60438 Frankfurt, Germany
| | - Sare Öztürk
- Institute
for Organic Chemistry and Chemical Biology, Goethe University Frankfurt am Main, D-60438 Frankfurt, Germany
| | - Santosh L. Gande
- Institute
for Organic Chemistry and Chemical Biology, Goethe University Frankfurt am Main, D-60438 Frankfurt, Germany
- Center
of Biomolecular Magnetic Resonance (BMRZ), Goethe University Frankfurt am Main, D-60438 Frankfurt, Germany
| | - Verena Linhard
- Institute
for Organic Chemistry and Chemical Biology, Goethe University Frankfurt am Main, D-60438 Frankfurt, Germany
- Center
of Biomolecular Magnetic Resonance (BMRZ), Goethe University Frankfurt am Main, D-60438 Frankfurt, Germany
| | - Julien Orts
- Department
of Pharmaceutical Sciences, University of
Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
| | - Marie Jose Abi Saad
- Department
of Pharmaceutical Sciences, University of
Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
| | - Matthias Bütikofer
- Swiss
Federal Institute of Technology, Laboratory of Physical Chemistry, ETH Zurich, 8093 Zürich, Switzerland
| | - Janina Kaderli
- Swiss
Federal Institute of Technology, Laboratory of Physical Chemistry, ETH Zurich, 8093 Zürich, Switzerland
| | - B. Göran Karlsson
- Swedish
NMR Centre, Department of Chemistry and Molecular Biology, University of Gothenburg, SE40530 Göteborg, Sweden
- SciLifeLab, University of Gothenburg, SE40530 Göteborg, Sweden
| | - Ulrika Brath
- Swedish
NMR Centre, Department of Chemistry and Molecular Biology, University of Gothenburg, SE40530 Göteborg, Sweden
| | - Mattias Hedenström
- Swedish
NMR Centre, Department of Chemistry, University
of Umeå, SE-90187 Umeå, Sweden
| | - Gerhard Gröbner
- Swedish
NMR Centre, Department of Chemistry, University
of Umeå, SE-90187 Umeå, Sweden
| | - Uwe H. Sauer
- Protein
Production Sweden, Department of Chemistry, University of Umeå, SE-90187 Umeå, Sweden
| | - Anastassis Perrakis
- Oncode
Institute and Division of Biochemistry, The Netherlands Cancer Institute, 1066CX Amsterdam, The Netherlands
| | - Julian Langer
- Max Planck Institute of
Biophysics, D-60438 Frankfurt am Main, Germany
| | - Lucia Banci
- Magnetic
Resonance Center and Department of Chemistry, University of Florence, Via L. Sacconi 6, 50019 Sesto Fiorentino, Italy
- Consorzio
Interuniversitario Risonanze Magnetiche Metalloproteine, Via L. Sacconi 6, 50019 Sesto Fiorentino, Italy
| | - Francesca Cantini
- Magnetic
Resonance Center and Department of Chemistry, University of Florence, Via L. Sacconi 6, 50019 Sesto Fiorentino, Italy
- Consorzio
Interuniversitario Risonanze Magnetiche Metalloproteine, Via L. Sacconi 6, 50019 Sesto Fiorentino, Italy
| | - Marco Fragai
- Magnetic
Resonance Center and Department of Chemistry, University of Florence, Via L. Sacconi 6, 50019 Sesto Fiorentino, Italy
- Consorzio
Interuniversitario Risonanze Magnetiche Metalloproteine, Via L. Sacconi 6, 50019 Sesto Fiorentino, Italy
| | - Deborah Grifagni
- Magnetic
Resonance Center and Department of Chemistry, University of Florence, Via L. Sacconi 6, 50019 Sesto Fiorentino, Italy
| | - Tatjana Barthel
- Macromolecular
Crystallography, Helmholtz-Zentrum Berlin, Albert-Einstein-Str. 15, D-12489 Berlin, Germany
| | - Jan Wollenhaupt
- Macromolecular
Crystallography, Helmholtz-Zentrum Berlin, Albert-Einstein-Str. 15, D-12489 Berlin, Germany
| | - Manfred S. Weiss
- Macromolecular
Crystallography, Helmholtz-Zentrum Berlin, Albert-Einstein-Str. 15, D-12489 Berlin, Germany
| | | | - Adriaan Bax
- NIH, LCP NIDDK, Bethesda, Maryland 20892, United States
| | - Sridhar Sreeramulu
- Institute
for Organic Chemistry and Chemical Biology, Goethe University Frankfurt am Main, D-60438 Frankfurt, Germany
- Center
of Biomolecular Magnetic Resonance (BMRZ), Goethe University Frankfurt am Main, D-60438 Frankfurt, Germany
| | - Harald Schwalbe
- Institute
for Organic Chemistry and Chemical Biology, Goethe University Frankfurt am Main, D-60438 Frankfurt, Germany
- Center
of Biomolecular Magnetic Resonance (BMRZ), Goethe University Frankfurt am Main, D-60438 Frankfurt, Germany
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4
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Gao Z, Ding P, Xu R. IUPHAR review - Data-driven computational drug repurposing approaches for opioid use disorder. Pharmacol Res 2024; 199:106960. [PMID: 37832859 DOI: 10.1016/j.phrs.2023.106960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/08/2023] [Accepted: 10/10/2023] [Indexed: 10/15/2023]
Abstract
Opioid Use Disorder (OUD) is a chronic and relapsing condition characterized by the misuse of opioid drugs, causing significant morbidity and mortality in the United States. Existing medications for OUD are limited, and there is an immediate need to discover treatments with enhanced safety and efficacy. Drug repurposing aims to find new indications for existing medications, offering a time-saving and cost-efficient alternative strategy to traditional drug discovery. Computational approaches have been developed to further facilitate the drug repurposing process. In this paper, we reviewed state-of-the-art data-driven computational drug repurposing approaches for OUD and discussed their advantages and potential challenges. We also highlighted promising repurposed candidate drugs for OUD that were identified by computational drug repurposing techniques and reviewed studies supporting their potential mechanisms of action in treating OUD.
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Affiliation(s)
- Zhenxiang Gao
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Pingjian Ding
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Rong Xu
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
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5
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Qin R, Zhang H, Huang W, Shao Z, Lei J. Deep learning-based design and screening of benzimidazole-pyrazine derivatives as adenosine A 2B receptor antagonists. J Biomol Struct Dyn 2023:1-17. [PMID: 38133953 DOI: 10.1080/07391102.2023.2295974] [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: 09/16/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023]
Abstract
The Adenosine A2B receptor (A2BAR) is considered a novel potential target for the immunotherapy of cancer, and A2BAR antagonists have an inhibitory effect on tumor growth, proliferation, and metastasis. In our previous studies, we identified a class of benzimidazole-pyrazine scaffolds whose derivatives exhibited the antagonistic effect but lacked subtype selectivity towards A2BAR. In this work, we developed a scaffold-based protocol that incorporates a deep generative model and multilayer virtual screening to design benzimidazole-pyrazine derivatives as potential selective A2BAR antagonists. By utilizing a generative model with reported A2BAR antagonists as the training set, we built up a scaffold-focused library of benzimidazole-pyrazine derivatives and processed a virtual screening protocol to discover potential A2BAR antagonists. Finally, five molecules with different Bemis-Murcko scaffolds were identified and exhibited higher binding free energies than the reference molecule 12o. Further computational analysis revealed that the 3-benzyl derivative ABA-1266 presented high selectivity toward A2BAR and showed preferred draggability, providing future potent development of selective A2BAR antagonists.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Rui Qin
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Hao Zhang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
| | - Weifeng Huang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
| | - Zhenglin Shao
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
| | - Jinping Lei
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
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6
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Ali M, Park IH, Kim J, Kim G, Oh J, You JS, Kim J, Shin JS, Yoon SS. How Deep Learning in Antiviral Molecular Profiling Identified Anti-SARS-CoV-2 Inhibitors. Biomedicines 2023; 11:3134. [PMID: 38137356 PMCID: PMC10740425 DOI: 10.3390/biomedicines11123134] [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: 10/18/2023] [Revised: 11/15/2023] [Accepted: 11/22/2023] [Indexed: 12/24/2023] Open
Abstract
The integration of artificial intelligence (AI) into drug discovery has markedly advanced the search for effective therapeutics. In our study, we employed a comprehensive computational-experimental approach to identify potential anti-SARS-CoV-2 compounds. We developed a predictive model to assess the activities of compounds based on their structural features. This model screened a library of approximately 700,000 compounds, culminating in the selection of the top 100 candidates for experimental validation. In vitro assays on human intestinal epithelial cells (Caco-2) revealed that 19 of these compounds exhibited inhibitory activity. Notably, eight compounds demonstrated dose-dependent activity in Vero cell lines, with half-maximal effective concentration (EC50) values ranging from 1 μM to 7 μM. Furthermore, we utilized a clustering approach to pinpoint potential nucleoside analog inhibitors, leading to the discovery of two promising candidates: azathioprine and its metabolite, thioinosinic acid. Both compounds showed in vitro activity against SARS-CoV-2, with thioinosinic acid also significantly reducing viral loads in mouse lungs. These findings underscore the utility of AI in accelerating drug discovery processes.
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Affiliation(s)
- Mohammed Ali
- Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (M.A.); (J.K.); (G.K.); (J.O.); (J.S.Y.); (J.K.)
- Brain Korea 21 Project for Medical Sciences, Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - In Ho Park
- Department of Biomedical Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea;
- Institute of Immunology and Immunological Diseases, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Junebeom Kim
- Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (M.A.); (J.K.); (G.K.); (J.O.); (J.S.Y.); (J.K.)
- Brain Korea 21 Project for Medical Sciences, Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Gwanghee Kim
- Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (M.A.); (J.K.); (G.K.); (J.O.); (J.S.Y.); (J.K.)
- Brain Korea 21 Project for Medical Sciences, Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Jooyeon Oh
- Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (M.A.); (J.K.); (G.K.); (J.O.); (J.S.Y.); (J.K.)
- Brain Korea 21 Project for Medical Sciences, Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Jin Sun You
- Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (M.A.); (J.K.); (G.K.); (J.O.); (J.S.Y.); (J.K.)
- Brain Korea 21 Project for Medical Sciences, Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Jieun Kim
- Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (M.A.); (J.K.); (G.K.); (J.O.); (J.S.Y.); (J.K.)
- Institute of Immunology and Immunological Diseases, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Jeon-Soo Shin
- Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (M.A.); (J.K.); (G.K.); (J.O.); (J.S.Y.); (J.K.)
- Brain Korea 21 Project for Medical Sciences, Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
- Institute of Immunology and Immunological Diseases, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Sang Sun Yoon
- Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (M.A.); (J.K.); (G.K.); (J.O.); (J.S.Y.); (J.K.)
- Brain Korea 21 Project for Medical Sciences, Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
- Institute of Immunology and Immunological Diseases, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
- BioMe Inc., Seoul 02455, Republic of Korea
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