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Zhang Q, He Y, Lu YP, Wei QH, Zhang HY, Quan Y. GETdb: A comprehensive database for genetic and evolutionary features of drug targets. Comput Struct Biotechnol J 2024; 23:1429-1438. [PMID: 38616961 PMCID: PMC11015738 DOI: 10.1016/j.csbj.2024.04.006] [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: 11/07/2023] [Revised: 03/25/2024] [Accepted: 04/01/2024] [Indexed: 04/16/2024] Open
Abstract
The development of an innovative drug is complex and time-consuming, and the drug target identification is one of the critical steps in drug discovery process. Effective and accurate identification of drug targets can accelerate the drug development process. According to previous research, evolutionary and genetic information of genes has been found to facilitate the identification of approved drug targets. In addition, allosteric proteins have great potential as targets due to their structural diversity. However, this information that could facilitate target identification has not been collated in existing drug target databases. Here, we construct a comprehensive drug target database named Genetic and Evolutionary features of drug Targets database (GETdb, http://zhanglab.hzau.edu.cn/GETdb/page/index.jsp). This database not only integrates and standardizes data from dozens of commonly used drug and target databases, but also innovatively includes the genetic and evolutionary information of targets. Moreover, this database features an effective allosteric protein prediction model. GETdb contains approximately 4000 targets and over 29,000 drugs, and is a user-friendly database for searching, browsing and downloading data to facilitate the development of novel targets.
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Affiliation(s)
- Qi Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Yang He
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Ya-Ping Lu
- Sinopharm Genomics Technology Co., Ltd., Wuhan 430030, PR China
- Sinopharm Medical Laboratory (Wuhan) Co., Ltd., Wuhan 430030, PR China
| | - Qi-Hao Wei
- Sinopharm (Wuhan) Precision Medical Technology Co., Ltd., Wuhan 430030, PR China
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Yuan Quan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, PR China
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Liang Z, Wang J, Zhang H, Gao L, Xu J, Li P, Yang J, Fu X, Duan H, Liu J, Liu T, Ma W, Wu K. Peptide S4 is an entry inhibitor of SARS-CoV-2 infection. Virology 2024; 597:110149. [PMID: 38917689 DOI: 10.1016/j.virol.2024.110149] [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: 03/11/2024] [Revised: 06/07/2024] [Accepted: 06/18/2024] [Indexed: 06/27/2024]
Abstract
Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a significant socioeconomic burden, and combating COVID-19 is imperative. Blocking the SARS-CoV-2 RBD-ACE2 interaction is a promising therapeutic approach for viral infections, as SARS-CoV-2 binds to the ACE2 receptors of host cells via the RBD of spike proteins to infiltrate these cells. We used computer-aided drug design technology and cellular experiments to screen for peptide S4 with high affinity and specificity for the human ACE2 receptor through structural analysis of SARS-CoV-2 and ACE2 interactions. Cellular experiments revealed that peptide S4 effectively inhibited SARS-CoV-2 and HCoV-NL63 viruses from infecting host cells and was safe for cells at effective concentrations. Based on these findings, peptide S4 may be a potential pharmaceutical agent for clinical application in the treatment of the ongoing SARS-CoV-2 pandemic.
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Affiliation(s)
- Zhiyu Liang
- Department of Pathogen Biology, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China; Department of Microbiology, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Jiamei Wang
- Department of Microbiology, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Huan Zhang
- Guangdong Center for Disease Control and Prevention, Guangdong, China
| | - Lixia Gao
- Department of Microbiology, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Jun Xu
- College of Pharmacy, Jinan University, Guangzhou, China
| | - Peiran Li
- Department of Microbiology, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Jie Yang
- Department of Microbiology, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Xinting Fu
- Department of Microbiology, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Han Duan
- Department of Microbiology, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Jiayan Liu
- Department of Pathogen Biology, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China; Institute of Antibody Engineering, School of Laboratory Medicine & Biotechnology, Southern Medical University, Guangzhou, China
| | - Tiancai Liu
- Institute of Antibody Engineering, School of Laboratory Medicine & Biotechnology, Southern Medical University, Guangzhou, China
| | - Weifeng Ma
- Department of Microbiology, School of Public Health, Southern Medical University, Guangzhou, 510515, China.
| | - Kun Wu
- Department of Pathogen Biology, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China.
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Chen Z, Li N, Zhang P, Li Y, Li X. CardioDPi: An explainable deep-learning model for identifying cardiotoxic chemicals targeting hERG, Cav1.2, and Nav1.5 channels. JOURNAL OF HAZARDOUS MATERIALS 2024; 474:134724. [PMID: 38805819 DOI: 10.1016/j.jhazmat.2024.134724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/08/2024] [Accepted: 05/22/2024] [Indexed: 05/30/2024]
Abstract
The cardiotoxic effects of various pollutants have been a growing concern in environmental and material science. These effects encompass arrhythmias, myocardial injury, cardiac insufficiency, and pericardial inflammation. Compounds such as organic solvents and air pollutants disrupt the potassium, sodium, and calcium ion channels cardiac cell membranes, leading to the dysregulation of cardiac function. However, current cardiotoxicity models have disadvantages of incomplete data, ion channels, interpretability issues, and inability of toxic structure visualization. Herein, an interpretable deep-learning model known as CardioDPi was developed, which is capable of discriminating cardiotoxicity induced by the human Ether-à-go-go-related gene (hERG) channel, sodium channel (Na_v1.5), and calcium channel (Ca_v1.5) blockade. External validation yielded promising area under the ROC curve (AUC) values of 0.89, 0.89, and 0.94 for the hERG, Na_v1.5, and Ca_v1.5 channels, respectively. The CardioDPi can be freely accessed on the web server CardioDPipredictor (http://cardiodpi.sapredictor.cn/). Furthermore, the structural characteristics of cardiotoxic compounds were analyzed and structural alerts (SAs) can be extracted using the user-friendly CardioDPi-SAdetector web service (http://cardiosa.sapredictor.cn/). CardioDPi is a valuable tool for identifying cardiotoxic chemicals that are environmental and health risks. Moreover, the SA system provides essential insights for mode-of-action studies concerning cardiotoxic compounds.
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Affiliation(s)
- Zhaoyang Chen
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Na Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Pei Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Yan Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China.
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Hossain FMA, Bappy MNI, Robin TB, Ahmad I, Patel H, Jahan N, Rabbi MGR, Roy A, Chowdhury W, Ahmed N, Prome AA, Rani NA, Khan P, Zinnah KMA. A review on computational studies and bioinformatics analysis of potential drugs against monkeypox virus. J Biomol Struct Dyn 2024; 42:6091-6107. [PMID: 37403283 DOI: 10.1080/07391102.2023.2231542] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 06/23/2023] [Indexed: 07/06/2023]
Abstract
Monkeypox, a viral disease that is caused by monkeypox virus and occurs mainly in central and western Africa. However, recently it is spreading worldwide and took the focus of the scientific world towards it. Therefore, we made an attempt to cluster all the related information that may make it easy for the researchers to get the information easily and carry out their research smoothly to find prophylaxis against this emerging virus. There are very few researches found available on monkeypox. Almost all the studies were focused on smallpox virus and the recommended vaccines and therapeutics for monkeypox virus were originally developed for smallpox virus. Though these are recommended for emergency cases, they are not fully effective and specific against monkeypox. For this, here we also took the help of bioinformatics tools to screen potential drug candidates against this growing burden. Some potential antiviral plant metabolites, inhibitors and available drugs were scrutinized that can block the essential survival proteins of this virus. All the compounds Amentoflavone, Pseudohypericin, Adefovirdipiboxil, Fialuridin, Novobiocin and Ofloxacin showed elite binding efficiency with suitable ADME properties and Amentoflavone and Pseudohypericin showed stability in MD simulation study indicating their potency as probable drugs against this emerging virus.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ferdaus Mohd Altaf Hossain
- Faculty of Veterinary, Animal and Biomedical Science, Sylhet Agricultural University, Sylhet, Bangladesh
- Department of Dairy Science, Sylhet Agricultural University, Sylhet, Bangladesh
| | - Md Nazmul Islam Bappy
- Faculty of Biotechnology and Genetic Engineering, Sylhet Agricultural University, Sylhet, Bangladesh
- Department of Animal and Fish Biotechnology, Sylhet Agricultural University, Sylhet, Bangladesh
| | - Tanjin Barketullah Robin
- Faculty of Biotechnology and Genetic Engineering, Sylhet Agricultural University, Sylhet, Bangladesh
| | - Iqrar Ahmad
- Department of Pharmaceutical Chemistry, Prof. Ravindra Nikam College of Pharmacy, Dhule, Maharashtra, India
| | - Harun Patel
- Department of Pharmaceutical Chemistry, Division of Computer Aided Drug Design, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Maharashtra, India
| | - Nusrat Jahan
- Faculty of Biotechnology and Genetic Engineering, Sylhet Agricultural University, Sylhet, Bangladesh
| | - Md Gulam Rabbany Rabbi
- Faculty of Biotechnology and Genetic Engineering, Sylhet Agricultural University, Sylhet, Bangladesh
| | - Anindita Roy
- Faculty of Biotechnology and Genetic Engineering, Sylhet Agricultural University, Sylhet, Bangladesh
| | - Wasima Chowdhury
- Faculty of Biotechnology and Genetic Engineering, Sylhet Agricultural University, Sylhet, Bangladesh
| | - Nadim Ahmed
- Faculty of Biotechnology and Genetic Engineering, Sylhet Agricultural University, Sylhet, Bangladesh
| | - Anindita Ash Prome
- Faculty of Biotechnology and Genetic Engineering, Sylhet Agricultural University, Sylhet, Bangladesh
| | - Nurul Amin Rani
- Faculty of Biotechnology and Genetic Engineering, Sylhet Agricultural University, Sylhet, Bangladesh
| | - Parvez Khan
- Department of Biochemistry & Molecular Biology, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Kazi Md Ali Zinnah
- Faculty of Biotechnology and Genetic Engineering, Sylhet Agricultural University, Sylhet, Bangladesh
- Department of Animal and Fish Biotechnology, Sylhet Agricultural University, Sylhet, Bangladesh
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Fan Z, Yu J, Zhang X, Chen Y, Sun S, Zhang Y, Chen M, Xiao F, Wu W, Li X, Zheng M, Luo X, Wang D. Reducing overconfident errors in molecular property classification using Posterior Network. PATTERNS (NEW YORK, N.Y.) 2024; 5:100991. [PMID: 39005492 PMCID: PMC11240180 DOI: 10.1016/j.patter.2024.100991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/20/2023] [Accepted: 04/15/2024] [Indexed: 07/16/2024]
Abstract
Deep-learning-based classification models are increasingly used for predicting molecular properties in drug development. However, traditional classification models using the Softmax function often give overconfident mispredictions for out-of-distribution samples, highlighting a critical lack of accurate uncertainty estimation. Such limitations can result in substantial costs and should be avoided during drug development. Inspired by advances in evidential deep learning and Posterior Network, we replaced the Softmax function with a normalizing flow to enhance the uncertainty estimation ability of the model in molecular property classification. The proposed strategy was evaluated across diverse scenarios, including simulated experiments based on a synthetic dataset, ADMET predictions, and ligand-based virtual screening. The results demonstrate that compared with the vanilla model, the proposed strategy effectively alleviates the problem of giving overconfident but incorrect predictions. Our findings support the promising application of evidential deep learning in drug development and offer a valuable framework for further research.
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Affiliation(s)
- Zhehuan Fan
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China
| | - Jie Yu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China
| | - Xiang Zhang
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Yijie Chen
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Shihui Sun
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Yuanyuan Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China
| | - Mingan Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Lingang Laboratory, Shanghai 200031, China
| | - Fu Xiao
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Wenyong Wu
- Lingang Laboratory, Shanghai 200031, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Xiaomin Luo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China
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Tanty DK, Sahu PR, Mohapatra R, Sahu SK. Antidiabetic potency and molecular insights of natural products bearing indole moiety: A systematic bioinformatics investigation targeting AKT1. Comput Biol Chem 2024; 110:108059. [PMID: 38608439 DOI: 10.1016/j.compbiolchem.2024.108059] [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: 01/31/2024] [Revised: 03/13/2024] [Accepted: 03/21/2024] [Indexed: 04/14/2024]
Abstract
Diabetic mellitus (DM) is a chronic disorder, and type 2 DM (T2DM) is the most prevalent among all categories (nearly 90%) across the globe every year. With the availability of potential drugs, the prevalence rate has remained uncontrollable, while natural resources showed a promising potency, and exploring such potential candidates at the preclinical stage is essential. An extensive literature search selected 89 marine and plant-derived indole derivatives with anti-inflammatory, antioxidant, lipid-lowering, etc., activities. However, as we know, drugs have not been able to convert from 'lead' to 'mainstream' due to inadequate drug-ability profiles, as our systematic investigation proved and selected herdmanine_A (HERD_A) and penerpene_D (PENE_D) as the most potential antidiabetic candidates from the library of indole derivatives. Based on our previous network pharmacology study, we selected three new target enzymes: Acetyl-CoA carboxylase 2 (ACACB; PDB ID: 3JRX), cyclin-dependent kinase 4 (CDK4; PDB ID: 3G33), and alpha serine/threonine-protein kinase 1 (AKT1; PDB ID: 3O96) to assess the antidiabetic potency of selected indole derivatives through binding energy or docking score. To conduct molecular docking studies with these enzymes, we used the PyRx-AutoDock platform. Furthermore, molecular dynamic simulation at 100 ns, physicochemical analysis, pharmacokinetics, toxicity assessment, and drug-likeness evaluation suggested that HERD_A and penerpene PENE_D were the most potent inhibitors against AKT1 compared to koenimbine (most potential based on the recorded IC50 value) and murrayakonine_A (most potential based on the docking score). In summary, HERD_A and/or PENE_D have the potential to be used as alternative therapeutic agent for the treatment of diabetes after some pharmacological investigation.
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Affiliation(s)
- Dhananjay K Tanty
- University Department of Pharmaceutical Sciences, Utkal University, Vani Vihar, Bhubaneswar, Odisha 751004, India
| | - Prachi R Sahu
- University Department of Pharmaceutical Sciences, Utkal University, Vani Vihar, Bhubaneswar, Odisha 751004, India
| | - Ranjit Mohapatra
- University Department of Pharmaceutical Sciences, Utkal University, Vani Vihar, Bhubaneswar, Odisha 751004, India
| | - Susanta K Sahu
- University Department of Pharmaceutical Sciences, Utkal University, Vani Vihar, Bhubaneswar, Odisha 751004, India.
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Wu Z, Chen S, Wang Y, Li F, Xu H, Li M, Zeng Y, Wu Z, Gao Y. Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis. Int J Surg 2024; 110:3848-3878. [PMID: 38502850 PMCID: PMC11175770 DOI: 10.1097/js9.0000000000001289] [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: 11/08/2023] [Accepted: 02/22/2024] [Indexed: 03/21/2024]
Abstract
AIM Computer-aided drug design (CADD) is a drug design technique for computing ligand-receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics. METHODS A systematic review of studies published between 2000 and 20 July 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analyzed using software such as Excel, VOSviewer, RStudio, and CiteSpace. RESULTS A total of 2031 publications were included. These publications primarily originated from 99 countries or regions led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and the greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications. CONCLUSIONS Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modeling, pharmacophore modeling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics that will influence future development. Furthermore, this paper will be helpful in better understanding the frontiers and hotspots of CADD.
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Affiliation(s)
- Zhenhui Wu
- School of Pharmacy, Jiangxi University of Chinese Medicine
- School of Clinical Medicine, Jiangxi University of Chinese Medicine, Nanchang
- Beijing Institute of Radiation Medicine, Academy of Military Sciences, Beijing, People’s Republic of China
| | - Shupeng Chen
- School of Clinical Medicine, Jiangxi University of Chinese Medicine, Nanchang
| | - Yihao Wang
- Beijing Institute of Radiation Medicine, Academy of Military Sciences, Beijing, People’s Republic of China
| | - Fangyang Li
- Beijing Institute of Radiation Medicine, Academy of Military Sciences, Beijing, People’s Republic of China
| | - Huanhua Xu
- School of Pharmacy, Jiangxi University of Chinese Medicine
| | - Maoxing Li
- Beijing Institute of Radiation Medicine, Academy of Military Sciences, Beijing, People’s Republic of China
| | - Yingjian Zeng
- School of Clinical Medicine, Jiangxi University of Chinese Medicine, Nanchang
| | - Zhenfeng Wu
- School of Pharmacy, Jiangxi University of Chinese Medicine
| | - Yue Gao
- School of Pharmacy, Jiangxi University of Chinese Medicine
- Beijing Institute of Radiation Medicine, Academy of Military Sciences, Beijing, People’s Republic of China
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Chen M, Jiang X, Zhang L, Chen X, Wen Y, Gu Z, Li X, Zheng M. The emergence of machine learning force fields in drug design. Med Res Rev 2024; 44:1147-1182. [PMID: 38173298 DOI: 10.1002/med.22008] [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: 08/19/2023] [Revised: 11/29/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024]
Abstract
In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, machine learning force fields (MLFFs) have emerged as a powerful tool capable of balancing accuracy with efficiency. MLFFs rely on the relationship between molecular structures and potential energy, bypassing the need for a preconceived notion of interaction representations. Their accuracy depends on the machine learning models used, and the quality and volume of training data sets. With recent advances in equivariant neural networks and high-quality datasets, MLFFs have significantly improved their performance. This review explores MLFFs, emphasizing their potential in drug design. It elucidates MLFF principles, provides development and validation guidelines, and highlights successful MLFF implementations. It also addresses potential challenges in developing and applying MLFFs. The review concludes by illuminating the path ahead for MLFFs, outlining the challenges to be overcome and the opportunities to be harnessed. This inspires researchers to embrace MLFFs in their investigations as a new tool to perform molecular simulations in drug design.
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Affiliation(s)
- Mingan Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, China
- Lingang Laboratory, Shanghai, China
| | - Xinyu Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Lehan Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoxu Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Yiming Wen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Zhiyong Gu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
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Yang W, Wang Y, Han D, Tang W, Sun L. Recent advances in application of computer-aided drug design in anti-COVID-19 Virials Drug Discovery. Biomed Pharmacother 2024; 173:116423. [PMID: 38493593 DOI: 10.1016/j.biopha.2024.116423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 03/19/2024] Open
Abstract
Corona Virus Disease 2019 (COVID-19) is a global pandemic epidemic caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which poses a serious threat to human health worldwide and results in significant economic losses. With the continuous emergence of new virus strains, small molecule drugs remain the most effective treatment for COVID-19. The traditional drug development process usually requires several years; however, the development of computer-aided drug design (CADD) offers the opportunity to develop innovative drugs quickly and efficiently. The literature review describes the general process of CADD, the viral proteins that play essential roles in the life cycle of SARS-CoV-2 and can serve as therapeutic targets, and examples of drug screening of viral target proteins by applying CADD methods. Finally, the potential of CADD in COVID-19 therapy, the deficiency, and the possible future development direction are discussed.
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Affiliation(s)
- Weiying Yang
- Department of Emergency Medicine, First Hospital of Jilin University, Changchun 130021, China
| | - Ye Wang
- School of Life Sciences, Jilin University, Changchun 130012, China
| | - Dongfeng Han
- Department of Emergency Medicine, First Hospital of Jilin University, Changchun 130021, China
| | - Wenjing Tang
- Department of Emergency Medicine, First Hospital of Jilin University, Changchun 130021, China
| | - Lichao Sun
- Department of Emergency Medicine, First Hospital of Jilin University, Changchun 130021, China.
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10
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Zhang C, Xie L, Lu X, Mao R, Xu L, Xu X. Developing an Improved Cycle Architecture for AI-Based Generation of New Structures Aimed at Drug Discovery. Molecules 2024; 29:1499. [PMID: 38611779 PMCID: PMC11013495 DOI: 10.3390/molecules29071499] [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: 01/25/2024] [Revised: 03/18/2024] [Accepted: 03/21/2024] [Indexed: 04/14/2024] Open
Abstract
Drug discovery involves a crucial step of optimizing molecules with the desired structural groups. In the domain of computer-aided drug discovery, deep learning has emerged as a prominent technique in molecular modeling. Deep generative models, based on deep learning, play a crucial role in generating novel molecules when optimizing molecules. However, many existing molecular generative models have limitations as they solely process input information in a forward way. To overcome this limitation, we propose an improved generative model called BD-CycleGAN, which incorporates BiLSTM (bidirectional long short-term memory) and Mol-CycleGAN (molecular cycle generative adversarial network) to preserve the information of molecular input. To evaluate the proposed model, we assess its performance by analyzing the structural distribution and evaluation matrices of generated molecules in the process of structural transformation. The results demonstrate that the BD-CycleGAN model achieves a higher success rate and exhibits increased diversity in molecular generation. Furthermore, we demonstrate its application in molecular docking, where it successfully increases the docking score for the generated molecules. The proposed BD-CycleGAN architecture harnesses the power of deep learning to facilitate the generation of molecules with desired structural features, thus offering promising advancements in the field of drug discovery processes.
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Affiliation(s)
| | | | | | | | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China; (C.Z.); (L.X.); (X.L.); (R.M.)
| | - Xiaojun Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China; (C.Z.); (L.X.); (X.L.); (R.M.)
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11
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Lv MT, Wang HC, Meng XW, Shi YT, Zhang YM, Shan LL, Shi RL, Ni TJ, Duan YC, Yang ZJ, Zhang W. In silico and in vitro analyses of a novel FoxO1 agonist reducing Aβ levels via downregulation of BACE1. CNS Neurosci Ther 2024; 30:e14140. [PMID: 36892036 PMCID: PMC10915984 DOI: 10.1111/cns.14140] [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: 10/22/2022] [Revised: 01/30/2023] [Accepted: 02/16/2023] [Indexed: 03/10/2023] Open
Abstract
AIMS FoxO1 is an important target in the treatment of Alzheimer's disease (AD). However, FoxO1-specific agonists and their effects on AD have not yet been reported. This study aimed to identify small molecules that upregulate the activity of FoxO1 to attenuate the symptoms of AD. METHODS FoxO1 agonists were identified by in silico screening and molecular dynamics simulation. Western blotting and reverse transcription-quantitative polymerase chain reaction assays were used to assess protein and gene expression levels of P21, BIM, and PPARγ downstream of FoxO1 in SH-SY5Y cells, respectively. Western blotting and enzyme-linked immunoassays were performed to explore the effect of FoxO1 agonists on APP metabolism. RESULTS N-(3-methylisothiazol-5-yl)-2-(2-oxobenzo[d]oxazol-3(2H)-yl) acetamide (compound D) had the highest affinity for FoxO1. Compound D activated FoxO1 and regulated the expression of its downstream target genes, P21, BIM, and PPARγ. In SH-SY5Y cells treated with compound D, BACE1 expression levels were downregulated, and the levels of Aβ1-40 and Aβ1-42 were also reduced. CONCLUSIONS We present a novel small-molecule FoxO1 agonist with good anti-AD effects. This study highlights a promising strategy for new drug discovery for AD.
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Affiliation(s)
- Ming-Ti Lv
- School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang, China
| | - He-Cheng Wang
- School of Life and Pharmaceutical Sciences, Dalian University of Technology, Panjin, China
| | - Xiao-Wen Meng
- School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang, China
| | - Ya-Ting Shi
- School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang, China
| | - Yi-Min Zhang
- School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang, China
| | - Lin-Lin Shan
- School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang, China
| | - Ru-Ling Shi
- School of Medical Technology, Xinxiang Medical University, Xinxiang, China
| | - Tian-Jun Ni
- School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang, China
| | - Ying-Chao Duan
- School of Pharmacy, Xinxiang Medical University, Xinxiang, China
| | - Zhi-Jun Yang
- School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang, China
- School of Life Science and Technology, Xinxiang Medical University, Xinxiang, China
| | - Wei Zhang
- School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang, China
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12
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Qi X, Zhao Y, Qi Z, Hou S, Chen J. Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges. Molecules 2024; 29:903. [PMID: 38398653 PMCID: PMC10892089 DOI: 10.3390/molecules29040903] [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: 01/15/2024] [Revised: 02/08/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.
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Affiliation(s)
- Xin Qi
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
| | - Yuanchun Zhao
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
| | - Zhuang Qi
- School of Software, Shandong University, Jinan 250101, China;
| | - Siyu Hou
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
| | - Jiajia Chen
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
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13
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Byun J, Tai J, Kim B, Kim J, Jung S, Lee J, Song YW, Shin J, Kim TH. Identification of Hit Compounds Using Artificial Intelligence for the Management of Allergic Diseases. Int J Mol Sci 2024; 25:2280. [PMID: 38396957 PMCID: PMC10889320 DOI: 10.3390/ijms25042280] [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: 01/05/2024] [Revised: 01/22/2024] [Accepted: 01/24/2024] [Indexed: 02/25/2024] Open
Abstract
This study aimed to identify and evaluate drug candidates targeting the kinase inhibitory region of suppressor of cytokine signaling (SOCS) 3 for the treatment of allergic rhinitis (AR). Utilizing an artificial intelligence (AI)-based new drug development platform, virtual screening was conducted to identify compounds inhibiting the SH2 domain binding of SOCS3. Luminescence assays assessed the ability of these compounds to restore JAK-2 activity diminished by SOCS3. Jurkat T and BEAS-2B cells were utilized to investigate changes in SOCS3 and STAT3 expression, along with STAT3 phosphorylation in response to the identified compounds. In an OVA-induced allergic rhinitis mouse model, we measured serum levels of total IgE and OVA-specific IgE, performed real-time PCR on nasal mucosa samples to quantify Th2 cytokines and IFN-γ expression, and conducted immunohistochemistry to analyze eosinophil levels. Screening identified 20 hit compounds with robust binding affinities. As the concentration of SOCS3 increased, a corresponding decrease in JAK2 activity was observed. Compounds 5 and 8 exhibited significant efficacy in restoring JAK2 activity without toxicity. Treatment with these compounds resulted in reduced SOCS3 expression and the reinstatement of STAT3 phosphorylation in Jurkat T and BEAS-2B cells. In the OVA-induced allergic rhinitis mouse model, compounds 5 and 8 effectively alleviated nasal symptoms and demonstrated lower levels of immune markers compared to the allergy group. This study underscores the promising nonclinical efficacy of compounds identified through the AI-based drug development platform. These findings introduce innovative strategies for the treatment of AR and highlight the potential therapeutic value of targeting SOCS3 in managing AR.
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Affiliation(s)
- Junhyoung Byun
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
- Mucosal Immunology Institute, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
| | - Junhu Tai
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
| | - Byoungjae Kim
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
- Neuroscience Research Institute, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
| | - Jaehyeong Kim
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
- Mucosal Immunology Institute, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
| | - Semyung Jung
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
| | - Juhyun Lee
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
| | - Youn woo Song
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
- Mucosal Immunology Institute, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
| | - Jaemin Shin
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
- Mucosal Immunology Institute, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
| | - Tae Hoon Kim
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
- Mucosal Immunology Institute, College of Medicine, Korea University, 02842 Seoul, Republic of Korea
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Hassan J, Saeed SM, Deka L, Uddin MJ, Das DB. Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges. Pharmaceutics 2024; 16:260. [PMID: 38399314 PMCID: PMC10892549 DOI: 10.3390/pharmaceutics16020260] [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: 12/08/2023] [Revised: 01/29/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.
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Affiliation(s)
- Jasmin Hassan
- Drug Delivery & Therapeutics Lab, Dhaka 1212, Bangladesh; (J.H.); (S.M.S.)
| | | | - Lipika Deka
- Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK;
| | - Md Jasim Uddin
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Diganta B. Das
- Department of Chemical Engineering, Loughborough University, Loughborough LE11 3TU, UK
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15
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Aamir A, Iqbal A, Jawed F, Ashfaque F, Hafsa H, Anas Z, Oduoye MO, Basit A, Ahmed S, Abdul Rauf S, Khan M, Mansoor T. Exploring the current and prospective role of artificial intelligence in disease diagnosis. Ann Med Surg (Lond) 2024; 86:943-949. [PMID: 38333305 PMCID: PMC10849462 DOI: 10.1097/ms9.0000000000001700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/28/2023] [Indexed: 02/10/2024] Open
Abstract
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems, providing assistance in a variety of patient care and health systems. The aim of this review is to contribute valuable insights to the ongoing discourse on the transformative potential of AI in healthcare, providing a nuanced understanding of its current applications, future possibilities, and associated challenges. The authors conducted a literature search on the current role of AI in disease diagnosis and its possible future applications using PubMed, Google Scholar, and ResearchGate within 10 years. Our investigation revealed that AI, encompassing machine-learning and deep-learning techniques, has become integral to healthcare, facilitating immediate access to evidence-based guidelines, the latest medical literature, and tools for generating differential diagnoses. However, our research also acknowledges the limitations of current AI methodologies in disease diagnosis and explores uncertainties and obstacles associated with the complete integration of AI into clinical practice. This review has highlighted the critical significance of integrating AI into the medical healthcare framework and meticulously examined the evolutionary trajectory of healthcare-oriented AI from its inception, delving into the current state of development and projecting the extent of reliance on AI in the future. The authors have found that central to this study is the exploration of how the strategic integration of AI can accelerate the diagnostic process, heighten diagnostic accuracy, and enhance overall operational efficiency, concurrently relieving the burdens faced by healthcare practitioners.
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Affiliation(s)
- Ali Aamir
- Department of Medicine, Dow University of Health Sciences
| | - Arham Iqbal
- Department of Medicine, Dow International Medical College, Karachi, Pakistan
| | - Fareeha Jawed
- Department of Medicine, Dow University of Health Sciences
| | - Faiza Ashfaque
- Department of Medicine, Dow University of Health Sciences
| | - Hafiza Hafsa
- Department of Medicine, Dow University of Health Sciences
| | - Zahra Anas
- Department of Medicine, Dow University of Health Sciences
| | - Malik Olatunde Oduoye
- Department of Research, Medical Research Circle, Bukavu, Democratic Republic of Congo
| | - Abdul Basit
- Department of Medicine, Dow University of Health Sciences
| | - Shaheer Ahmed
- Department of Medicine, Dow University of Health Sciences
| | | | - Mushkbar Khan
- Liaquat National Hospital and Medical College, Pakistan
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16
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Zaib S, Rana N, Ali HS, Hussain N, Areeba, Ogaly HA, Al-Zahrani FAM, Khan I. Discovery of druggable potent inhibitors of serine proteases and farnesoid X receptor by ligand-based virtual screening to obstruct SARS-CoV-2. Int J Biol Macromol 2023; 253:127379. [PMID: 37838109 DOI: 10.1016/j.ijbiomac.2023.127379] [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: 05/08/2023] [Revised: 09/12/2023] [Accepted: 10/09/2023] [Indexed: 10/16/2023]
Abstract
The coronavirus, a subfamily of the coronavirinae family, is an RNA virus with over 40 variations that can infect humans, non-human mammals and birds. There are seven types of human coronaviruses, including SARS-CoV-2, is responsible for the recent COVID-19 pandemic. The current study is focused on the identification of drug molecules for the treatment of COVID-19 by targeting human proteases like transmembrane serine protease 2 (TMPRSS2), furin, cathepsin B, and a nuclear receptor named farnesoid X receptor (FXR). TMPRSS2 and furin help in cleaving the spike protein of the SARS-CoV-2 virus, while cathepsin B plays a critical role in the entry and pathogenesis. FXR, on the other hand, regulates the expression of ACE2, and its inhibition can reduce SARS-CoV-2 infection. By inhibiting these four protein targets with non-toxic inhibitors, the entry of the infectious agent into host cells and its pathogenesis can be obstructed. We have used the BioSolveIT suite for pharmacophore-based computational drug designing. A total of 1611 ligands from the ligand library were docked with the target proteins to obtain potent inhibitors on the basis of pharmacophore. Following the ADMET analysis and protein ligand interactions, potent and druggable inhibitors of the target proteins were obtained. Additionally, toxic substructures and the less toxic route of administration of the most potent inhibitors in rodents were also determined computationally. Compounds namely N-(diaminomethylene)-2-((3-((1R,3R)-3-(2-(methoxy(methyl)amino)-2-oxoethyl)cyclopentyl)propyl)amino)-2-oxoethan-1-aminium (26), (1R,3R)-3-(((2-ammonioethyl)ammonio)methyl)-1-((4-propyl-1H-imidazol-2-yl)methyl)piperidin-1-ium (29) and (1R,3R)-3-(((2-ammonioethyl)ammonio)methyl)-1-((1-propyl-1H-pyrazol-4-yl)methyl)piperidin-1-ium (30) were found as the potent inhibitors of TMPRSS2, whereas, 1-(1-(1-(1H-tetrazol-1-yl)cyclopropane-1‑carbonyl)piperidin-4-yl)azepan-2-one (6), (2R)-4-methyl-1-oxo-1-((7R,11S)-4-oxo-6,7,8,9,10,11-hexahydro-4H-7,11-methanopyrido[1,2-a]azocin-9-yl)pentan-2-aminium (12), 4-((1-(3-(3,5-dimethylisoxazol-4-yl)propanoyl)piperidin-4-yl)methyl)morpholin-4-ium (13), 1-(4,6-dimethylpyrimidin-2-yl)-N-(3-oxocyclohex-1-en-1-yl)piperidine-4-carboxamide (14), 1-(4-(1,5-dimethyl-1H-1,2,4-triazol-3-yl)piperidin-1-yl)-3-(3,5-dimethylisoxazol-4-yl)propan-1-one (25) and N,N-dimethyl-4-oxo-4-((1S,5R)-8-oxo-5,6-dihydro-1H-1,5-methanopyrido[1,2-a][1,5]diazocin-3(2H,4H,8H)-yl)butanamide (31) inhibited the FXR preferentially. In case of cathepsin B, N-((5-benzoylthiophen-2-yl)methyl)-2-hydrazineyl-2-oxoacetamide (2) and N-([2,2'-bifuran]-5-ylmethyl)-2-hydrazineyl-2-oxoacetamide (7) were identified as the most druggable inhibitors whereas 1-amino-2,7-diethyl-3,8-dioxo-6-(p-tolyl)-2,3,7,8-tetrahydro-2,7-naphthyridine-4‑carbonitrile (5) and (R)-6-amino-2-(2,3-dihydroxypropyl)-1H-benzo[de]isoquinoline-1,3(2H)-dione (20) were active against furin.
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Affiliation(s)
- Sumera Zaib
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore 54590, Pakistan.
| | - Nehal Rana
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore 54590, Pakistan
| | - Hafiz Saqib Ali
- INEOS Oxford Institute for Antimicrobial Research and Chemistry Research Laboratory, Department of Chemistry, University of Oxford, 12 Mansfield Road, Oxford OX1 3TA, United Kingdom
| | - Nadia Hussain
- Department of Pharmaceutical Sciences, College of Pharmacy, Al Ain University, Al Ain, P.O. Box 64141, United Arab Emirates; AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, P.O. Box 144534, United Arab Emirates
| | - Areeba
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore 54590, Pakistan
| | - Hanan A Ogaly
- Chemistry Department, College of Science, King Khalid University, Abha 61421, Saudi Arabia
| | - Fatimah A M Al-Zahrani
- Chemistry Department, College of Science, King Khalid University, Abha 61421, Saudi Arabia
| | - Imtiaz Khan
- Department of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, United Kingdom.
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17
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Das K, Paltani M, Tripathi PK, Kumar R, Verma S, Kumar S, Jain CK. Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:1286-1300. [PMID: 38213536 PMCID: PMC10776591 DOI: 10.37349/etat.2023.00197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 08/28/2023] [Indexed: 01/13/2024] Open
Abstract
Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. Fewer than 20% of patients only survive beyond five years from diagnosis. CRC is a highly preventable disease if diagnosed at the early stage of malignancy. Several screening methods like endoscopy (like colonoscopy; gold standard), imaging examination [computed tomographic colonography (CTC)], guaiac-based fecal occult blood (gFOBT), immunochemical test from faeces, and stool DNA test are available with different levels of sensitivity and specificity. The available screening methods are associated with certain drawbacks like invasiveness, cost, or sensitivity. In recent years, computer-aided systems-based screening, diagnosis, and treatment have been very promising in the early-stage detection and diagnosis of CRC cases. Artificial intelligence (AI) is an enormously in-demand, cost-effective technology, that uses various tools machine learning (ML), and deep learning (DL) to screen, diagnose, and stage, and has great potential to treat CRC. Moreover, different ML algorithms and neural networks [artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machines (SVMs)] have been deployed to predict precise and personalized treatment options. This review examines and summarizes different ML and DL models used for therapeutic intervention in CRC cancer along with the gap and challenges for AI.
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Affiliation(s)
- Kriti Das
- Department of Artificial Intelligence and Precision Medicine, School of Allied Health Sciences and Management, Delhi Pharmaceutical Sciences and Research University, New Delhi 110017, India
| | - Maanvi Paltani
- Department of Artificial Intelligence and Precision Medicine, School of Allied Health Sciences and Management, Delhi Pharmaceutical Sciences and Research University, New Delhi 110017, India
| | - Pankaj Kumar Tripathi
- Department of Biotechnology, Jaypee Institute of Information Technology, Noida 201309, Uttar Pradesh, India
| | - Rajnish Kumar
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Delhi 110017, India
| | - Saniya Verma
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Delhi 110017, India
| | - Subodh Kumar
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Delhi 110017, India
| | - Chakresh Kumar Jain
- Department of Biotechnology, Jaypee Institute of Information Technology, Noida 201309, Uttar Pradesh, India
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Ali S, Shaikh S, Ahmad K, Choi I. Identification of active compounds as novel dipeptidyl peptidase-4 inhibitors through machine learning and structure-based molecular docking simulations. J Biomol Struct Dyn 2023:1-10. [PMID: 38100571 DOI: 10.1080/07391102.2023.2292299] [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: 07/25/2023] [Accepted: 11/23/2023] [Indexed: 12/17/2023]
Abstract
The enzyme dipeptidyl peptidase 4 (DPP4) is a potential therapeutic target for type 2 diabetes (T2DM). Many synthetic anti-DPP4 medications are available to treat T2DM. The need for secure and efficient medicines has been unmet due to the adverse side effects of existing DPP4 medications. The present study implemented a combined approach to machine learning and structure-based virtual screening to identify DPP4 inhibitors. Two ML models were trained based on DPP4 IC50 datasets. The ML models random forest (RF) and multilayer perceptron (MLP) neural network showed good accuracy, with the area under the curve being 0.93 and 0.91, respectively. The natural compound library was screened through ML models, and 1% (217) of compounds were selected for further screening. Structure-based virtual screening was performed along with positive control sitagliptin to obtain more specific and selective leads for DPP4. Based on binding affinity, drug-likeness properties, and interaction with DPP4, Z-614 and Z-997 compounds showed high binding affinity and specificity in the catalytic pocket of DPP4. Finally, the stability conformation of the DPP4 enzyme complex was checked by a molecular dynamics (MD) simulation. The MD simulation showed that both compounds bind better in the catalytic pocket, but the Z-614 compound altered the DPP4 native conformation. Therefore, Z-614 showed a high deviation in the backbone. This combined approach (ML and structure-based) study reported that Z-997 binds most stably to DPP4 in their catalytic pocket with a binding free energy of -70.3 kJ/mol, suggesting its therapeutic potential as a treatment option for T2DM disease.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shahid Ali
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan, South Korea
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan, South Korea
| | - Sibhghatulla Shaikh
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan, South Korea
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan, South Korea
| | - Khurshid Ahmad
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan, South Korea
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan, South Korea
| | - Inho Choi
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan, South Korea
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan, South Korea
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19
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Tiwari PC, Pal R, Chaudhary MJ, Nath R. Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges. Drug Dev Res 2023; 84:1652-1663. [PMID: 37712494 DOI: 10.1002/ddr.22115] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/14/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023]
Abstract
By harnessing artificial intelligence (AI) algorithms and machine learning techniques, the entire drug discovery process stands to undergo a profound transformation, offering a myriad of advantages. Foremost among these is the ability of AI to conduct swift and efficient screenings of expansive compound libraries, significantly augmenting the identification of potential drug candidates. Moreover, AI algorithms can prove instrumental in predicting the efficacy and safety profiles of candidate compounds, thus endowing invaluable insights and reducing reliance on extensive preclinical and clinical testing. This predictive capacity of AI has the potential to streamline the drug development pipeline and enhance the success rate of clinical trials, ultimately resulting in the emergence of more efficacious and safer therapeutic agents. However, the deployment of AI in drug discovery introduces certain challenges that warrant attention. A primary hurdle entails the imperative acquisition of high-quality and diverse data. Furthermore, ensuring the interpretability of AI models assumes critical importance in securing regulatory endorsement and cultivating trust within scientific and medical communities. Addressing ethical considerations, including data privacy and mitigating bias, represents an additional momentous challenge, requiring assiduous navigation. In this review, we provide an intricate and comprehensive overview of the multifaceted challenges intrinsic to conventional drug development paradigms, while simultaneously interrogating the efficacy of AI in effectively surmounting these formidable obstacles.
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Affiliation(s)
- Prafulla C Tiwari
- Department of Pharmacology and Therapeutics, King George's Medical University, Lucknow, Uttar Pradesh, India
| | - Rishi Pal
- Department of Pharmacology and Therapeutics, King George's Medical University, Lucknow, Uttar Pradesh, India
| | - Manju J Chaudhary
- Department of Physiology, Government Medical College, Kannauj, Uttar Pradesh, India
| | - Rajendra Nath
- Department of Pharmacology and Therapeutics, King George's Medical University, Lucknow, Uttar Pradesh, India
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20
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Klaus B. AI Models and Drug Discovery Within Pharmaceutical Drug Market. Dela J Public Health 2023; 9:52-53. [PMID: 38173958 PMCID: PMC10759970 DOI: 10.32481/djph.2023.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024] Open
Abstract
This literature review aims to highlight new drug discovery specifically in the United States, and introduce how artificial intelligence can be used to help reduce development time and costs.
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Affiliation(s)
- Bridget Klaus
- Intern, Delaware Academy of Medicine/Delaware Public Health Association
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21
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Feng X, Chang R, Zhu H, Yang Y, Ji Y, Liu D, Qin H, Yin J, Rong H. Engineering Proteins for Cell Entry. Mol Pharm 2023; 20:4868-4882. [PMID: 37708383 DOI: 10.1021/acs.molpharmaceut.3c00467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
Proteins are essential for life, as they participate in all vital processes in the body. In the past decade, delivery of active proteins to specific cells and organs has attracted increasing interest. However, most proteins cannot enter the cytoplasm due to the cell membrane acting as a natural barrier. To overcome this challenge, various proteins have been engineered to acquire cell-penetrating capacity by mimicking or modifying natural shuttling proteins. In this review, we provide an overview of the different types of engineered cell-penetrating proteins such as cell-penetrating peptides, supercharged proteins, receptor-binding proteins, and bacterial toxins. We also discuss some strategies for improving endosomal escape such as pore formation, the proton sponge effect, and hijacking intracellular trafficking pathways. Finally, we introduce some novel methods and technologies for designing and detecting engineered cell-penetrating proteins.
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Affiliation(s)
- Xiaoyu Feng
- Jiangsu Key Laboratory of Druggability of Biopharmaceuticals and State Key Laboratory of Natural Medicines, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China
| | - Ruilong Chang
- Jiangsu Key Laboratory of Druggability of Biopharmaceuticals and State Key Laboratory of Natural Medicines, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China
| | - Haichao Zhu
- Jiangsu Key Laboratory of Druggability of Biopharmaceuticals and State Key Laboratory of Natural Medicines, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China
| | - Yifan Yang
- Jiangsu Key Laboratory of Druggability of Biopharmaceuticals and State Key Laboratory of Natural Medicines, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China
| | - Yue Ji
- Jiangsu Key Laboratory of Druggability of Biopharmaceuticals and State Key Laboratory of Natural Medicines, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China
| | - Dingkang Liu
- Jiangsu Key Laboratory of Druggability of Biopharmaceuticals and State Key Laboratory of Natural Medicines, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China
| | - Hai Qin
- Department of Clinical Laboratory, Beijing Jishuitan Hospital Guizhou Hospital, No. 206, Sixian Street, Baiyun District, Guiyang, Guizhou 550014, China
| | - Jun Yin
- Jiangsu Key Laboratory of Druggability of Biopharmaceuticals and State Key Laboratory of Natural Medicines, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China
| | - Haibo Rong
- Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210009, China
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22
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Chen Q, Wang Y, Shuai J. Current status and future prospects of stomatology research. J Zhejiang Univ Sci B 2023; 24:853-867. [PMID: 37752088 PMCID: PMC10522564 DOI: 10.1631/jzus.b2200702] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 03/27/2023] [Indexed: 08/08/2023]
Abstract
Research in stomatology (dental medicine) continues to expand globally and is oriented towards solving clinical issues, focusing on clarifying the clinical relevance and potential mechanisms of oral-systemic connections via clinical epidemiology, oral microecological characterization, and the establishment of animal models. Interdisciplinary integration of materials science and tissue engineering with stomatology is expected to lead to the creation of innovative materials and technologies to better resolve the most prevalent and challenging clinical issues such as peri-implantitis, soft and hard tissue defects, and dentin hypersensitivity. With the rapid development of artificial intelligence (AI), 5th generation mobile communication technology (5G), and big data applications, "intelligent stomatology" is emerging to build models for better clinical diagnosis and management, accelerate the reform of education, and support the growth and advancement of scientific research. Here, we summarized the current research status, and listed the future prospects and limitations of these three aspects, aiming to provide a basis for more accurate etiological exploration, novel treatment methods, and abundant big data analysis in stomatology to promote the translation of research achievements into practical applications for both clinicians and the public.
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Affiliation(s)
- Qianming Chen
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou 310006, China.
| | - Yahui Wang
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou 310006, China
| | - Jing Shuai
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou 310006, China
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23
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Diao Y, Liu D, Ge H, Zhang R, Jiang K, Bao R, Zhu X, Bi H, Liao W, Chen Z, Zhang K, Wang R, Zhu L, Zhao Z, Hu Q, Li H. Macrocyclization of linear molecules by deep learning to facilitate macrocyclic drug candidates discovery. Nat Commun 2023; 14:4552. [PMID: 37507402 PMCID: PMC10382584 DOI: 10.1038/s41467-023-40219-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
Interest in macrocycles as potential therapeutic agents has increased rapidly. Macrocyclization of bioactive acyclic molecules provides a potential avenue to yield novel chemical scaffolds, which can contribute to the improvement of the biological activity and physicochemical properties of these molecules. In this study, we propose a computational macrocyclization method based on Transformer architecture (which we name Macformer). Leveraging deep learning, Macformer explores the vast chemical space of macrocyclic analogues of a given acyclic molecule by adding diverse linkers compatible with the acyclic molecule. Macformer can efficiently learn the implicit relationships between acyclic and macrocyclic structures represented as SMILES strings and generate plenty of macrocycles with chemical diversity and structural novelty. In data augmentation scenarios using both internal ChEMBL and external ZINC test datasets, Macformer display excellent performance and generalisability. We showcase the utility of Macformer when combined with molecular docking simulations and wet lab based experimental validation, by applying it to the prospective design of macrocyclic JAK2 inhibitors.
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Affiliation(s)
- Yanyan Diao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Dandan Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Huan Ge
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Rongrong Zhang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Kexin Jiang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Runhui Bao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Xiaoqian Zhu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Hongjie Bi
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Wenjie Liao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Ziqi Chen
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Kai Zhang
- Innovation Center for AI and Drug Discovery, East China Normal University, Shanghai, 200062, China
| | - Rui Wang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Lili Zhu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Zhenjiang Zhao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Qiaoyu Hu
- Innovation Center for AI and Drug Discovery, East China Normal University, Shanghai, 200062, China
| | - Honglin Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China.
- Innovation Center for AI and Drug Discovery, East China Normal University, Shanghai, 200062, China.
- Lingang Laboratory, Shanghai, 200031, China.
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24
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Bhat S, Pradeep S, Patil SS, Flores-Holguín N, Glossman-Mitnik D, Frau J, Sommano SR, Ali N, Mohany M, Shivamallu C, Prasad SK, Kollur SP. Preliminary Evaluation of Lablab purpureus Phytochemicals for Anti-BoHV-1 Activity Using In Vitro and In Silico Approaches. ACS OMEGA 2023; 8:22684-22697. [PMID: 37396248 PMCID: PMC10308559 DOI: 10.1021/acsomega.3c01478] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 05/31/2023] [Indexed: 07/04/2023]
Abstract
Lablab purpureus from the Fabaceae family has been reported to have antiviral properties and used in traditional medical systems like ayurveda and Chinese medicine and has been employed to treat a variety of illnesses including cholera, food poisoning, diarrhea, and phlegmatic diseases. The bovine alphaherpesvirus-1 (BoHV-1) is notorious for causing significant harm to the veterinary and agriculture industries. The removal of the contagious BoHV-1 from host organs, particularly in those reservoir creatures, has required the use of antiviral drugs that target infected cells. This study developed LP-CuO NPs from methanolic crude extracts, and FTIR, SEM, and EDX analyses were used to confirm their formation. SEM analysis revealed that the LP-CuO NPs had a spherical shape with particle sizes between 22 and 30 nm. Energy-dispersive X-ray pattern analysis revealed the presence of only copper and oxide ions. By preventing viral cytopathic effects in the Madin-Darby bovine kidney cell line, the methanolic extract of Lablab purpureus and LP-CuO NPs demonstrated a remarkable dose-dependent anti-BoHV-1 action in vitro. Furthermore, molecular docking and molecular dynamics simulation studies of bio-actives from Lablab purpureus against the BoHV-1 viral envelope glycoprotein disclosed effective interactions between all phytochemicals and the protein, although kievitone was found to have the highest binding affinity, with the greatest number of interactions, which was also validated with molecular dynamics simulation studies. Understanding the chemical reactivity qualities of the four ligands was taken into consideration facilitated by the global and local descriptors, which aimed to predict the chemical reactivity descriptors of the studied molecules through the conceptual DFT methodology, which, along with ADMET finding, support the in vitro and in silico results.
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Affiliation(s)
- Smitha
S. Bhat
- Department
of Biotechnology and Bioinformatics, JSS
Academy of Higher Education and Research, Mysuru 570 015, India
| | - Sushma Pradeep
- Department
of Biotechnology and Bioinformatics, JSS
Academy of Higher Education and Research, Mysuru 570 015, India
| | - Sharanagouda S. Patil
- ICAR-National
Institute of Veterinary Epidemiology and Disease Informatics (NIVEDI), Yelahanka, Bengaluru 560 064, India
| | - Norma Flores-Holguín
- Laboratorio
Virtual NANOCOSMOS, Departamento de Medio Ambiente y Energía, Centro de Investigación en Materiales Avanzados, Chihuahua, Chihuahua 31136, Mexico
| | - Daniel Glossman-Mitnik
- Laboratorio
Virtual NANOCOSMOS, Departamento de Medio Ambiente y Energía, Centro de Investigación en Materiales Avanzados, Chihuahua, Chihuahua 31136, Mexico
| | - Juan Frau
- Departament
de Química, Facultat de Ciences, Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain
| | - Sarana Rose Sommano
- Plant
Bioactive Compound Laboratory, Faculty of Agriculture, Chiang Mai University, Chiang Mai 50100, Thailand
| | - Nemat Ali
- Department
of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
| | - Mohamed Mohany
- Department
of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
| | - Chandan Shivamallu
- Department
of Biotechnology and Bioinformatics, JSS
Academy of Higher Education and Research, Mysuru 570 015, India
| | - Shashanka K. Prasad
- Department
of Biotechnology and Bioinformatics, JSS
Academy of Higher Education and Research, Mysuru 570 015, India
- Plant
Bioactive Compound Laboratory, Faculty of Agriculture, Chiang Mai University, Chiang Mai 50100, Thailand
| | - Shiva Prasad Kollur
- School
of Physical Sciences, Amrita Vishwa Vidyapeetham,
Mysuru Campus, Mysuru, Karnataka 570 026, India
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25
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Bappy MNI, Robin TB, Prome AA, Patil RB, Moin AT, Akter R, Laskar FS, Roy A, Akter H, Zinnah KMA. Subtractive proteomics analysis to uncover the potent drug targets for distinctive drug design of Candidaauris. Heliyon 2023; 9:e17026. [PMID: 37484251 PMCID: PMC10361121 DOI: 10.1016/j.heliyon.2023.e17026] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 06/02/2023] [Accepted: 06/05/2023] [Indexed: 07/25/2023] Open
Abstract
Candida auris is a serious health concern of the current world that possesses a serious global health threat and is emerging at a high rate. Available antifungal drugs are failing to combat this pathogen as they are growing resistant to those drugs and some strains have already shown resistance to all three available antifungal drugs in the market. Hence, finding alternative therapies is essential for saving lives from this enemy. To make the development of new treatments easier, we conducted some in silico study of this pathogen to discover possible targets for drug design and also recommended some possible metabolites to test in vivo circumstances. The complete proteome of the representative strain was retrieved, and the duplicate, non-essential, human homologous, non-metabolic, and druggable proteins were then eliminated. As a result, out of a total of 5441 C. auris proteins, we were able to isolate three proteins (XP 028890156.1, XP 028891672.1, and XP 028891858.1) that are crucial for the pathogen's survival as well as host-non-homolog, metabolic, and unrelated proteins to the human microbiome. Their subcellular locations and interactions with a large number of proteins (10 proteins) further point to them being good candidates for therapeutic targets. Following in silico docking of 29 putative antifungals of plant origin against the three proteins we chose, Caledonixanthone E, Viniferin, Glaucine, and Jatrorrhizine were discovered to be the most effective means of inhibiting those proteins since they displayed higher binding affinities (ranging from -28.97 kcal/mol to -51.99 kcal/mol) than the control fluconazole (which ranged between -28.84 kcal/mol and -41.15 kcal/mol). According to the results of MD simulations and MM-PBSA calculations, Viniferin and Caledonixanthone E are the most effective ligands for the proteins XP 028890156.1, XP 028891672.1, and XP 028891858.1. Furthermore, they were predicted to be safe and also showed proper ADME properties.
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Affiliation(s)
- Md. Nazmul Islam Bappy
- Faculty of Biotechnology and Genetic Engineering, Sylhet Agricultural University, Sylhet-3100, Bangladesh
- Department of Animal and Fish Biotechnology, Sylhet Agricultural University, Sylhet-3100, Bangladesh
| | - Tanjin Barketullah Robin
- Faculty of Biotechnology and Genetic Engineering, Sylhet Agricultural University, Sylhet-3100, Bangladesh
| | - Anindita Ash Prome
- Faculty of Biotechnology and Genetic Engineering, Sylhet Agricultural University, Sylhet-3100, Bangladesh
| | - Rajesh B. Patil
- Department of Pharmaceutical Chemistry, Sinhgad Technical Education Society's, Sinhgad College of Pharmacy, Off Sinhgad Road, Vadgaon (Bk), Pune 411041, Maharashtra, India
| | - Abu Tayab Moin
- Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chattogram, Bangladesh
| | - Rupali Akter
- Department of Biochemistry and Molecular Biology, Faculty of Biological Sciences, University of Chittagong, Chattogram, Bangladesh
| | - Fayeza Sadia Laskar
- Faculty of Biotechnology and Genetic Engineering, Sylhet Agricultural University, Sylhet-3100, Bangladesh
| | - Anindita Roy
- Faculty of Biotechnology and Genetic Engineering, Sylhet Agricultural University, Sylhet-3100, Bangladesh
| | - Hafsa Akter
- Faculty of Biotechnology and Genetic Engineering, Sylhet Agricultural University, Sylhet-3100, Bangladesh
- Department of Biochemistry and Chemistry, Sylhet Agricultural University, Sylhet-3100, Bangladesh
| | - Kazi Md. Ali Zinnah
- Faculty of Biotechnology and Genetic Engineering, Sylhet Agricultural University, Sylhet-3100, Bangladesh
- Department of Animal and Fish Biotechnology, Sylhet Agricultural University, Sylhet-3100, Bangladesh
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26
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Yang R, Liu L, Jiang D, Liu L, Yang H, Xu H, Qin M, Wang P, Gu J, Xing Y. Identification of Potential TMPRSS2 Inhibitors for COVID-19 Treatment in Chinese Medicine by Computational Approaches and Surface Plasmon Resonance Technology. J Chem Inf Model 2023; 63:3005-3017. [PMID: 37155923 DOI: 10.1021/acs.jcim.2c01643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
BACKGROUND Coronavirus disease-19 (COVID-19) pneumonia continues to spread in the entire globe with limited medication available. In this study, the active compounds in Chinese medicine (CM) recipes targeting the transmembrane serine protease 2 (TMPRSS2) protein for the treatment of COVID-19 were explored. METHODS The conformational structure of TMPRSS2 protein (TMPS2) was built through homology modeling. A training set covering TMPS2 inhibitors and decoy molecules was docked to TMPS2, and their docking poses were re-scored with scoring schemes. A receiver operating characteristic (ROC) curve was applied to select the best scoring function. Virtual screening of the candidate compounds (CCDs) in the six highly effective CM recipes against TMPS2 was conducted based on the validated docking protocol. The potential CCDs after docking were subject to molecular dynamics (MD) simulations and surface plasmon resonance (SPR) experiment. RESULTS A training set of 65 molecules were docked with modeled TMPS2 and LigScore2 with the highest area under the curve, AUC, value (0.886) after ROC analysis selected to best differentiate inhibitors from decoys. A total of 421 CCDs in the six recipes were successfully docked into TMPS2, and the top 16 CCDs with LigScore2 higher than the cutoff (4.995) were screened out. MD simulations revealed a stable binding between these CCDs and TMPS2 due to the negative binding free energy. Lastly, SPR experiments validated the direct combination of narirutin, saikosaponin B1, and rutin with TMPS2. CONCLUSIONS Specific active compounds including narirutin, saikosaponin B1, and rutin in CM recipes potentially target and inhibit TMPS2, probably exerting a therapeutic effect on COVID-19.
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Affiliation(s)
- Rong Yang
- Department of Hepatology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen 518033, China
- Faculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau 999078, China
| | - Linhua Liu
- Department of Hepatology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen 518033, China
| | - Dansheng Jiang
- Department of Hepatology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen 518033, China
| | - Lei Liu
- Department of Infectious Diseases, Shenzhen Traditional Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518033, China
| | - Huili Yang
- Department of Hepatology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen 518033, China
| | - Hongling Xu
- Department of Hepatology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen 518033, China
| | - Meirong Qin
- National Medical Products Administration, Shenzhen Institute for Drug Control, Shenzhen 518057, China
| | - Ping Wang
- National Medical Products Administration, Shenzhen Institute for Drug Control, Shenzhen 518057, China
| | - Jiangyong Gu
- Research Centre for Integrative Medicine, School of Basic Medical Science, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Yufeng Xing
- Department of Hepatology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen 518033, China
- Faculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau 999078, China
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27
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Bassani D, Moro S. Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies. Molecules 2023; 28:molecules28093906. [PMID: 37175316 PMCID: PMC10180087 DOI: 10.3390/molecules28093906] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 05/15/2023] Open
Abstract
The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.
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Affiliation(s)
- Davide Bassani
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy
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28
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Gui C, Li Y, Peng T. Development of predictive QSAR models for the substrates/inhibitors of OATP1B1 by deep neural networks. Toxicol Lett 2023; 376:20-25. [PMID: 36649904 DOI: 10.1016/j.toxlet.2023.01.006] [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: 09/21/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 01/15/2023]
Abstract
The organic anion transporting polypeptide 1B1 (OATP1B1) is an important hepatic uptake transporter. Inhibition of its normal function could lead to drug-drug interactions. In silico prediction is an effective means to identify potential OATP1B1 inhibitors and quantitative structure-activity relationship (QSAR) modeling is extensively used. As the structures of OATP1B1 substrates/inhibitors are quite diverse, machine learning based methods should be a good option for their QSAR analysis. In the present study, deep neural networks (DNNs) were employed to develop QSAR models for the substrates/inhibitors of OATP1B1 with different molecular fingerprints. Our results showed that QSAR models based on 4-hidden layer DNNs and ECFP4/FCFP4 fingerprints had the best generalization performance. The correlation coefficients (R2) of test set for ECFP4 and FCFP4 models were 0.641 and 0.653, respectively. Model application domain (AD) was calculated with Euclidean distance-based method, and AD could improve the performance of ECFP4 model but has little effect on FCFP4 model. Finally, the prediction of additional 8 compounds that not included in the data set further demonstrated that our QSAR models had a good predictive ability (averaged prediction accuracy >92%). The developed QSAR models could be used to screen large data sets and discover novel inhibitors for OATP1B1.
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Affiliation(s)
- Chunshan Gui
- College of Pharmaceutical Sciences, Soochow University, 199 Renai Road, Suzhou Industrial Park, Suzhou 215123, China.
| | - Ying Li
- College of Pharmaceutical Sciences, Soochow University, 199 Renai Road, Suzhou Industrial Park, Suzhou 215123, China
| | - Taotao Peng
- College of Pharmaceutical Sciences, Soochow University, 199 Renai Road, Suzhou Industrial Park, Suzhou 215123, China
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29
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Liu C, Sun Y, Davis R, Cardona ST, Hu P. ABT-MPNN: an atom-bond transformer-based message-passing neural network for molecular property prediction. J Cheminform 2023; 15:29. [PMID: 36843022 PMCID: PMC9968697 DOI: 10.1186/s13321-023-00698-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 02/10/2023] [Indexed: 02/27/2023] Open
Abstract
Graph convolutional neural networks (GCNs) have been repeatedly shown to have robust capacities for modeling graph data such as small molecules. Message-passing neural networks (MPNNs), a group of GCN variants that can learn and aggregate local information of molecules through iterative message-passing iterations, have exhibited advancements in molecular modeling and property prediction. Moreover, given the merits of Transformers in multiple artificial intelligence domains, it is desirable to combine the self-attention mechanism with MPNNs for better molecular representation. We propose an atom-bond transformer-based message-passing neural network (ABT-MPNN), to improve the molecular representation embedding process for molecular property predictions. By designing corresponding attention mechanisms in the message-passing and readout phases of the MPNN, our method provides a novel architecture that integrates molecular representations at the bond, atom and molecule levels in an end-to-end way. The experimental results across nine datasets show that the proposed ABT-MPNN outperforms or is comparable to the state-of-the-art baseline models in quantitative structure-property relationship tasks. We provide case examples of Mycobacterium tuberculosis growth inhibitors and demonstrate that our model's visualization modality of attention at the atomic level could be an insightful way to investigate molecular atoms or functional groups associated with desired biological properties. The new model provides an innovative way to investigate the effect of self-attention on chemical substructures and functional groups in molecular representation learning, which increases the interpretability of the traditional MPNN and can serve as a valuable way to investigate the mechanism of action of drugs.
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Affiliation(s)
- Chengyou Liu
- grid.21613.370000 0004 1936 9609Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB Canada
| | - Yan Sun
- grid.21613.370000 0004 1936 9609Department of Computer Science, University of Manitoba, Winnipeg, MB Canada
| | - Rebecca Davis
- grid.21613.370000 0004 1936 9609Department of Chemistry, University of Manitoba, Winnipeg, MB Canada
| | - Silvia T. Cardona
- grid.21613.370000 0004 1936 9609Department of Microbiology, University of Manitoba, Winnipeg, MB Canada ,grid.21613.370000 0004 1936 9609Department of Medical Microbiology & Infectious Disease, University of Manitoba, Winnipeg, MB Canada
| | - Pingzhao Hu
- Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada. .,Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada. .,Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada. .,Department of Biochemistry, Western University, Building Rm. 362, London, ON, N6A 5C1, Canada.
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Artificial Intelligence Applied to clinical trials: opportunities and challenges. HEALTH AND TECHNOLOGY 2023; 13:203-213. [PMID: 36923325 PMCID: PMC9974218 DOI: 10.1007/s12553-023-00738-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 02/08/2023] [Indexed: 03/06/2023]
Abstract
Background Clinical Trials (CTs) remain the foundation of safe and effective drug development. Given the evolving data-driven and personalized medicine approach in healthcare, it is imperative for companies and regulators to utilize tailored Artificial Intelligence (AI) solutions that enable expeditious and streamlined clinical research. In this paper, we identified opportunities, challenges, and potential implications of AI in CTs. Methods Following an extensive search in relevant databases and websites, we gathered publications tackling the use of AI and Machine Learning (ML) in CTs from the past 5 years in the US and Europe, including Regulatory Authorities' documents. Results Documented applications of AI commonly concern the oncology field and are mostly being applied in the area of recruitment. Main opportunities discussed aim to create efficiencies across CT activities, including the ability to reduce sample sizes, improve enrollment and conduct faster, more optimized adaptive CTs. While AI is an area of enthusiastic development, the identified challenges are ethical in nature and relate to data availability, standards, and most importantly, lack of regulatory guidance hindering the acceptance of AI tools in drug development. However, future implications are significant and are anticipated to improve the probability of success, reduce trial burden and overall, speed up research and regulatory approval. Conclusion The use of AI in CTs is in its relative infancy; however, it is a fast-evolving field. As regulators provide more guidance on the acceptability of AI in specific areas, we anticipate the scope of use to broaden and the volume of implementation to increase rapidly.
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Singh DP, Kaushik B. A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques. Chem Biol Drug Des 2023; 101:175-194. [PMID: 36303299 DOI: 10.1111/cbdd.14164] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/13/2022] [Accepted: 10/24/2022] [Indexed: 12/24/2022]
Abstract
Computational methods have gained prominence in healthcare research. The accessibility of healthcare data has greatly incited academicians and researchers to develop executions that help in prognosis of cancer drug response. Among various computational methods, machine-learning (ML) and deep-learning (DL) methods provide the most consistent and effectual approaches to handle the serious aftermaths of the deadly disease and drug administered to the patients. Hence, this systematic literature review has reviewed researches that have investigated drug discovery and prognosis of anticancer drug response using ML and DL algorithms. Fot this purpose, PRISMA guidelines have been followed to choose research papers from Google Scholar, PubMed, and Sciencedirect websites. A total count of 105 papers that align with the context of this review were chosen. Further, the review also presents accuracy of the existing ML and DL methods in the prediction of anticancer drug response. It has been found from the review that, amidst the availability of various studies, there are certain challenges associated with each method. Thus, future researchers can consider these limitations and challenges to develop a prominent anticancer drug response prediction method, and it would be greatly beneficial to the medical professionals in administering non-invasive treatment to the patients.
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Affiliation(s)
- Davinder Paul Singh
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
| | - Baijnath Kaushik
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
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Ahmadi S, Abdolmaleki A, Jebeli Javan M. In silico study of natural antioxidants. VITAMINS AND HORMONES 2022; 121:1-43. [PMID: 36707131 DOI: 10.1016/bs.vh.2022.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Antioxidants are the body's defense system against the damage of reactive oxygen species, which are usually produced in the body through various physiological processes. There are various sources of these antioxidants such as endogenous antioxidants in the body and exogenous food sources. This chapter provides important information on methods used to investigate antioxidant activity and sources of plant antioxidants. Over the past two decades, numerous studies have demonstrated the importance of in silico research in the development of novel natural and synthesized antioxidants. In silico methods such as quantitative structure-activity relationships (QSAR), pharmacophore, docking, and virtual screenings are play critical roles in designing effective antioxidants that may be synthesized and tested later. This chapter introduces the available in silico approaches for different classes of antioxidants. Many successful applications of in silico methods in the development and design of novel antioxidants are thoroughly discussed. The QSAR, pharmacophore, molecular docking techniques, and virtual screenings process summarized here would help readers to find out the proper mechanism for the interaction between the free radicals and antioxidant compounds. Furthermore, this chapter focuses on introducing new QSAR models in combination with other in silico methods to predict antioxidants activity and design more active antioxidants. In silico studies are essential to explore largely unknown plant tissue, food sources for antioxidant synthesis, as well as saving time and money in such studies.
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Affiliation(s)
- Shahin Ahmadi
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
| | - Azizeh Abdolmaleki
- Department of Chemistry, Tuyserkan Branch, Islamic Azad University, Tuyserkan, Iran
| | - Marjan Jebeli Javan
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
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Zhang Y, Luo M, Wu P, Wu S, Lee TY, Bai C. Application of Computational Biology and Artificial Intelligence in Drug Design. Int J Mol Sci 2022; 23:13568. [PMID: 36362355 PMCID: PMC9658956 DOI: 10.3390/ijms232113568] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/29/2022] [Accepted: 11/03/2022] [Indexed: 08/24/2023] Open
Abstract
Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.
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Affiliation(s)
- Yue Zhang
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Mengqi Luo
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Peng Wu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518055, China
| | - Song Wu
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Tzong-Yi Lee
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Chen Bai
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
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Yuan S, Wang DS, Liu H, Zhang SN, Yang WG, Lv M, Zhou YX, Zhang SY, Song J, Liu HM. New drug approvals for 2021: Synthesis and clinical applications. Eur J Med Chem 2022; 245:114898. [DOI: 10.1016/j.ejmech.2022.114898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/27/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022]
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Could Artificial Intelligence Prevent Intraoperative Anaphylaxis? Reference Review and Proof of Concept. Medicina (B Aires) 2022; 58:medicina58111530. [PMID: 36363487 PMCID: PMC9694532 DOI: 10.3390/medicina58111530] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 10/17/2022] [Accepted: 10/24/2022] [Indexed: 01/25/2023] Open
Abstract
Drugs and various medical substances have been used for many decades to diagnose or treat diseases. Procedures like surgery and anesthesia (either local or general) use different pharmacological products during these events. In most of the cases, the procedure is safe and the physician performs the technique without incidents. Although they are safe for use, these substances (including drugs) may have adverse effects, varying from mild ones to life-threatening reactions in a minority of patients. Artificial intelligence may be a useful tool in approximating the risk of anaphylaxis before undertaking a medical procedure. This material presents these undesirable responses produced by medical products from a multidisciplinary point of view. Moreover, we present a proof of concept for using artificial intelligence as a possible guardship against intraoperative anaphylaxis.
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Sun F, Xu H, Meng Y, Lu Z. A BERT-based model for coupled biological strategies in biomimetic design. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07734-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Structure-Based Virtual Screening, Docking, ADMET, Molecular Dynamics, and MM-PBSA Calculations for the Discovery of Potential Natural SARS-CoV-2 Helicase Inhibitors from the Traditional Chinese Medicine. J CHEM-NY 2022. [DOI: 10.1155/2022/7270094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Continuing our antecedent work against COVID-19, a set of 5956 compounds of traditional Chinese medicine have been virtually screened for their potential against SARS-CoV-2 helicase (PDB ID: 5RMM). Initially, a fingerprint study with VXG, the ligand of the target enzyme, disclosed the similarity of 187 compounds. Then, a molecular similarity study declared the most similar 40 compounds. Subsequently, molecular docking studies were carried out to examine the binding modes and energies. Then, the most appropriate 26 compounds were subjected to in silico ADMET and toxicity studies to select the most convenient inhibitors to be: (1R,2S)-ephedrine (57), (1R,2S)-norephedrine (59), 2-(4-(pyrrolidin-1-yl)phenyl)acetic acid (84), 1-phenylpropane-1,2-dione (195), 2-methoxycinnamic acid (246), 2-methoxybenzoic acid (364), (R)-2-((R)-5-oxopyrrolidin-3-yl)-2-phenylacetic acid (405), (Z)-6-(3-hydroxy-4-methoxystyryl)-4-methoxy-2H-pyran-2-one (533), 8-chloro-2-(2-phenylethyl)-5,6,7-trihydroxy-5,6,7,8-tetrahydrochromone (637), 3-((1R,2S)-2-(dimethylamino)-1-hydroxypropyl)phenol (818), (R)-2-ethyl-4-(1-hydroxy-2-(methylamino)ethyl)phenol (5159), and (R)-2-((1S,2S,5S)-2-benzyl-5-hydroxy-4-methylcyclohex-3-en-1-yl)propane-1,2-diol (5168). Among the selected 12 compounds, the metabolites, compound 533 showed the best docking scores. Interestingly, the MD simulation studies for compound 533, the one with the highest docking score, over 100 ns showed its correct binding to SARS-CoV-2 helicase with low energy and optimum dynamics. Finally, MM-PBSA studies showed that 533 bonded favorably to SARS-CoV-2 helicase with a free energy value of −83 kJ/mol. Further, the free energy decomposition study determined the essential amino acid residues that contributed favorably to the binding process. The obtained results give a huge hope to find a cure for COVID-19 through further in vitro and in vivo studies for the selected compounds.
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Rahman MM, Islam MR, Akash S, Mim SA, Rahaman MS, Emran TB, Akkol EK, Sharma R, Alhumaydhi FA, Sweilam SH, Hossain ME, Ray TK, Sultana S, Ahmed M, Sobarzo-Sánchez E, Wilairatana P. In silico investigation and potential therapeutic approaches of natural products for COVID-19: Computer-aided drug design perspective. Front Cell Infect Microbiol 2022; 12:929430. [PMID: 36072227 PMCID: PMC9441699 DOI: 10.3389/fcimb.2022.929430] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 06/03/2022] [Indexed: 12/07/2022] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a substantial number of deaths around the world, making it a serious and pressing public health hazard. Phytochemicals could thus provide a rich source of potent and safer anti-SARS-CoV-2 drugs. The absence of approved treatments or vaccinations continues to be an issue, forcing the creation of new medicines. Computer-aided drug design has helped to speed up the drug research and development process by decreasing costs and time. Natural compounds like terpenoids, alkaloids, polyphenols, and flavonoid derivatives have a perfect impact against viral replication and facilitate future studies in novel drug discovery. This would be more effective if collaboration took place between governments, researchers, clinicians, and traditional medicine practitioners’ safe and effective therapeutic research. Through a computational approach, this study aims to contribute to the development of effective treatment methods by examining the mechanisms relating to the binding and subsequent inhibition of SARS-CoV-2 ribonucleic acid (RNA)-dependent RNA polymerase (RdRp). The in silico method has also been employed to determine the most effective drug among the mentioned compound and their aquatic, nonaquatic, and pharmacokinetics’ data have been analyzed. The highest binding energy has been reported -11.4 kcal/mol against SARS-CoV-2 main protease (7MBG) in L05. Besides, all the ligands are non-carcinogenic, excluding L04, and have good water solubility and no AMES toxicity. The discovery of preclinical drug candidate molecules and the structural elucidation of pharmacological therapeutic targets have expedited both structure-based and ligand-based drug design. This review article will assist physicians and researchers in realizing the enormous potential of computer-aided drug design in the design and discovery of therapeutic molecules, and hence in the treatment of deadly diseases.
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Affiliation(s)
- Md. Mominur Rahman
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh
| | - Md. Rezaul Islam
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh
| | - Shopnil Akash
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh
| | - Sadia Afsana Mim
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh
| | - Md. Saidur Rahaman
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh
| | - Talha Bin Emran
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh
- Department of Pharmacy, BGC Trust University Bangladesh, Chittagong, Bangladesh
- *Correspondence: Eduardo Sobarzo-Sánchez, ; Talha Bin Emran, ; Polrat Wilairatana,
| | - Esra Küpeli Akkol
- Department of Pharmacognosy, Faculty of Pharmacy, Gazi University, Ankara, Turkey
| | - Rohit Sharma
- Department of Rasashastra and Bhaishajya Kalpana, Faculty of Ayurveda, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
| | - Fahad A. Alhumaydhi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah, Saudi Arabia
| | - Sherouk Hussein Sweilam
- Department of Pharmacognosy, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
- Department of Pharmacognosy, Faculty of Pharmacy, Egyptian Russian University, Badr City, Egypt
| | - Md. Emon Hossain
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh
| | - Tanmay Kumar Ray
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh
| | - Sharifa Sultana
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh
| | - Muniruddin Ahmed
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh
| | - Eduardo Sobarzo-Sánchez
- Instituto de Investigación y Postgrado, Facultad de Ciencias de la Salud, Universidad Central de Chile, Santiago, Chile
- Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, Santiago de Compostela, Spain
- *Correspondence: Eduardo Sobarzo-Sánchez, ; Talha Bin Emran, ; Polrat Wilairatana,
| | - Polrat Wilairatana
- Department of Clinical Tropical Medicine, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- *Correspondence: Eduardo Sobarzo-Sánchez, ; Talha Bin Emran, ; Polrat Wilairatana,
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A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19. Biomed Pharmacother 2022; 153:113350. [PMID: 35777222 PMCID: PMC9236981 DOI: 10.1016/j.biopha.2022.113350] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/22/2022] [Accepted: 06/24/2022] [Indexed: 11/26/2022] Open
Abstract
Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.
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Identification of Potential WSB1 Inhibitors by AlphaFold Modeling, Virtual Screening, and Molecular Dynamics Simulation Studies. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:4629392. [PMID: 35600960 PMCID: PMC9122669 DOI: 10.1155/2022/4629392] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 04/27/2022] [Indexed: 12/03/2022]
Abstract
WD40 repeat and SOCS box containing 1 (WSB1) consists of seven WD40 repeat structural domains at the N-terminal end and one SOCS box structural domain at the C-terminal end. WSB1 promotes cancer progression by affecting the Von Hippel–Lindau tumor suppressor protein (pVHL) and upregulating hypoxia inducible factor-1α (HIF-1α) target gene expression. However, the crystal structure of WSB1 has not been reported, which is not beneficial to the research on WSB1 inhibitors. Therefore, we focused on specific small molecule inhibitors of WSB1. This study applied virtual screening and molecular dynamics simulations; finally, 20 compounds were obtained. Among them, compound G490-0341 showed the best stable structure and was a promising composite for further development of WSB1 inhibitors.
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Decoding the protein-ligand interactions using parallel graph neural networks. Sci Rep 2022; 12:7624. [PMID: 35538084 PMCID: PMC9086424 DOI: 10.1038/s41598-022-10418-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 04/06/2022] [Indexed: 12/13/2022] Open
Abstract
Protein-ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: [Formula: see text] is the base implementation that employs distinct featurization to enhance domain-awareness, while [Formula: see text] is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein's 3D structure with 0.979 test accuracy for [Formula: see text] and 0.958 for [Formula: see text] for predicting activity of a protein-ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and [Formula: see text] crucial for compound's potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on [Formula: see text] with [Formula: see text] and [Formula: see text], respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of [Formula: see text] on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data.
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Hadipour H, Liu C, Davis R, Cardona ST, Hu P. Deep clustering of small molecules at large-scale via variational autoencoder embedding and K-means. BMC Bioinformatics 2022; 23:132. [PMID: 35428173 PMCID: PMC9011935 DOI: 10.1186/s12859-022-04667-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
Background Converting molecules into computer-interpretable features with rich molecular information is a core problem of data-driven machine learning applications in chemical and drug-related tasks. Generally speaking, there are global and local features to represent a given molecule. As most algorithms have been developed based on one type of feature, a remaining bottleneck is to combine both feature sets for advanced molecule-based machine learning analysis. Here, we explored a novel analytical framework to make embeddings of the molecular features and apply them in the clustering of a large number of small molecules. Results In this novel framework, we first introduced a principal component analysis method encoding the molecule-specific atom and bond information. We then used a variational autoencoder (AE)-based method to make embeddings of the global chemical properties and the local atom and bond features. Next, using the embeddings from the encoded local and global features, we implemented and compared several unsupervised clustering algorithms to group the molecule-specific embeddings. The number of clusters was treated as a hyper-parameter and determined by the Silhouette method. Finally, we evaluated the corresponding results using three internal indices. Applying the analysis framework to a large chemical library of more than 47,000 molecules, we successfully identified 50 molecular clusters using the K-means method with 32 embeddings based on the AE method. We visualized the clustering result via t-SNE for the overall distribution of molecules and the similarity maps for the structural analysis of randomly selected cluster-specific molecules. Conclusions This study developed a novel analytical framework that comprises a feature engineering scheme for molecule-specific atomic and bonding features and a deep learning-based embedding strategy for different molecular features. By applying the identified embeddings, we show their usefulness for clustering a large molecule dataset. Our novel analytic algorithms can be applied to any virtual library of chemical compounds with diverse molecular structures. Hence, these tools have the potential of optimizing drug discovery, as they can decrease the number of compounds to be screened in any drug screening campaign.
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Panahi Y, Dadkhah M, Talei S, Gharari Z, Asghariazar V, Abdolmaleki A, Matin S, Molaei S. Can anti-parasitic drugs help control COVID-19? Future Virol 2022. [PMID: 35359702 PMCID: PMC8940209 DOI: 10.2217/fvl-2021-0160] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 02/28/2022] [Indexed: 01/18/2023]
Abstract
Novel COVID-19 is a public health emergency that poses a serious threat to people worldwide. Given the virus spreading so quickly, novel antiviral medications are desperately needed. Repurposing existing drugs is the first strategy. Anti-parasitic drugs were among the first to be considered as a potential treatment option for this disease. Even though many papers have discussed the efficacy of various anti-parasitic drugs in treating COVID-19 separately, so far, no single study comprehensively discussed these drugs. This study reviews some anti-parasitic recommended drugs to treat COVID-19, in terms of function and in vitro as well as clinical results. Finally, we briefly review the advanced techniques, such as artificial intelligence, that have been used to find effective drugs for the treatment of COVID-19.
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Affiliation(s)
- Yasin Panahi
- Department of Pharmacology & Toxicology, School of Pharmacy, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Masoomeh Dadkhah
- Pharmaceutical Sciences Research Center, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Sahand Talei
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Zahra Gharari
- Department of Biotechnology, Faculty of Biological Sciences, Al-Zahra University, Tehran, Iran
| | - Vahid Asghariazar
- Deputy of Research & Technology, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Arash Abdolmaleki
- Department of Engineering Sciences, Faculty of Advanced Technologies, University of Mohaghegh Ardabili, Namin, Iran.,Bio Science & Biotechnology Research center (BBRC), Sabalan University of Advanced Technologies (SUAT), Namin, Iran
| | - Somayeh Matin
- Department of Internal Medicine, Imam Khomeini Hospital, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Soheila Molaei
- Pharmaceutical Sciences Research Center, Ardabil University of Medical Sciences, Ardabil, Iran.,Zoonoses Research Center, Ardabil University of Medical Sciences, Ardabil, Iran
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Tian R, Li Y, Wang X, Li J, Li Y, Bei S, Li H. A Pharmacoinformatics Analysis of Artemisinin Targets and de novo Design of Hits for Treating Ulcerative Colitis. Front Pharmacol 2022; 13:843043. [PMID: 35370688 PMCID: PMC8971781 DOI: 10.3389/fphar.2022.843043] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 02/14/2022] [Indexed: 11/13/2022] Open
Abstract
Ulcerative colitis (UC), as an intractably treated disease, seriously affects the quality of life of patients and has an increase in terms of incidence and prevalence annually. However, due to the lack of a direct etiology and drug-induced side effects, the medical treatment of UC falls into a bottleneck. There are many natural phytochemicals with the potential to regulate immune function in nature. Herein, a potential mechanism of artemisinin in the treatment of UC and potential druggability compounds with an artemisinin peroxide bond were discussed and predicted based on computer-aided drug design (CADD) technology by using the methods of network pharmacology, molecular docking, de novo drug structure design and molecular dynamics through the integration of artemisinin related targets from TCMSP, ChEMBL and HERB databases. The networks were constructed based on 50 artemisinin-disease intersection targets related to inflammation, cytokines, proliferation and apoptosis, showing the importance of GALNT2, BMP7 and TGFBR2 in the treatment of disease, which may be due to the occupation of the ricin B-type lectin domain of GALNT2 by artemisinin compounds or de novo designed candidates. This result could guide the direction of experiments and actual case studies in the future. This study provides a new route for the application of artemisinin and the development of drugs.
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Affiliation(s)
- Rui Tian
- Department of Anoenterology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yufei Li
- Department of Anoenterology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xiaofeng Wang
- Department of Anoenterology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Jiajun Li
- Department of Anoenterology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yingqian Li
- Department of Anoenterology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Shaosheng Bei
- Department of Anoenterology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- *Correspondence: Shaosheng Bei, ; Huashan Li,
| | - Huashan Li
- Department of Anoenterology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- *Correspondence: Shaosheng Bei, ; Huashan Li,
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Liu C, Hogan AM, Sturm H, Khan MW, Islam MM, Rahman ASMZ, Davis R, Cardona ST, Hu P. Deep learning-driven prediction of drug mechanism of action from large-scale chemical-genetic interaction profiles. J Cheminform 2022; 14:12. [PMID: 35279211 PMCID: PMC8917716 DOI: 10.1186/s13321-022-00596-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 02/26/2022] [Indexed: 12/02/2022] Open
Abstract
Motivation Chemical–genetic interaction profiling is a genetic approach that quantifies the susceptibility of a set of mutants depleted in specific gene product(s) to a set of chemical compounds. With the recent advances in artificial intelligence, chemical–genetic interaction profiles (CGIPs) can be leveraged to predict mechanism of action of compounds. This can be achieved by using machine learning, where the data from a CGIP is fed into the machine learning platform along with the chemical descriptors to develop a chemogenetically trained model. As small molecules can be considered non-structural data, graph convolutional neural networks, which can learn from the chemical structures directly, can be used to successfully predict molecular properties. Clustering analysis, on the other hand, is a critical approach to get insights into the underlying biological relationships between the gene products in the high-dimensional chemical-genetic data. Methods and results In this study, we proposed a comprehensive framework based on the large-scale chemical-genetics dataset built in Mycobacterium tuberculosis for predicting CGIPs using graph-based deep learning models. Our approach is structured into three parts. First, by matching M. tuberculosis genes with homologous genes in Escherichia coli (E. coli) according to their gene products, we grouped the genes into clusters with distinct biological functions. Second, we employed a directed message passing neural network to predict growth inhibition against M. tuberculosis gene clusters using a collection of 50,000 chemicals with the profile. We compared the performance of different baseline models and implemented multi-label tasks in binary classification frameworks. Lastly, we applied the trained model to an externally curated drug set that had experimental results against M. tuberculosis genes to examine the effectiveness of our method. Overall, we demonstrate that our approach effectively created M. tuberculosis gene clusters, and the trained classifier is able to predict activity against essential M. tuberculosis targets with high accuracy. Conclusion This work provides an analytical framework for modeling large-scale chemical-genetic datasets for predicting CGIPs and generating hypothesis about mechanism of action of novel drugs. In addition, this work highlights the importance of graph-based deep neural networks in drug discovery. Supplementary Information The online version contains supplementary material available at 10.1186/s13321-022-00596-6.
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Multi-Phase In Silico Discovery of Potential SARS-CoV-2 RNA-Dependent RNA Polymerase Inhibitors among 3009 Clinical and FDA-Approved Related Drugs. Processes (Basel) 2022. [DOI: 10.3390/pr10030530] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Proceeding our prior studies of SARS-CoV-2, the inhibitory potential against SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) has been investigated for a collection of 3009 clinical and FDA-approved drugs. A multi-phase in silico approach has been employed in this study. Initially, a molecular fingerprint experiment of Remdesivir (RTP), the co-crystallized ligand of the examined protein, revealed the most similar 150 compounds. Among them, 30 compounds were selected after a structure similarity experiment. Subsequently, the most similar 30 compounds were docked against SARS-CoV-2 RNA-dependent RNA polymerase (PDB ID: 7BV2). Aloin 359, Baicalin 456, Cefadroxil 1273, Sophoricoside 1459, Hyperoside 2109, and Vitexin 2286 exhibited the most precise binding modes, as well as the best binding energies. To confirm the obtained results, MD simulations experiments have been conducted for Hyperoside 2109, the natural flavonoid glycoside that exhibited the best docking scores, against RdRp (PDB ID: 7BV2) for 100 ns. The achieved results authenticated the correct binding of 2109, showing low energy and optimum dynamics. Our team presents these outcomes for scientists all over the world to advance in vitro and in vivo examinations against COVID-19 for the promising compounds.
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Huang W, Zhang L, Li Z. Advances in computer-aided drug design for type 2 diabetes. Expert Opin Drug Discov 2022; 17:461-472. [PMID: 35254188 DOI: 10.1080/17460441.2022.2047644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION The number of diabetic patients is increasing, posing a heavy social and economic burden worldwide. Traditional drug development technology is time-consuming and costly, and the emergence of computer-aided drug design (CADD) has changed this situation. This study reviews the applications of CADD in diabetic drug designing. AREAS COVERED In this article, the authors focus on the advance in CADD in diabetic drug design by elaborating the discovery, including peroxisome proliferator-activated receptor (PPAR), G protein-coupled receptor 40 (GPR40), dipeptidyl peptidase-IV (DDP-IV), protein tyrosine phosphatase 1B (PTP1B), sodium-dependent glucose transporter 2 (SGLT-2), and glucokinase (GK). Some drug discovery of these targets is related to CADD strategies. EXPERT OPINION There is no doubt that CADD has contributed to the discovery of novel anti-diabetic agents. However, there are still many limitations and challenges, such as lack of co-crystal complex, dynamic simulations, water, and metal ion treatment. In the near future, artificial intelligence (AI) may be a promising strategy to accelerate drug discovery and reduce costs by identifying candidates. Moreover, AlphaFold, a deep learning model that predicts the 3D structure of proteins, represents a considerable advancement in the structural prediction of proteins, especially in the absence of homologous templates for protein structures.
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Affiliation(s)
- Wanqiu Huang
- School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou, PR China.,Key Laboratory of New Drug Discovery and Evaluation, Guangdong Pharmaceutical University, Guangzhou, PR China.,Guangzhou Key Laboratory of Construction and Application of New Drug Screening Model Systems, Guangdong Pharmaceutical University, Guangzhou, PR China
| | - Luyong Zhang
- School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou, PR China.,Key Laboratory of New Drug Discovery and Evaluation, Guangdong Pharmaceutical University, Guangzhou, PR China.,Guangzhou Key Laboratory of Construction and Application of New Drug Screening Model Systems, Guangdong Pharmaceutical University, Guangzhou, PR China.,Jiangsu Key Laboratory of Drug Screening, China Pharmaceutical University, Nanjing, PR China
| | - Zheng Li
- School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou, PR China.,Key Laboratory of New Drug Discovery and Evaluation, Guangdong Pharmaceutical University, Guangzhou, PR China
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Abstract
Drug design is a complex pharmaceutical science with a long history. Many achievements have been made in the field of drug design since the end of 19th century, when Emil Fisher suggested that the drug-receptor interaction resembles the key and lock interplay. Gradually, drug design has been transformed into a coherent and well-organized science with a solid theoretical background and practical applications. Now, drug design is the most advanced approach for drug discovery. It utilizes the innovations in science and technology and includes them in its wide-ranging arsenal of methods and tools in order to achieve the main goal: discovery of effective, specific, non-toxic, safe and well-tolerated drugs. Drug design is one of the most intensively developing modern sciences and its progress is accelerated by the implication of artificial intelligence. The present review aims to capture some of the most important milestones in the development of drug design, to outline some of the most used current methods and to sketch the future perspective according to the author's point of view. Without pretending to cover fully the wide range of drug design topics, the review introduces the reader to the content of Molecules' Special Issue "Drug Design-Science and Practice".
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Affiliation(s)
- Irini Doytchinova
- Drug Design and Bioinformatics Lab, Faculty of Pharmacy, Medical University of Sofia, 1000 Sofia, Bulgaria
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Bahalul Haque AKM, Bhushan B, Nawar A, Talha KR, Ayesha SJ. Attacks and Countermeasures in IoT Based Smart Healthcare Applications. INTELLIGENT SYSTEMS REFERENCE LIBRARY 2022:67-90. [DOI: 10.1007/978-3-030-90119-6_6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Wang B, Wan Z, Li C, Zhang M, Shi Y, Miao X, Jian Y, Luo Y, Yao J, Tian W. Identification of benign and malignant thyroid nodules based on dynamic AI ultrasound intelligent auxiliary diagnosis system. Front Endocrinol (Lausanne) 2022; 13:1018321. [PMID: 36237194 PMCID: PMC9551607 DOI: 10.3389/fendo.2022.1018321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 09/06/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Dynamic artificial intelligence (AI) ultrasound intelligent auxiliary diagnosis system (Dynamic AI) is a joint application of AI technology and medical imaging data, which can perform a real-time synchronous dynamic analysis of nodules. The aim of this study is to investigate the value of dynamic AI in differentiating benign and malignant thyroid nodules and its guiding significance for treatment strategies. METHODS The data of 607 patients with 1007 thyroid nodules who underwent surgical treatment were reviewed and analyzed, retrospectively. Dynamic AI was used to differentiate benign and malignant nodules. The diagnostic efficacy of dynamic AI was evaluated by comparing the results of dynamic AI examination, preoperative fine needle aspiration cytology (FNAC) and postoperative pathology of nodules with different sizes and properties in patients of different sexes and ages. RESULTS The sensitivity, specificity and accuracy of dynamic AI in the diagnosis of thyroid nodules were 92.21%, 83.20% and 89.97%, respectively, which were highly consistent with the postoperative pathological results (kappa = 0.737, p < 0.001). There is no statistical difference in accuracy between people with different ages and sexes and nodules of different sizes, which showed the good stability. The accuracy of dynamic AI in malignant nodules (92.21%) was significantly higher than that in benign nodules (83.20%) (p < 0.001). The specificity and positive predictive value were significantly higher, and the misdiagnosis rate was significantly lower in dynamic AI than that of preoperative ultrasound ACR TI-RADS (p < 0.001). The accuracy of dynamic AI in nodules with diameter ≤ 0.50 cm was significantly higher than that of preoperative ultrasound (p = 0.044). Compared with FNAC, the sensitivity (96.58%) and accuracy (94.06%) of dynamic AI were similar. CONCLUSIONS The dynamic AI examination has high diagnostic value for benign and malignant thyroid nodules, which can effectively assist surgeons in formulating scientific and reasonable individualized diagnosis and treatment strategies for patients.
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Affiliation(s)
- Bing Wang
- Senior Department of General Surgery, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Zheng Wan
- Senior Department of General Surgery, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Chen Li
- Senior Department of General Surgery, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Mingbo Zhang
- Department of Ultrasound, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - YiLei Shi
- MedAI Technology (Wuxi) Co. Ltd, Wuxi, China
| | - Xin Miao
- Senior Department of General Surgery, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Yanbing Jian
- Senior Department of General Surgery, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Yukun Luo
- Department of Ultrasound, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Jing Yao
- Senior Department of General Surgery, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
- *Correspondence: Jing Yao, ; Wen Tian,
| | - Wen Tian
- Senior Department of General Surgery, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
- *Correspondence: Jing Yao, ; Wen Tian,
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