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Gomatam A, Hirlekar BU, Singh KD, Murty US, Dixit VA. Improved QSAR models for PARP-1 inhibition using data balancing, interpretable machine learning, and matched molecular pair analysis. Mol Divers 2024:10.1007/s11030-024-10809-9. [PMID: 38374474 DOI: 10.1007/s11030-024-10809-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/07/2024] [Indexed: 02/21/2024]
Abstract
The poly (ADP-ribose) polymerase-1 (PARP-1) enzyme is an important target in the treatment of breast cancer. Currently, treatment options include the drugs Olaparib, Niraparib, Rucaparib, and Talazoparib; however, these drugs can cause severe side effects including hematological toxicity and cardiotoxicity. Although in silico models for the prediction of PARP-1 activity have been developed, the drawbacks of these models include low specificity, a narrow applicability domain, and a lack of interpretability. To address these issues, a comprehensive machine learning (ML)-based quantitative structure-activity relationship (QSAR) approach for the informed prediction of PARP-1 activity is presented. Classification models built using the Synthetic Minority Oversampling Technique (SMOTE) for data balancing gave robust and predictive models based on the K-nearest neighbor algorithm (accuracy 0.86, sensitivity 0.88, specificity 0.80). Regression models were built on structurally congeneric datasets, with the models for the phthalazinone class and fused cyclic compounds giving the best performance. In accordance with the Organization for Economic Cooperation and Development (OECD) guidelines, a mechanistic interpretation is proposed using the Shapley Additive Explanations (SHAP) to identify the important topological features to differentiate between PARP-1 actives and inactives. Moreover, an analysis of the PARP-1 dataset revealed the prevalence of activity cliffs, which possibly negatively impacts the model's predictive performance. Finally, a set of chemical transformation rules were extracted using the matched molecular pair analysis (MMPA) which provided mechanistic insights and can guide medicinal chemists in the design of novel PARP-1 inhibitors.
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Affiliation(s)
- Anish Gomatam
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, (NIPER Guwahati), Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Govt. of India, Sila Katamur (Halugurisuk), Dist: Kamrup, P.O.: Changsari, Guwahati, Assam, 781101, India
| | - Bhakti Umesh Hirlekar
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, (NIPER Guwahati), Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Govt. of India, Sila Katamur (Halugurisuk), Dist: Kamrup, P.O.: Changsari, Guwahati, Assam, 781101, India
| | - Krishan Dev Singh
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, (NIPER Guwahati), Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Govt. of India, Sila Katamur (Halugurisuk), Dist: Kamrup, P.O.: Changsari, Guwahati, Assam, 781101, India
| | - Upadhyayula Suryanarayana Murty
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, (NIPER Guwahati), Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Govt. of India, Sila Katamur (Halugurisuk), Dist: Kamrup, P.O.: Changsari, Guwahati, Assam, 781101, India
| | - Vaibhav A Dixit
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, (NIPER Guwahati), Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Govt. of India, Sila Katamur (Halugurisuk), Dist: Kamrup, P.O.: Changsari, Guwahati, Assam, 781101, India.
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2
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Liang Z, Lin C, Tan G, Li J, He Y, Cai S. A low-cost machine learning framework for predicting drug-drug interactions based on fusion of multiple features and a parameter self-tuning strategy. Phys Chem Chem Phys 2024; 26:6300-6315. [PMID: 38305788 DOI: 10.1039/d4cp00039k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Poly-drug therapy is now recognized as a crucial treatment, and the analysis of drug-drug interactions (DDIs) offers substantial theoretical support and guidance for its implementation. Predicting potential DDIs using intelligent algorithms is an emerging approach in pharmacological research. However, the existing supervised models and deep learning-based techniques still have several limitations. This paper proposes a novel DDI analysis and prediction framework called the Multi-View Semi-supervised Graph-based (MVSG) framework, which provides a comprehensive judgment by integrating multiple DDI features and functions without any time-consuming training process. Unlike conventional approaches, MVSG can search for the most suitable similarity (or distance) measurement among DDI data and construct graph structures for each feature. By employing a parameter self-tuning strategy, MVSG fuses multiple graphs according to the contributions of features' information. The actual anticancer drug data are extracted from the authoritative public database for evaluating the effectiveness of our framework, including 904 drugs, 7730 DDI records and 19 types of drug interactions. Validation results indicate that the prediction is more accurate when multiple features are adopted by our framework. In comparison to conventional machine learning techniques, MVSG can achieve higher performance even with less labeled data and without a training process. Finally, MVSG is employed to narrow down the search for potential valuable combinations.
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Affiliation(s)
- Zexiao Liang
- School of Integrated Circuits, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China.
| | - Canxin Lin
- School of Computer Science and Technology, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China
| | - Guoliang Tan
- School of Automation, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China
| | - Jianzhong Li
- School of Integrated Circuits, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China.
| | - Yan He
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China
| | - Shuting Cai
- School of Integrated Circuits, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China.
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3
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Janin YL. On the origins of SARS-CoV-2 main protease inhibitors. RSC Med Chem 2024; 15:81-118. [PMID: 38283212 PMCID: PMC10809347 DOI: 10.1039/d3md00493g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 10/13/2023] [Indexed: 01/30/2024] Open
Abstract
In order to address the world-wide health challenge caused by the COVID-19 pandemic, the 3CL protease/SARS-CoV-2 main protease (SARS-CoV-2-Mpro) coded by its nsp5 gene became one of the biochemical targets for the design of antiviral drugs. In less than 3 years of research, 4 inhibitors of SARS-CoV-2-Mpro have actually been authorized for COVID-19 treatment (nirmatrelvir, ensitrelvir, leritrelvir and simnotrelvir) and more such as EDP-235, FB-2001 and STI-1558/Olgotrelvir or five undisclosed compounds (CDI-988, ASC11, ALG-097558, QLS1128 and H-10517) are undergoing clinical trials. This review is an attempt to picture this quite unprecedented medicinal chemistry feat and provide insights on how these cysteine protease inhibitors were discovered. Since many series of covalent SARS-CoV-2-Mpro inhibitors owe some of their origins to previous work on other proteases, we first provided a description of various inhibitors of cysteine-bearing human caspase-1 or cathepsin K, as well as inhibitors of serine proteases such as human dipeptidyl peptidase-4 or the hepatitis C protein complex NS3/4A. This is then followed by a description of the results of the approaches adopted (repurposing, structure-based and high throughput screening) to discover coronavirus main protease inhibitors.
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Affiliation(s)
- Yves L Janin
- Structure et Instabilité des Génomes (StrInG), Muséum National d'Histoire Naturelle, INSERM, CNRS, Alliance Sorbonne Université 75005 Paris France
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4
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Song L, Gao S, Ye B, Yang M, Cheng Y, Kang D, Yi F, Sun JP, Menéndez-Arias L, Neyts J, Liu X, Zhan P. Medicinal chemistry strategies towards the development of non-covalent SARS-CoV-2 M pro inhibitors. Acta Pharm Sin B 2024; 14:87-109. [PMID: 38239241 PMCID: PMC10792984 DOI: 10.1016/j.apsb.2023.08.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/10/2023] [Accepted: 08/02/2023] [Indexed: 01/22/2024] Open
Abstract
The main protease (Mpro) of SARS-CoV-2 is an attractive target in anti-COVID-19 therapy for its high conservation and major role in the virus life cycle. The covalent Mpro inhibitor nirmatrelvir (in combination with ritonavir, a pharmacokinetic enhancer) and the non-covalent inhibitor ensitrelvir have shown efficacy in clinical trials and have been approved for therapeutic use. Effective antiviral drugs are needed to fight the pandemic, while non-covalent Mpro inhibitors could be promising alternatives due to their high selectivity and favorable druggability. Numerous non-covalent Mpro inhibitors with desirable properties have been developed based on available crystal structures of Mpro. In this article, we describe medicinal chemistry strategies applied for the discovery and optimization of non-covalent Mpro inhibitors, followed by a general overview and critical analysis of the available information. Prospective viewpoints and insights into current strategies for the development of non-covalent Mpro inhibitors are also discussed.
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Affiliation(s)
- Letian Song
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Shenghua Gao
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Shenzhen Research Institute of Shandong University, Shenzhen 518057, China
| | - Bing Ye
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Mianling Yang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Yusen Cheng
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Dongwei Kang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Fan Yi
- The Key Laboratory of Infection and Immunity of Shandong Province, Department of Pharmacology, School of Basic Medical Sciences, Shandong University, Jinan 250012, China
| | - Jin-Peng Sun
- Key Laboratory Experimental Teratology of the Ministry of Education, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Luis Menéndez-Arias
- Centro de Biología Molecular “Severo Ochoa” (Consejo Superior de Investigaciones Científicas & Autonomous University of Madrid), Madrid 28049, Spain
| | - Johan Neyts
- KU Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, Leuven 3000, Belgium
| | - Xinyong Liu
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Peng Zhan
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
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5
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Arnaut ZA, Pinto SMA, Aroso RT, Amorim AS, Lobo CS, Schaberle FA, Pereira D, Núñez J, Nunes SCC, Pais AACC, Rodrigues-Santos P, de Almeida LP, Pereira MM, Arnaut LG. Selective, broad-spectrum antiviral photodynamic disinfection with dicationic imidazolyl chlorin photosensitizers. Photochem Photobiol Sci 2023; 22:2607-2620. [PMID: 37755667 DOI: 10.1007/s43630-023-00476-4] [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: 06/19/2023] [Accepted: 08/28/2023] [Indexed: 09/28/2023]
Abstract
The COVID-19 pandemic exposes our vulnerability to viruses that acquire the ability to infect our cells. Classical disinfection methods are limited by toxicity. Existing medicines performed poorly against SARS-CoV-2 because of their specificity to targets in different organisms. We address the challenge of mitigating known and prospective viral infections with a new photosensitizer for antimicrobial photodynamic therapy (aPDT). Photodynamic inactivation is based on local oxidative stress, which is particularly damaging to enveloped viruses. We synthesized a cationic imidazolyl chlorin that reduced by > 99.999% of the percentage inhibition of amplification of SARS-CoV-2 collected from patients at 0.2 µM concentration and 4 J cm-2. Similar results were obtained in the prevention of infection of human ACE2-expressing HEK293T cells by a pseudotyped lentiviral vector exhibiting the S protein of SARS-CoV-2 at its surface. No toxicity to human epidermal keratinocytes (HaCaT) cells was found under similar conditions. aPDT with this chlorin offers fast and safe broad-spectrum photodisinfection and can be repeated with low risk of resistance.
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Affiliation(s)
- Zoe A Arnaut
- Chemistry Department, CQC-IMS, University of Coimbra, 3004-535, Coimbra, Portugal
| | - Sara M A Pinto
- Chemistry Department, CQC-IMS, University of Coimbra, 3004-535, Coimbra, Portugal
| | - Rafael T Aroso
- Chemistry Department, CQC-IMS, University of Coimbra, 3004-535, Coimbra, Portugal
| | - Anita S Amorim
- Chemistry Department, CQC-IMS, University of Coimbra, 3004-535, Coimbra, Portugal
| | - Catarina S Lobo
- LaserLeap Technologies, R. Col. Júlio Veiga Simão, CTCV, Ed. B, 3025-307, Coimbra, Portugal
| | - Fabio A Schaberle
- Chemistry Department, CQC-IMS, University of Coimbra, 3004-535, Coimbra, Portugal
| | - Dina Pereira
- Center for Neuroscience and Cell Biology (CNC), University of Coimbra, 3004-517, Coimbra, Portugal
- Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal
| | - Jisette Núñez
- Center for Neuroscience and Cell Biology (CNC), University of Coimbra, 3004-517, Coimbra, Portugal
- Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal
| | - Sandra C C Nunes
- Chemistry Department, CQC-IMS, University of Coimbra, 3004-535, Coimbra, Portugal
| | - Alberto A C C Pais
- Chemistry Department, CQC-IMS, University of Coimbra, 3004-535, Coimbra, Portugal
| | - Paulo Rodrigues-Santos
- Center for Neuroscience and Cell Biology (CNC), University of Coimbra, 3004-517, Coimbra, Portugal
- Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal
- Laboratory of Immunology and Oncology, Center for Neuroscience and Cell Biology (CNC), University of Coimbra, Coimbra, Portugal
| | - Luis Pereira de Almeida
- Center for Neuroscience and Cell Biology (CNC), University of Coimbra, 3004-517, Coimbra, Portugal
- Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal
| | - Mariette M Pereira
- Chemistry Department, CQC-IMS, University of Coimbra, 3004-535, Coimbra, Portugal.
| | - Luis G Arnaut
- Chemistry Department, CQC-IMS, University of Coimbra, 3004-535, Coimbra, Portugal.
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6
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Katarzyna Lesiów M, Witwicki M, Tan NK, Graziotto ME, New EJ. Unravelling the Mystery of COVID-19 Pathogenesis: Spike Protein and Cu Can Synergize to Trigger ROS Production. Chemistry 2023; 29:e202301530. [PMID: 37414735 DOI: 10.1002/chem.202301530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/29/2023] [Accepted: 07/06/2023] [Indexed: 07/08/2023]
Abstract
The COVID-19 pandemic has had a devastating impact on global health, highlighting the need to understand how the SARS-CoV-2 virus damages the lungs in order to develop effective treatments. Recent research has shown that patients with COVID-19 experience severe oxidative damage to various biomolecules. We propose that the overproduction of reactive oxygen species (ROS) in SARS-CoV-2 infection involves an interaction between copper ions and the virus's spike protein. We tested two peptide fragments, Ac-ELDKYFKNH-NH2 (L1) and Ac-WSHPQFEK-NH2 (L2), derived from the spike protein of the Wuhan strain and the β variant, respectively, and found that they bind Cu(II) ions and form a three-nitrogen complexes at lung pH. Our research demonstrates that these complexes trigger the overproduction of ROS, which can break both DNA strands and transform DNA into its linear form. Using A549 cells, we demonstrated that ROS overproduction occurs in the mitochondria, not in the cytoplasm. Our findings highlight the importance of the interaction between copper ions and the virus's spike protein in the development of lung damage and may aid in the development of therapeutic procedures.
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Affiliation(s)
| | - Maciej Witwicki
- Faculty of Chemistry, University of Wrocław, F. Joliot-Curie 14, 50-383, Wrocław, Poland
| | - Nian Kee Tan
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
- Australian Research Council Centre of Excellence for, Innovations in Peptide and Protein Science, The University of Sydney, Sydney, NSW 2006, Australia
| | | | - Elizabeth Joy New
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
- Australian Research Council Centre of Excellence for, Innovations in Peptide and Protein Science, The University of Sydney, Sydney, NSW 2006, Australia
- Sydney Nano Institute, The University of Sydney, Sydney, NSW 2006, Australia
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7
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Ashraf FB, Akter S, Mumu SH, Islam MU, Uddin J. Bio-activity prediction of drug candidate compounds targeting SARS-Cov-2 using machine learning approaches. PLoS One 2023; 18:e0288053. [PMID: 37669264 PMCID: PMC10479925 DOI: 10.1371/journal.pone.0288053] [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: 02/22/2023] [Accepted: 06/18/2023] [Indexed: 09/07/2023] Open
Abstract
The SARS-CoV-2 3CLpro protein is one of the key therapeutic targets of interest for COVID-19 due to its critical role in viral replication, various high-quality protein crystal structures, and as a basis for computationally screening for compounds with improved inhibitory activity, bioavailability, and ADMETox properties. The ChEMBL and PubChem database contains experimental data from screening small molecules against SARS-CoV-2 3CLpro, which expands the opportunity to learn the pattern and design a computational model that can predict the potency of any drug compound against coronavirus before in-vitro and in-vivo testing. In this study, Utilizing several descriptors, we evaluated 27 machine learning classifiers. We also developed a neural network model that can correctly identify bioactive and inactive chemicals with 91% accuracy, on CheMBL data and 93% accuracy on combined data on both CheMBL and Pubchem. The F1-score for inactive and active compounds was 93% and 94%, respectively. SHAP (SHapley Additive exPlanations) on XGB classifier to find important fingerprints from the PaDEL descriptors for this task. The results indicated that the PaDEL descriptors were effective in predicting bioactivity, the proposed neural network design was efficient, and the Explanatory factor through SHAP correctly identified the important fingertips. In addition, we validated the effectiveness of our proposed model using a large dataset encompassing over 100,000 molecules. This research employed various molecular descriptors to discover the optimal one for this task. To evaluate the effectiveness of these possible medications against SARS-CoV-2, more in-vitro and in-vivo research is required.
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Affiliation(s)
- Faisal Bin Ashraf
- Department of Computer Science and Engineering, Brac University, Dhaka, Bangladesh
- Department of Computer Science and Engineering, University of California, Riverside, California, United States of America
| | - Sanjida Akter
- Department of Cell Molecular and Developmental Biology, University of California, Riverside, California, United States of America
| | - Sumona Hoque Mumu
- School of Kinesiology, University of Louisiana at Lafayette, Lafayette, Louisiana, United States of America
| | - Muhammad Usama Islam
- School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, Louisiana, United States of America
| | - Jasim Uddin
- Department of Applied Computing and Engineering, Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Wales, United Kingdom
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8
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Xu T, Li S, Li AJ, Zhao J, Sakamuru S, Huang W, Xia M, Huang R. Identification of Potent and Selective Acetylcholinesterase/Butyrylcholinesterase Inhibitors by Virtual Screening. J Chem Inf Model 2023; 63:2321-2330. [PMID: 37011147 PMCID: PMC10688023 DOI: 10.1021/acs.jcim.3c00230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
Abstract
Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) play important roles in human neurodegenerative disorders such as Alzheimer's disease. In this study, machine learning methods were applied to develop quantitative structure-activity relationship models for the prediction of novel AChE and BChE inhibitors based on data from quantitative high-throughput screening assays. The models were used to virtually screen an in-house collection of ∼360K compounds. The optimal models achieved good performance with area under the receiver operating characteristic curve values ranging from 0.83 ± 0.03 to 0.87 ± 0.01 for the prediction of AChE/BChE inhibition activity and selectivity. Experimental validation showed that the best-performing models increased the assay hit rate by several folds. We identified 88 novel AChE and 126 novel BChE inhibitors, 25% (AChE) and 53% (BChE) of which showed potent inhibitory effects (IC50 < 5 μM). In addition, structure-activity relationship analysis of the BChE inhibitors revealed scaffolds for chemistry design and optimization. In conclusion, machine learning models were shown to efficiently identify potent and selective inhibitors against AChE and BChE and novel structural series for further design and development of potential therapeutics against neurodegenerative disorders.
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Affiliation(s)
- Tuan Xu
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Shuaizhang Li
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Andrew J. Li
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Jinghua Zhao
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Srilatha Sakamuru
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Wenwei Huang
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Menghang Xia
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Ruili Huang
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
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9
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Xu T, Kabir M, Sakamuru S, Shah P, Padilha E, Ngan DK, Xia M, Xu X, Simeonov A, Huang R. Predictive Models for Human Cytochrome P450 3A7 Selective Inhibitors and Substrates. J Chem Inf Model 2023; 63:846-855. [PMID: 36719788 PMCID: PMC10664139 DOI: 10.1021/acs.jcim.2c01516] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Inappropriate use of prescription drugs is potentially more harmful in fetuses/neonates than in adults. Cytochrome P450 (CYP) 3A subfamily undergoes developmental changes in expression, such as a transition from CYP3A7 to CYP3A4 shortly after birth, which provides a potential way to distinguish medication effects on fetuses/neonates and adults. The purpose of this study was to build first-in-class predictive models for both inhibitors and substrates of CYP3A7/CYP3A4 using chemical structure analysis. Three metrics were used to evaluate model performance: area under the receiver operating characteristic curve (AUC-ROC), balanced accuracy (BA), and Matthews correlation coefficient (MCC). The performance varied for each CYP3A7/CYP3A4 inhibitor/substrate model depending on the data set type, model type, rebalancing method, and specific feature set. For the active inhibitor/substrate data set, the optimal models achieved AUC-ROC values ranging from 0.77 ± 0.01 to 0.84 ± 0.01. For the selective inhibitor/substrate data set, the optimal models achieved AUC-ROC values ranging from 0.72 ± 0.02 to 0.79 ± 0.04. The predictive power of the optimal models was validated by compounds with known potencies as CYP3A7/CYP3A4 inhibitors or substrates. In addition, we identified structural features significant for CYP3A7/CYP3A4 selective or common inhibitors and substrates. In summary, the top performing models can be further applied as a tool to rapidly evaluate the safety and efficacy of new drugs separately for fetuses/neonates and adults. The significant structural features could guide the design of new therapeutic drugs as well as aid in the optimization of existing medicine for fetuses/neonates.
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Affiliation(s)
- Tuan Xu
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Md Kabir
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
- The Graduate School of Biomedical Sciences, Departments of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Srilatha Sakamuru
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Pranav Shah
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Elias Padilha
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Deborah K. Ngan
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Menghang Xia
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Xin Xu
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Anton Simeonov
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Ruili Huang
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
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10
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In Silico Identification of Anti-SARS-CoV-2 Medicinal Plants Using Cheminformatics and Machine Learning. MOLECULES (BASEL, SWITZERLAND) 2022; 28:molecules28010208. [PMID: 36615401 PMCID: PMC9821958 DOI: 10.3390/molecules28010208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/17/2022] [Accepted: 12/23/2022] [Indexed: 12/28/2022]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of COVID-19, is spreading rapidly and has caused hundreds of millions of infections and millions of deaths worldwide. Due to the lack of specific vaccines and effective treatments for COVID-19, there is an urgent need to identify effective drugs. Traditional Chinese medicine (TCM) is a valuable resource for identifying novel anti-SARS-CoV-2 drugs based on the important contribution of TCM and its potential benefits in COVID-19 treatment. Herein, we aimed to discover novel anti-SARS-CoV-2 compounds and medicinal plants from TCM by establishing a prediction method of anti-SARS-CoV-2 activity using machine learning methods. We first constructed a benchmark dataset from anti-SARS-CoV-2 bioactivity data collected from the ChEMBL database. Then, we established random forest (RF) and support vector machine (SVM) models that both achieved satisfactory predictive performance with AUC values of 0.90. By using this method, a total of 1011 active anti-SARS-CoV-2 compounds were predicted from the TCMSP database. Among these compounds, six compounds with highly potent activity were confirmed in the anti-SARS-CoV-2 experiments. The molecular fingerprint similarity analysis revealed that only 24 of the 1011 compounds have high similarity to the FDA-approved antiviral drugs, indicating that most of the compounds were structurally novel. Based on the predicted anti-SARS-CoV-2 compounds, we identified 74 anti-SARS-CoV-2 medicinal plants through enrichment analysis. The 74 plants are widely distributed in 68 genera and 43 families, 14 of which belong to antipyretic detoxicate plants. In summary, this study provided several medicinal plants with potential anti-SARS-CoV-2 activity, which offer an attractive starting point and a broader scope to mine for potentially novel anti-SARS-CoV-2 drugs.
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Hu F, Wang D, Huang H, Hu Y, Yin P. Bridging the Gap between Target-Based and Cell-Based Drug Discovery with a Graph Generative Multitask Model. J Chem Inf Model 2022; 62:6046-6056. [PMID: 36401569 DOI: 10.1021/acs.jcim.2c01180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The development of new drugs is crucial for protecting humans from disease. In the past several decades, target-based screening has been one of the most popular methods for developing new drugs. This method efficiently screens potential inhibitors of a target protein in vitro, but it frequently fails in vivo due to insufficient activity of the selected drugs. There is a need for accurate computational methods to bridge this gap. Here, we present a novel graph multi-task deep learning model to identify compounds with both target inhibitory and cell active (MATIC) properties. On a carefully curated SARS-CoV-2 data set, the proposed MATIC model shows advantages compared with the traditional method in screening effective compounds in vivo. Following this, we investigated the interpretability of the model and discovered that the learned features for target inhibition (in vitro) or cell active (in vivo) tasks are different with molecular property correlations and atom functional attention. Based on these findings, we utilized a Monte Carlo-based reinforcement learning generative model to generate novel multiproperty compounds with both in vitro and in vivo efficacy, thus bridging the gap between target-based and cell-based drug discovery. The tool is freely accessible at https://github.com/SIAT-code/MATIC.
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Affiliation(s)
- Fan Hu
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen518055, China
| | - Dongqi Wang
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen518055, China
| | - Huazhen Huang
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen518055, China
| | - Yishen Hu
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen518055, China
| | - Peng Yin
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen518055, China
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Jiang J, Zhang R, Ma J, Liu Y, Yang E, Du S, Zhao Z, Yuan Y. TranGRU: focusing on both the local and global information of molecules for molecular property prediction. APPL INTELL 2022; 53:15246-15260. [PMID: 36405344 PMCID: PMC9662124 DOI: 10.1007/s10489-022-04280-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/17/2022] [Indexed: 11/16/2022]
Abstract
Molecular property prediction is an essential but challenging task in drug discovery. The recurrent neural network (RNN) and Transformer are the mainstream methods for sequence modeling, and both have been successfully applied independently for molecular property prediction. As the local information and global information of molecules are very important for molecular properties, we aim to integrate the bi-directional gated recurrent unit (BiGRU) into the original Transformer encoder, together with self-attention to better capture local and global molecular information simultaneously. To this end, we propose the TranGRU approach, which encodes the local and global information of molecules by using the BiGRU and self-attention, respectively. Then, we use a gate mechanism to reasonably fuse the two molecular representations. In this way, we enhance the ability of the proposed model to encode both local and global molecular information. Compared to the baselines and state-of-the-art methods when treating each task as a single-task classification on Tox21, the proposed approach outperforms the baselines on 9 out of 12 tasks and state-of-the-art methods on 5 out of 12 tasks. TranGRU also obtains the best ROC-AUC scores on BBBP, FDA, LogP, and Tox21 (multitask classification) and has a comparable performance on ToxCast, BACE, and ecoli. On the whole, TranGRU achieves better performance for molecular property prediction. The source code is available in GitHub: https://github.com/Jiangjing0122/TranGRU.
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Affiliation(s)
- Jing Jiang
- School of Information Science and Engineering, Lanzhou University, Tianshui Road, Lanzhou, 730000 Gansu China
- Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Baiyin Road, Lanzhou, 730030 Gansu China
| | - Ruisheng Zhang
- School of Information Science and Engineering, Lanzhou University, Tianshui Road, Lanzhou, 730000 Gansu China
| | - Jun Ma
- School of Information Science and Engineering, Lanzhou University, Tianshui Road, Lanzhou, 730000 Gansu China
| | - Yunwu Liu
- School of Information Science and Engineering, Lanzhou University, Tianshui Road, Lanzhou, 730000 Gansu China
| | - Enjie Yang
- School of Information Science and Engineering, Lanzhou University, Tianshui Road, Lanzhou, 730000 Gansu China
| | - Shikang Du
- School of Information Science and Engineering, Lanzhou University, Tianshui Road, Lanzhou, 730000 Gansu China
| | - Zhili Zhao
- School of Information Science and Engineering, Lanzhou University, Tianshui Road, Lanzhou, 730000 Gansu China
| | - Yongna Yuan
- School of Information Science and Engineering, Lanzhou University, Tianshui Road, Lanzhou, 730000 Gansu China
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Shekh S, Moi S, Gowd KH. Virtual screening of sulfur compounds of Allium against coronavirus proteases: E-Ajoene is a potential dual protease targeting covalent inhibitor. J Sulphur Chem 2022. [DOI: 10.1080/17415993.2022.2119086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Shamasoddin Shekh
- Department of Chemistry, School of Chemical Sciences, Central University of Karnataka, Kalaburagi, India
| | - Smriti Moi
- Department of Chemistry, School of Chemical Sciences, Central University of Karnataka, Kalaburagi, India
| | - Konkallu Hanumae Gowd
- Department of Chemistry, School of Chemical Sciences, Central University of Karnataka, Kalaburagi, India
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Nepali K, Sharma R, Sharma S, Thakur A, Liou JP. Beyond the vaccines: a glance at the small molecule and peptide-based anti-COVID19 arsenal. J Biomed Sci 2022; 29:65. [PMID: 36064696 PMCID: PMC9444709 DOI: 10.1186/s12929-022-00847-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 08/16/2022] [Indexed: 02/08/2023] Open
Abstract
Unprecedented efforts of the researchers have been witnessed in the recent past towards the development of vaccine platforms for the control of the COVID-19 pandemic. Albeit, vaccination stands as a practical strategy to prevent SARS-CoV-2 infection, supplementing the anti-COVID19 arsenal with therapeutic options such as small molecules/peptides and antibodies is being conceived as a prudent strategy to tackle the emerging SARS-CoV-2 variants. Noteworthy to mention that collective efforts from numerous teams have led to the generation of a voluminous library composed of chemically and mechanistically diverse small molecules as anti-COVID19 scaffolds. This review article presents an overview of medicinal chemistry campaigns and drug repurposing programs that culminated in the identification of a plethora of small molecule-based anti-COVID19 drugs mediating their antiviral effects through inhibition of proteases, S protein, RdRp, ACE2, TMPRSS2, cathepsin and other targets. In light of the evidence ascertaining the potential of small molecule drugs to approach conserved proteins required for the viral replication of all coronaviruses, accelerated FDA approvals are anticipated for small molecules for the treatment of COVID19 shortly. Though the recent attempts invested in this direction in pursuit of enrichment of the anti-COVID-19 armoury (chemical tools) are praiseworthy, some strategies need to be implemented to extract conclusive benefits of the recently reported small molecule viz. (i) detailed preclinical investigation of the generated anti-COVID19 scaffolds (ii) in-vitro profiling of the inhibitors against the emerging SARS-CoV-2 variants (iii) development of assays enabling rapid screening of the libraries of anti-COVID19 scaffold (iv) leveraging the applications of machine learning based predictive models to expedite the anti-COVID19 drug discovery campaign (v) design of antibody-drug conjugates.
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Affiliation(s)
- Kunal Nepali
- School of Pharmacy, College of Pharmacy, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan
- TMU Research Center for Drug Discovery, Taipei Medical University, Taipei, 11031, Taiwan
| | - Ram Sharma
- School of Pharmacy, College of Pharmacy, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan
| | - Sachin Sharma
- School of Pharmacy, College of Pharmacy, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan
| | - Amandeep Thakur
- School of Pharmacy, College of Pharmacy, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan
| | - Jing-Ping Liou
- School of Pharmacy, College of Pharmacy, Taipei Medical University, 250 Wuxing Street, Taipei, 11031, Taiwan.
- TMU Research Center for Drug Discovery, Taipei Medical University, Taipei, 11031, Taiwan.
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