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Guo Q, Fu B, Tian Y, Xu S, Meng X. Recent progress in artificial intelligence and machine learning for novel diabetes mellitus medications development. Curr Med Res Opin 2024; 40:1483-1493. [PMID: 39083361 DOI: 10.1080/03007995.2024.2387187] [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: 02/04/2024] [Accepted: 07/29/2024] [Indexed: 08/02/2024]
Abstract
Diabetes mellitus, stemming from either insulin resistance or inadequate insulin secretion, represents a complex ailment that results in prolonged hyperglycemia and severe complications. Patients endure severe ramifications such as kidney disease, vision impairment, cardiovascular disorders, and susceptibility to infections, leading to significant physical suffering and imposing substantial socio-economic burdens. This condition has evolved into an increasingly severe health crisis. There is an urgent need to develop new treatments with improved efficacy and fewer adverse effects to meet clinical demands. However, novel drug development is costly, time-consuming, and often associated with side effects and suboptimal efficacy, making it a major challenge. Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized drug development across its comprehensive lifecycle, spanning drug discovery, preclinical studies, clinical trials, and post-market surveillance. These technologies have significantly accelerated the identification of promising therapeutic candidates, optimized trial designs, and enhanced post-approval safety monitoring. Recent advances in AI, including data augmentation, interpretable AI, and integration of AI with traditional experimental methods, offer promising strategies for overcoming the challenges inherent in AI-based drug discovery. Despite these advancements, there exists a notable gap in comprehensive reviews detailing AI and ML applications throughout the entirety of developing medications for diabetes mellitus. This review aims to fill this gap by evaluating the impact and potential of AI and ML technologies at various stages of diabetes mellitus drug development. It does that by synthesizing current research findings and technological advances so as to effectively control diabetes mellitus and mitigate its far-reaching social and economic impacts. The integration of AI and ML promises to revolutionize diabetes mellitus treatment strategies, offering hope for improved patient outcomes and reduced healthcare burdens worldwide.
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Affiliation(s)
- Qi Guo
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
| | - Bo Fu
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
| | - Yuan Tian
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
| | - Shujun Xu
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
| | - Xin Meng
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
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2
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Aghahasani R, Shiri F, Kamaladiny H, Haddadi F, Pirhadi S. Hit discovery of potential CDK8 inhibitors and analysis of amino acid mutations for cancer therapy through computer-aided drug discovery. BMC Chem 2024; 18:73. [PMID: 38615023 PMCID: PMC11016228 DOI: 10.1186/s13065-024-01175-6] [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: 10/13/2023] [Accepted: 03/28/2024] [Indexed: 04/15/2024] Open
Abstract
Cyclin-dependent kinase 8 (CDK8) has emerged as a promising target for inhibiting cancer cell function, intensifying efforts towards the development of CDK8 inhibitors as potential cancer therapeutics. Mutations in CDK8, a protein kinase, are also implicated as a primary factor associated with tumor formation. In this study, we identified potential inhibitors through virtual screening for CDK8 and single amino acid mutations in CDK8, namely D173A (Aspartate 173 mutate to Alanine), D189N (Aspartate 189 mutate to Asparagine), T196A (Threonine 196 mutate to Alanine) and T196D (Threonine 196 mutate to Aspartate). Four databases (CHEMBEL, ZINC, MCULE, and MolPort) containing 65,209,131 molecules have been searched to identify new inhibitors for CDK8 and its single mutations. In the first step, structure-based pharmacophore modeling in the Pharmit server was used to select the compounds to know the inhibitors. Then molecules with better predicted drug-like molecule properties were selected. The final filter used to select more effective inhibitors among the previously selected molecules was molecular docking. Finally, 13 hits for CDK8, 11 hits for D173A, 11 hits for D189N, 15 hits for T196A, and 12 hits for T196D were considered potential inhibitors. A majority of the virtual screening hits exhibited satisfactorily predict pharmacokinetic characteristics and toxicity properties.
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Affiliation(s)
| | | | | | | | - Somayeh Pirhadi
- Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
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3
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Stevenson GA, Kirshner D, Bennion BJ, Yang Y, Zhang X, Zemla A, Torres MW, Epstein A, Jones D, Kim H, Bennett WFD, Wong SE, Allen JE, Lightstone FC. Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method. J Chem Inf Model 2023; 63:6655-6666. [PMID: 37847557 PMCID: PMC10647021 DOI: 10.1021/acs.jcim.3c00722] [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: 05/11/2023] [Indexed: 10/18/2023]
Abstract
Protein-ligand interactions are essential to drug discovery and drug development efforts. Desirable on-target or multitarget interactions are the first step in finding an effective therapeutic, while undesirable off-target interactions are the first step in assessing safety. In this work, we introduce a novel ligand-based featurization and mapping of human protein pockets to identify closely related protein targets and to project novel drugs into a hybrid protein-ligand feature space to identify their likely protein interactions. Using structure-based template matches from PDB, protein pockets are featured by the ligands that bind to their best co-complex template matches. The simplicity and interpretability of this approach provide a granular characterization of the human proteome at the protein-pocket level instead of the traditional protein-level characterization by family, function, or pathway. We demonstrate the power of this featurization method by clustering a subset of the human proteome and evaluating the predicted cluster associations of over 7000 compounds.
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Affiliation(s)
- Garrett A. Stevenson
- Computational
Engineering Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Dan Kirshner
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Brian J. Bennion
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Yue Yang
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Xiaohua Zhang
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Adam Zemla
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Marisa W. Torres
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Aidan Epstein
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Derek Jones
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
- Department
of Computer Science and Engineering, University
of California, San Diego, La Jolla, California 92093, United States
| | - Hyojin Kim
- Center
for Applied Scientific Computing, Lawrence
Livermore National Laboratory, Livermore, California 94550, United States
| | - W. F. Drew Bennett
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Sergio E. Wong
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Jonathan E. Allen
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Felice C. Lightstone
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
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4
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Shehzadi K, Saba A, Yu M, Liang J. Structure-Based Drug Design of RdRp Inhibitors against SARS-CoV-2. Top Curr Chem (Cham) 2023; 381:22. [PMID: 37318607 DOI: 10.1007/s41061-023-00432-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 05/15/2023] [Indexed: 06/16/2023]
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a worldwide pandemic since 2019, spreading rapidly and posing a significant threat to human health and life. With over 6 billion confirmed cases of the virus, the need for effective therapeutic drugs has become more urgent than ever before. RNA-dependent RNA polymerase (RdRp) is crucial in viral replication and transcription, catalysing viral RNA synthesis and serving as a promising therapeutic target for developing antiviral drugs. In this article, we explore the inhibition of RdRp as a potential treatment for viral diseases, analysing the structural information of RdRp in virus proliferation and summarizing the reported inhibitors' pharmacophore features and structure-activity relationship profiles. We hope that the information provided by this review will aid in structure-based drug design and aid in the global fight against SARS-CoV-2 infection.
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Affiliation(s)
- Kiran Shehzadi
- Key Laboratory of Medical Molecule Science and Pharmaceutical Engineering, Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, 10081, China
| | - Afsheen Saba
- Key Laboratory of Medical Molecule Science and Pharmaceutical Engineering, Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, 10081, China
| | - Mingjia Yu
- Key Laboratory of Medical Molecule Science and Pharmaceutical Engineering, Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, 10081, China.
| | - Jianhua Liang
- Key Laboratory of Medical Molecule Science and Pharmaceutical Engineering, Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, 10081, China.
- Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing, 314019, China.
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5
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Walesch S, Birkelbach J, Jézéquel G, Haeckl FPJ, Hegemann JD, Hesterkamp T, Hirsch AKH, Hammann P, Müller R. Fighting antibiotic resistance-strategies and (pre)clinical developments to find new antibacterials. EMBO Rep 2022; 24:e56033. [PMID: 36533629 PMCID: PMC9827564 DOI: 10.15252/embr.202256033] [Citation(s) in RCA: 56] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/23/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022] Open
Abstract
Antibacterial resistance is one of the greatest threats to human health. The development of new therapeutics against bacterial pathogens has slowed drastically since the approvals of the first antibiotics in the early and mid-20th century. Most of the currently investigated drug leads are modifications of approved antibacterials, many of which are derived from natural products. In this review, we highlight the challenges, advancements and current standing of the clinical and preclinical antibacterial research pipeline. Additionally, we present novel strategies for rejuvenating the discovery process and advocate for renewed and enthusiastic investment in the antibacterial discovery pipeline.
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Affiliation(s)
- Sebastian Walesch
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)SaarbrückenGermany,Department of PharmacySaarland UniversitySaarbrückenGermany,Helmholtz Centre for Infection research (HZI)BraunschweigGermany,German Center for infection research (DZIF)BraunschweigGermany
| | - Joy Birkelbach
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)SaarbrückenGermany,Department of PharmacySaarland UniversitySaarbrückenGermany,Helmholtz Centre for Infection research (HZI)BraunschweigGermany,German Center for infection research (DZIF)BraunschweigGermany
| | - Gwenaëlle Jézéquel
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)SaarbrückenGermany,Helmholtz Centre for Infection research (HZI)BraunschweigGermany
| | - F P Jake Haeckl
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)SaarbrückenGermany,Department of PharmacySaarland UniversitySaarbrückenGermany,Helmholtz Centre for Infection research (HZI)BraunschweigGermany,German Center for infection research (DZIF)BraunschweigGermany
| | - Julian D Hegemann
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)SaarbrückenGermany,Department of PharmacySaarland UniversitySaarbrückenGermany,Helmholtz Centre for Infection research (HZI)BraunschweigGermany,German Center for infection research (DZIF)BraunschweigGermany
| | - Thomas Hesterkamp
- Helmholtz Centre for Infection research (HZI)BraunschweigGermany,German Center for infection research (DZIF)BraunschweigGermany
| | - Anna K H Hirsch
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)SaarbrückenGermany,Department of PharmacySaarland UniversitySaarbrückenGermany,Helmholtz Centre for Infection research (HZI)BraunschweigGermany,German Center for infection research (DZIF)BraunschweigGermany,Helmholtz International Lab for Anti‐InfectivesSaarbrückenGermany
| | - Peter Hammann
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)SaarbrückenGermany,Department of PharmacySaarland UniversitySaarbrückenGermany,Helmholtz Centre for Infection research (HZI)BraunschweigGermany,German Center for infection research (DZIF)BraunschweigGermany
| | - Rolf Müller
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)SaarbrückenGermany,Department of PharmacySaarland UniversitySaarbrückenGermany,Helmholtz Centre for Infection research (HZI)BraunschweigGermany,German Center for infection research (DZIF)BraunschweigGermany,Helmholtz International Lab for Anti‐InfectivesSaarbrückenGermany
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6
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Gao K, Wang R, Chen J, Cheng L, Frishcosy J, Huzumi Y, Qiu Y, Schluckbier T, Wei X, Wei GW. Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2. Chem Rev 2022; 122:11287-11368. [PMID: 35594413 PMCID: PMC9159519 DOI: 10.1021/acs.chemrev.1c00965] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.
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Affiliation(s)
- Kaifu Gao
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Rui Wang
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Jiahui Chen
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Limei Cheng
- Clinical
Pharmacology and Pharmacometrics, Bristol
Myers Squibb, Princeton, New Jersey 08536, United States
| | - Jaclyn Frishcosy
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yuta Huzumi
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yuchi Qiu
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Tom Schluckbier
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Xiaoqi Wei
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Guo-Wei Wei
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Biochemistry and Molecular Biology, Michigan
State University, East Lansing, Michigan 48824, United States
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7
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Radaeva M, Dong X, Cherkasov A. The Use of Methods of Computer-Aided Drug Discovery in the Development of Topoisomerase II Inhibitors: Applications and Future Directions. J Chem Inf Model 2020; 60:3703-3721. [DOI: 10.1021/acs.jcim.0c00325] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Mariia Radaeva
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia V6H 3Z6, Canada
| | - Xuesen Dong
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia V6H 3Z6, Canada
| | - Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia V6H 3Z6, Canada
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8
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Mashayekh K, Sharifi S, Damghani T, Elyasi M, Avestan MS, Pirhadi S. Clustering and Sampling of the c-Met Conformational Space: A Computational Drug Discovery Study. Comb Chem High Throughput Screen 2019; 22:635-648. [PMID: 31696808 DOI: 10.2174/1386207322666191024103902] [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: 07/12/2019] [Revised: 10/08/2019] [Accepted: 10/16/2019] [Indexed: 12/22/2022]
Abstract
BACKGROUND c-Met kinase plays a critical role in a myriad of human cancers, and a massive scientific work was devoted to design more potent inhibitors. OBJECTIVE In this study, 16 molecular dynamics simulations of different complexes of potent c-Met inhibitors with U-shaped binding mode were carried out regarding the dynamic ensembles to design novel potent inhibitors. METHODS A cluster analysis was performed, and the most representative frame of each complex was subjected to the structure-based pharmacophore screening. The GOLD docking program investigated the interaction energy and pattern of output hits from the virtual screening. The most promising hits with the highest scoring values that showed critical interactions with c-Met were presented for ADME/Tox analysis. RESULTS The screening yielded 45,324 hits that all of them were subjected to the docking studies and 10 of them with the highest-scoring values having diverse structures were presented for ADME/Tox analyses. CONCLUSION The results indicated that all the hits shared critical Pi-Pi stacked and hydrogen bond interactions with Tyr1230 and Met1160 respectively.
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Affiliation(s)
- Korosh Mashayekh
- Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shahrzad Sharifi
- Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Tahereh Damghani
- Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.,School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Maryam Elyasi
- Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad S Avestan
- Department of Chemistry, University of Cincinnati, Cincinnati, OH 45221, United States
| | - Somayeh Pirhadi
- Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
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