Displaying publications 1 - 20 of 171 in total

Abstract:
Sort:
  1. Fulltext Amino-Acid-Conjugated Natural Compounds: Aims, Designs and Results
    Arifian H, Maharani R, Megantara S, Gazzali AM, Muchtaridi M
    Molecules, 2022 Nov 07;27(21).
    PMID: 36364457 DOI: 10.3390/molecules27217631
    Protein is one of the essential macronutrients required by all living things. The breakdown of protein produces monomers known as amino acids. The concept of conjugating natural compounds with amino acids for therapeutic applications emerged from the fact that amino acids are important building blocks of life and are abundantly available; thus, a greater shift can result in structural modification, since amino acids contain a variety of sidechains. This review discusses the data available on amino acid-natural compound conjugates that were reported with respect to their backgrounds, the synthetic approach and their bioactivity. Several amino acid-natural compound conjugates have shown enhanced pharmacokinetic characteristics, including absorption and distribution properties, reduced toxicity and increased physiological effects. This approach could offer a potentially effective system of drug discovery that can enable the development of pharmacologically active and pharmacokinetically acceptable molecules.
    Matched MeSH terms: Drug Discovery
  2. Artificial intelligence in drug development: present status and future prospects
    Mak KK, Pichika MR
    Drug Discov Today, 2019 03;24(3):773-780.
    PMID: 30472429 DOI: 10.1016/j.drudis.2018.11.014
    Artificial intelligence (AI) uses personified knowledge and learns from the solutions it produces to address not only specific but also complex problems. Remarkable improvements in computational power coupled with advancements in AI technology could be utilised to revolutionise the drug development process. At present, the pharmaceutical industry is facing challenges in sustaining their drug development programmes because of increased R&D costs and reduced efficiency. In this review, we discuss the major causes of attrition rates in new drug approvals, the possible ways that AI can improve the efficiency of the drug development process and collaboration of pharmaceutical industry giants with AI-powered drug discovery firms.
    Matched MeSH terms: Drug Discovery*
  3. Senolytics: Opening avenues in drug discovery to find novel therapeutics for Parkinson's Disease
    Kakoty V, Kalarikkal Chandran S, Gulati M, Goh BH, Dua K, Kumar Singh S
    Drug Discov Today, 2023 Jun;28(6):103582.
    PMID: 37023942 DOI: 10.1016/j.drudis.2023.103582
    Aging is one of the major risk factors for most neurodegenerative disorders including Parkinson's disease (PD). More than 10 million people are affected with PD worldwide. One of the predominant factors accountable for progression of PD pathology could be enhanced accumulation of senescent cells in the brain with the progress of age. Recent investigations have highlighted that senescent cells can ignite PD pathology via increased oxidative stress and neuroinflammation. Senolytics are agents that kill senescent cells. This review mainly focuses on understanding the pathological connection between senescence and PD, with emphasis on some of the recent advances made in the area of senolytics and their evolution to potential clinical candidates for future pharmaceuticals against PD.
    Matched MeSH terms: Drug Discovery
  4. Weighted voting-based consensus clustering for chemical structure databases
    Saeed F, Ahmed A, Shamsir MS, Salim N
    J Comput Aided Mol Des, 2014 Jun;28(6):675-84.
    PMID: 24830925 DOI: 10.1007/s10822-014-9750-2
    The cluster-based compound selection is used in the lead identification process of drug discovery and design. Many clustering methods have been used for chemical databases, but there is no clustering method that can obtain the best results under all circumstances. However, little attention has been focused on the use of combination methods for chemical structure clustering, which is known as consensus clustering. Recently, consensus clustering has been used in many areas including bioinformatics, machine learning and information theory. This process can improve the robustness, stability, consistency and novelty of clustering. For chemical databases, different consensus clustering methods have been used including the co-association matrix-based, graph-based, hypergraph-based and voting-based methods. In this paper, a weighted cumulative voting-based aggregation algorithm (W-CVAA) was developed. The MDL Drug Data Report (MDDR) benchmark chemical dataset was used in the experiments and represented by the AlogP and ECPF_4 descriptors. The results from the clustering methods were evaluated by the ability of the clustering to separate biologically active molecules in each cluster from inactive ones using different criteria, and the effectiveness of the consensus clustering was compared to that of Ward's method, which is the current standard clustering method in chemoinformatics. This study indicated that weighted voting-based consensus clustering can overcome the limitations of the existing voting-based methods and improve the effectiveness of combining multiple clusterings of chemical structures.
    Matched MeSH terms: Drug Discovery*
  5. Unveiling dynamics of nitrogen content and selected nitrogen heterocycles in thrombin inhibitors: a ceteris paribus approach
    Masand VH, Al-Hussain S, Alzahrani AY, Al-Mutairi AA, Sultan Alqahtani A, Samad A, et al.
    Expert Opin Drug Discov, 2024 Aug;19(8):991-1009.
    PMID: 38898679 DOI: 10.1080/17460441.2024.2368743
    BACKGROUND: Despite the progress in comprehending molecular design principles and biochemical processes associated with thrombin inhibition, there is a crucial need to optimize efforts and curtail the recurrence of synthesis-testing cycles. Nitrogen and N-heterocycles are key features of many anti-thrombin drugs. Hence, a pragmatic analysis of nitrogen and N-heterocycles in thrombin inhibitors is important throughout the drug discovery pipeline. In the present work, the authors present an analysis with a specific focus on understanding the occurrence and distribution of nitrogen and selected N-heterocycles in the realm of thrombin inhibitors.

    RESEARCH DESIGN AND METHODS: A dataset comprising 4359 thrombin inhibitors is used to scrutinize various categories of nitrogen atoms such as ring, non-ring, aromatic, and non-aromatic. In addition, selected aromatic and aliphatic N-heterocycles have been analyzed.

    RESULTS: The analysis indicates that ~62% of thrombin inhibitors possess five or fewer nitrogen atoms. Substituted N-heterocycles have a high occurrence, like pyrrolidine (23.24%), pyridine (20.56%), piperidine (16.10%), thiazole (9.61%), imidazole (7.36%), etc. in thrombin inhibitors.

    CONCLUSIONS: The majority of active thrombin inhibitors contain nitrogen atoms close to 5 and a combination of N-heterocycles like pyrrolidine, pyridine, piperidine, etc. This analysis provides crucial insights to optimize the transformation of lead compounds into potential anti-thrombin inhibitors.

    Matched MeSH terms: Drug Discovery/methods
  6. Annonaceae: bio-resource for tomorrow's drug discovery
    Aminimoghadamfarouj N, Nematollahi A, Wiart C
    J Asian Nat Prod Res, 2011 May;13(5):465-76.
    PMID: 21534046 DOI: 10.1080/10286020.2011.570265
    One of the rich sources of lead compounds is the Angiosperms. Many of these lead compounds are useful medicines naturally, whereas others have been used as the basis for synthetic agents. These are potent and effective compounds, which have been obtained from plants, including anti-cancer (cytotoxic) agents, anti-malaria (anti-protozoal) agents, and anti-bacterial agents. Today, the number of plant families that have been extensively studied is relatively very few and the vast majorities have not been studied at all. The Annonaceae is the largest family in the order Magnoliales. It includes tropical trees, bushes, and climbers, which are often used as traditional remedies in Southeast Asia. Members of the Annonaceae have the particularity to elaborate a broad spectrum of natural products that have displayed anti-bacterial, anti-fungal, and anti-protozoal effects and have been used for the treatment of medical conditions, such as skin diseases, intestinal worms, inflammation of the eyes, HIV, and cancer. These special effects and the vast range of variation in potent compounds make the Annonaceae unique from other similar families in the Magnoliales and the Angiosperms in general. This paper attempts to summarize some important information and discusses a series of hypotheses about the effects of Annonaceae compounds.
    Matched MeSH terms: Drug Discovery*
  7. Fulltext Bioactive Molecule Prediction Using Extreme Gradient Boosting
    Babajide Mustapha I, Saeed F
    Molecules, 2016 Jul 28;21(8).
    PMID: 27483216 DOI: 10.3390/molecules21080983
    Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today's drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting Machine, was investigated for the prediction of biological activity based on quantitative description of the compound's molecular structure. Seven datasets, well known in the literature were used in this paper and experimental results show that Xgboost can outperform machine learning algorithms like Random Forest (RF), Support Vector Machines (LSVM), Radial Basis Function Neural Network (RBFN) and Naïve Bayes (NB) for the prediction of biological activities. In addition to its ability to detect minority activity classes in highly imbalanced datasets, it showed remarkable performance on both high and low diversity datasets.
    Matched MeSH terms: Drug Discovery/methods*
  8. Discovery of new targeting agents against GAPDH receptor for antituberculosis drug delivery
    Noh MAA, Fazalul Rahiman SS, A Wahab H, Mohd Gazzali A
    J Basic Clin Physiol Pharmacol, 2021 Jun 25;32(4):715-722.
    PMID: 34214294 DOI: 10.1515/jbcpp-2020-0435
    OBJECTIVES: Tuberculosis (TB) remains a public health concern due to the emergence and evolution of multidrug-resistant strains. To overcome this issue, reinforcing the effectiveness of first line antituberculosis agents using targeted drug delivery approach is an option. Glyceraldehyde-3-Phosphate Dehydrogenase (GADPH), a common virulence factor found in the pathogenic microorganisms has recently been discovered on the cell-surface of Mycobacterium tuberculosis, allowing it to be used as a drug target for TB. This study aims to discover active small molecule(s) that target GAPDH and eventually enhance the delivery of antituberculosis drugs.

    METHODS: Ten ligands with reported in vitro and/or in vivo activities against GAPDH were evaluated for their binding interactions through molecular docking studies using AutoDock 4.2 program. The ligand with the best binding energy was then modified to produce 10 derivatives, which were redocked against GAPDH using previous protocols. BIOVIA Discovery Studio Visualizer 2019 was used to explore the ligand-receptor interactions between the derivatives and GAPDH.

    RESULTS: Among the 10 ligands, curcumin, koningic acid and folic acid showed the best binding energies. Further analysis on the docking of two folic acid derivatives, F7 (γ-{[tert-butyl-N-(6-aminohexyl)]carbamate}folic acid) and F8 (folic acid N-hydroxysuccinimide ester) showed that the addition of a bulky substituent at the carboxyl group of the glutamic acid subcomponent resulted in improved binding energy.

    CONCLUSIONS: Folic acid and the two derivatives F7 and F8 have huge potentials to be developed as targeting agents against the GAPDH receptor. Further study is currently on-going to evaluate the effectiveness of these molecules in vitro.

    Matched MeSH terms: Drug Discovery*
  9. Benzimidazoles in Drug Discovery: A Patent Review
    Law CSW, Yeong KY
    ChemMedChem, 2021 06 17;16(12):1861-1877.
    PMID: 33646618 DOI: 10.1002/cmdc.202100004
    Benzimidazole is a heterocyclic ring system that has been widely studied in the pharmaceutical field. For the past decade, numerous benzimidazole derivatives have been synthesized and evaluated for their wide range of pharmacological activities, which are beneficial for drug development. This article presents the biological effects of benzimidazole derivatives in each invention from 2015 to 2020. Two patent databases, Google Patents and Lens, were used to locate relevant granted patent applications. Specifically, this review delineates the role of patented benzimidazoles from a disease-centric perspective and examines the mechanisms of action of these compounds in related diseases. Most of the benzimidazoles have shown good activities against various target proteins. Whilst several of them have progressed into clinical trials, most patents presented novel therapeutic approaches for respective target diseases. Hence, their potential in being developed into clinical drugs are also discussed.
    Matched MeSH terms: Drug Discovery*
  10. Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors
    Shiammala PN, Duraimutharasan NKB, Vaseeharan B, Alothaim AS, Al-Malki ES, Snekaa B, et al.
    Methods, 2023 Nov;219:82-94.
    PMID: 37778659 DOI: 10.1016/j.ymeth.2023.09.010
    Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way new drugs are discovered and developed. As AI technology continues to evolve, it is likely that AI will play an even greater role in the future of drug discovery. AI is used to identify new drug targets, design new molecules, and predict the efficacy and safety of potential drugs. The inclusion of AI in drug discovery can screen millions of compounds in a matter of hours, identifying potential drug candidates that would have taken years to find using traditional methods. AI is highly utilized in the pharmaceutical industry by optimizing processes, reducing waste, and ensuring quality control. This review covers much-needed topics, including the different types of machine-learning techniques, their applications in drug discovery, and the challenges and limitations of using machine learning in this field. The state-of-the-art of AI-assisted pharmaceutical discovery is described, covering applications in structure and ligand-based virtual screening, de novo drug creation, prediction of physicochemical and pharmacokinetic properties, drug repurposing, and related topics. Finally, many obstacles and limits of present approaches are outlined, with an eye on potential future avenues for AI-assisted drug discovery and design.
    Matched MeSH terms: Drug Discovery/methods
  11. Fulltext The Future of Clinical Trial Design in Oncology
    Spreafico A, Hansen AR, Abdul Razak AR, Bedard PL, Siu LL
    Cancer Discov, 2021 Apr;11(4):822-837.
    PMID: 33811119 DOI: 10.1158/2159-8290.CD-20-1301
    Clinical trials represent a fulcrum for oncology drug discovery and development to bring safe and effective medicines to patients in a timely manner. Clinical trials have shifted from traditional studies evaluating cytotoxic chemotherapy in largely histology-based populations to become adaptively designed and biomarker-driven evaluations of molecularly targeted agents and immune therapies in selected patient subsets. This review will discuss the scientific, methodological, practical, and patient-focused considerations to transform clinical trials. A call to action is proposed to establish the framework for next-generation clinical trials that strikes an optimal balance of operational efficiency, scientific impact, and value to patients. SIGNIFICANCE: The future of cancer clinical trials requires a framework that can efficiently transform scientific discoveries to clinical utility through applications of innovative technologies and dynamic design methodologies. Next-generation clinical trials will offer individualized strategies which ultimately contribute to globalized knowledge and collective learning, through the joint efforts of all key stakeholders including investigators and patients.
    Matched MeSH terms: Drug Discovery/trends
  12. Fulltext Multicriteria decision making attributes and estimation of physicochemical properties of kidney cancer drugs via topological descriptors
    Husin MN, Khan AR, Awan NUH, Campena FJH, Tchier F, Hussain S
    PLoS One, 2024;19(5):e0302276.
    PMID: 38713692 DOI: 10.1371/journal.pone.0302276
    Based on topological descriptors, QSPR analysis is an incredibly helpful statistical method for examining many physical and chemical properties of compounds without demanding costly and time-consuming laboratory tests. Firstly, we discuss and provide research on kidney cancer drugs using topological indices and done partition of the edges of kidney cancer drugs which are based on the degree. Secondly, we examine the attributes of nineteen drugs casodex, eligard, mitoxanrone, rubraca, and zoladex, etc and among others, using linear QSPR model. The study in the article not only demonstrates a good correlation between TIs and physical characteristics with the QSPR model being the most suitable for predicting complexity, enthalpy, molar refractivity, and other factors and a best-fit model is attained in this study. This theoretical approach might benefit chemists and professionals in the pharmaceutical industry to forecast the characteristics of kidney cancer therapies. This leads towards new opportunities to paved the way for drug discovery and the formation of efficient and suitable treatment options in therapeutic targeting. We also employed multicriteria decision making techniques like COPRAS and PROMETHEE-II for ranking of said disease treatment drugs and physicochemical characteristics.
    Matched MeSH terms: Drug Discovery/methods
  13. Advances in artificial intelligence for drug delivery and development: A comprehensive review
    Gholap AD, Uddin MJ, Faiyazuddin M, Omri A, Gowri S, Khalid M
    Comput Biol Med, 2024 Aug;178:108702.
    PMID: 38878397 DOI: 10.1016/j.compbiomed.2024.108702
    Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.
    Matched MeSH terms: Drug Discovery/methods
  14. Primary liver cells cultured on carbon nanotube substrates for liver tissue engineering and drug discovery applications
    Che Abdullah CA, Azad CL, Ovalle-Robles R, Fang S, Lima MD, Lepró X, et al.
    ACS Appl Mater Interfaces, 2014 Jul 9;6(13):10373-80.
    PMID: 24933259 DOI: 10.1021/am5018489
    Here, we explore the use of two- and three-dimensional scaffolds of multiwalled-carbon nanotubes (MWNTs) for hepatocyte cell culture. Our objective is to study the use of these scaffolds in liver tissue engineering and drug discovery. In our experiments, primary rat hepatocytes, the parenchymal (main functional) cell type in the liver, were cultured on aligned nanogrooved MWNT sheets, MWNT yarns, or standard 2-dimensional culture conditions as a control. We find comparable cell viability between all three culture conditions but enhanced production of the hepatocyte-specific marker albumin for cells cultured on MWNTs. The basal activity of two clinically relevant cytochrome P450 enzymes, CYP1A2 and CYP3A4, are similar on all substrates, but we find enhanced induction of CYP1A2 for cells on the MWNT sheets. Our data thus supports the use of these substrates for applications including tissue engineering and enhancing liver-specific functions, as well as in in vitro model systems with enhanced predictive capability in drug discovery and development.
    Matched MeSH terms: Drug Discovery*
  15. Building a discovery partnership with Sarawak Biodiversity Centre: a gateway to access natural products from the rainforests
    Yeo TC, Naming M, Manurung R
    Comb Chem High Throughput Screen, 2014 Mar;17(3):192-200.
    PMID: 24409959
    The Sarawak Biodiversity Centre (SBC) is a state government agency which regulates research and promotes the sustainable use of biodiversity. It has a program on documentation of traditional knowledge (TK) and is well-equipped with facilities for natural product research. SBC maintains a Natural Product Library (NPL) consisting of local plant and microbial extracts for bioprospecting. The NPL is a core discovery platform for screening of bioactive compounds by researchers through a formal agreement with clear benefit sharing obligations. SBC aims to develop partnerships with leading institutions and the industries to explore the benefits of biodiversity.
    Matched MeSH terms: Drug Discovery*
  16. Recent progress in adjuvant discovery for peptide-based subunit vaccines
    Azmi F, Ahmad Fuaad AA, Skwarczynski M, Toth I
    Hum Vaccin Immunother, 2014;10(3):778-96.
    PMID: 24300669
    Peptide-based subunit vaccines are of great interest in modern immunotherapy as they are safe, easy to produce and well defined. However, peptide antigens produce a relatively weak immune response, and thus require the use of immunostimulants (adjuvants) for optimal efficacy. Developing a safe and effective adjuvant remains a challenge for peptide-based vaccine design. Recent advances in immunology have allowed researchers to have a better understanding of the immunological implication of related diseases, which facilitates more rational design of adjuvant systems. Understanding the molecular structure of the adjuvants allows the establishment of their structure-activity relationships which is useful for the development of next-generation adjuvants. This review summarizes the current state of adjuvants development in the field of synthetic peptide-based vaccines. The structural, chemical and biological properties of adjuvants associated with their immunomodulatory effects are discussed.
    Matched MeSH terms: Drug Discovery/trends
  17. Natural Products Towards the Discovery of Potential Future Antithrombotic Drugs
    Islam MA, Alam F, Khalil MI, Sasongko TH, Gan SH
    Curr Pharm Des, 2016;22(20):2926-46.
    PMID: 26951101
    Globally, thrombosis-associated disorders are one of the main contributors to fatalities. Besides genetic influences, there are some acquired and environmental risk factors dominating thrombotic diseases. Although standard regimens have been used for a long time, many side effects still occur which can be life threatening. Therefore, natural products are good alternatives. Although the quest for antithrombotic natural products came to light only since the end of last century, in the last two decades, a considerable number of natural products showing antithrombotic activities (antiplatelet, anticoagulant and fibrinolytic) with no or minimal side effects have been reported. In this review, several natural products used as antithrombotic agents including medicinal plants, vegetables, fruits, spices and edible mushrooms which have been discovered in the last 15 years and their target sites (thrombogenic components, factors and thrombotic pathways) are described. In addition, the side effects, limitations and interactions of standard regimens with natural products are also discussed. The active compounds could serve as potential sources for future research on antithrombotic drug development. As a future direction, more advanced researches (in quest of the target cofactor or component involved in antithrombotic pathways) are warranted for the development of potential natural antithrombotic medications (alone or combined with standard regimens) to ensure maximum safety and efficacy.
    Matched MeSH terms: Drug Discovery*
  18. Peptidyl-prolyl isomerases: functionality and potential therapeutic targets in cardiovascular disease
    Rostam MA, Piva TJ, Rezaei HB, Kamato D, Little PJ, Zheng W, et al.
    Clin Exp Pharmacol Physiol, 2015 Feb;42(2):117-24.
    PMID: 25377120 DOI: 10.1111/1440-1681.12335
    Peptidyl-prolyl cis/trans isomerases (PPIases) are a conserved group of enzymes that catalyse the conversion between cis and trans conformations of proline imidic peptide bonds. These enzymes play critical roles in regulatory mechanisms of cellular function and pathophysiology of disease. There are three different classes of PPIases and increasing interest in the development of specific PPIase inhibitors. Cyclosporine A, FK506, rapamycin and juglone are known PPIase inhibitors. Herein, we review recent advances in elucidating the role and regulation of the PPIase family in vascular disease. We focus on peptidyl-prolyl cis/trans isomerase NIMA-interacting 1 (Pin1), an important member of the PPIase family that plays a role in cell cycle progression, gene expression, cell signalling and cell proliferation. In addition, Pin1 may be involved in atherosclerosis. The unique role of Pin1 as a molecular switch that impacts on multiple downstream pathways necessitates the evaluation of a highly specific Pin1 inhibitor to aid in potential therapeutic drug discovery.
    Matched MeSH terms: Drug Discovery/methods
  19. Fulltext Potential of Endophytic Diaporthe sp. as a New Source of Bioactive Compounds
    Nagarajan K, Tong WY, Leong CR, Tan WN
    J Microbiol Biotechnol, 2021 Apr 28;31(4):493-500.
    PMID: 32627761 DOI: 10.4014/jmb.2005.05012
    Endophytic fungi are symbiotically related to plants and spend most of their life cycle within them. In nature, they have a crucial role in plant micro-ecosystem. They are harnessed for their bioactive compounds to counter human health problems and diseases. Endophytic Diaporthe sp. is a widely distributed fungal genus that has garnered much interest within the scientific community. A substantial number of secondary metabolites have been detected from Diaporthe sp. inhabited in various plants. As such, this minireview highlights the potential of Diaporthe sp. as a rich source of bioactive compounds by emphasizing on their diverse chemical entities and potent biological properties. The bioactive compounds produced are of significant importance to act as new lead compounds for drug discovery and development.
    Matched MeSH terms: Drug Discovery*
  20. Computer-Aided Drug Discovery (CADD) Approaches for the Management of Neuropathic Pain
    Ramesh M, Muthuraman A
    Curr Top Med Chem, 2021;21(32):2856-2868.
    PMID: 34809547 DOI: 10.2174/1568026621666211122161932
    Neuropathic pain occurs due to physical damage, injury, or dysfunction of neuronal fibers. The pathophysiology of neuropathic pain is too complex. Therefore, an accurate and reliable prediction of the appropriate hits/ligands for the treatment of neuropathic pain is a challenging process. However, computer-aided drug discovery approaches contributed significantly to discovering newer hits/ligands for the treatment of neuropathic pain. The computational approaches like homology modeling, induced-fit molecular docking, structure-activity relationships, metadynamics, and virtual screening were cited in the literature for the identification of potential hit molecules against neuropathic pain. These hit molecules act as inducible nitric oxide synthase inhibitors, FLAT antagonists, TRPA1 modulators, voltage-gated sodium channel binder, cannabinoid receptor-2 agonists, sigma-1 receptor antagonists, etc. Sigma-1 receptor is a distinct type of opioid receptor and several patents were obtained for sigma-1 receptor antagonists for the treatment of neuropathic pain. These molecules were found to have a profound role in the management of neuropathic pain. The present review describes the validated therapeutic targets, potential chemical scaffolds, and crucial protein-ligand interactions for the management of neuropathic pain based on the recently reported computational methodologies of the present and past decades. The study can help the researcher to discover newer drugs/drug-like molecules against neuropathic pain.
    Matched MeSH terms: Drug Discovery*
Related Terms
Filters
Contact Us

Please provide feedback to Administrator ([email protected])

External Links