Current drug discovery technologies最新文献

{"title":"Exploration of Bacopa monnieri-derived Green Ligands for Targeted IDH1 Inhibition in Glioblastoma.","authors":"Mudra Sikenis, Anjali Saxena, Abhinav Nair, Biswajit Saha, Subhabrata Kar","doi":"10.2174/0115701638486071260907064647","DOIUrl":"https://doi.org/10.2174/0115701638486071260907064647","url":null,"abstract":"<p><strong>Background: </strong>Sustainable drug discovery is an emerging paradigm in biomedicine driven by green chemistry principles, which allow mindful utilization of favorable renewable biological scaffolds and energy-efficient computational strategies. Plant-derived phytochemicals represent an ecologically reliable alternative to conventional synthetic drug libraries, consistent with the principles of low-carbon use and resource-effective innovation. In this study, phytocompounds from Bacopa monnieri were screened against wild-type and mutant Isocitrate Dehydrogenase 1 (IDH1), which is a key enzyme involved in glioblastoma pathogenesis.</p><p><strong>Method: </strong>This study employed a comprehensive, multi-layered computational approach comprising Protein-Protein Interaction (PPI) networks and Gene Ontology (GO) annotations, KEGG pathway enrichment, molecular docking, ADME profiling, and Molecular Dynamics (MD) simulations.</p><p><strong>Result: </strong>The network analysis revealed that IDH1, IDH3A, IDH3B, CS, and OGDH were identified as major regulators of oncogenic metabolic pathways. Further, enrichment analysis associated these proteins with key processes involved in tumor metabolism and redox regulation. Molecular docking identified Bacoside A2 as the most favorable compound with high affinity for wild-type and mutant IDH1's catalytic residues, followed by Bacopaside VII. MD simulation studies confirmed that Bacoside A2 forms a stable, energetically favorable complex within the catalytic pocket, which remained consistent throughout the simulation. In addition to its potential medical applications, this computational based phytochemical study reflects the principles of green energy and green chemistry by combining renewable plant-based scaffolds with energy-efficient in silico techniques, thereby greatly reducing laboratory resource consumption.</p><p><strong>Discussion: </strong>The integrated system-level network analysis, combined with computational docking and molecular dynamics studies, supported the feasibility of using the renewable phytochemical scaffold, Bacoside A2, as a potential dual inhibitor of IDH1 in central nervous system tumors.</p><p><strong>Conclusion: </strong>Bacoside A2 emerges as a strong, nanotechnologically versatile phytocompound and a dual IDH1 inhibitor, with potential for use in the development of next-generation, natural product-based therapies against glioblastoma and other malignancies associated with IDH1.</p>","PeriodicalId":93962,"journal":{"name":"Current drug discovery technologies","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149013371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI Frameworks for the Early Detection and Management of Diabetic Retinopathy: A Real-World Application.","authors":"Harshita, Saumya Das, Priyanka Bansal","doi":"10.2174/0115701638445311260825100649","DOIUrl":"https://doi.org/10.2174/0115701638445311260825100649","url":null,"abstract":"<p><p>Diabetic retinopathy (DR) is a condition that progressively affects the microvasculature, commonly seen in individuals with diabetes, which is a leading cause of visual impairment across the globe. Approximately one-third of people with diabetes will eventually develop DR, and it remains a public health issue because the retinal damage is irreversible if the disease continues to go undiagnosed and untreated. Although various screening methods exist, including fundus photography and optical coherence tomography (OCT), they are often limited as a result of availability and expertise required for diagnosis and management. Recent advancements in artificial intelligence (AI) are transforming DR screening and diagnosis. AI has demonstrated the potential to automate retinal image evaluation with accuracies that, in some studies, approach or exceed existing standards of care; however, reported performance varies across algorithms, datasets, and clinical settings, highlighting the need for cautious interpretation and further validation in diverse populations. Machine learning (ML) & deep learning (DL) models, particularly convolutional neural networks (CNNs), have been found to have excellent performance in identifying DR-related abnormalities, including mild, moderate, and severe DR, and diabetic macular edema, at sensitivities and specificities exceeding traditional screening. AI tools under various names, such as IDx-DR, EyeArt, and Google's AI algorithm, are quick and price-effective tests using an AI solution for early diagnosis of DR in either developed or developing economies. In addition, AI can also support predictive analytics, risk stratification, and treatment decision-making, so that ophthalmologists can provide patients with better management options. Importantly, AI also comes with its own challenges, such as data harmonization and regulatory approval for deploying AI in clinical care. This review will focus on the existing literature on the applications of AI in diagnosing and management of DR and will outline the importance of this innovation.</p>","PeriodicalId":93962,"journal":{"name":"Current drug discovery technologies","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148972784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emerging Therapies in Lung Cancer: A Review on Targeted and Immune Based Interventions.","authors":"K C Geervani, Repollu Maddileti, Kalpi Sania Kausar, Mulla Shaikshavali, Teleke Kumar Swamy, Penugonda Shivarani, E Bhargav","doi":"10.2174/0115701638434891260825091103","DOIUrl":"https://doi.org/10.2174/0115701638434891260825091103","url":null,"abstract":"<p><p>Lung cancer continues to be the foremost cause of cancer-related mortality globally, with non-small cell lung cancer (NSCLC) accounting for nearly 85% of cases. Historically, the prognosis for NSCLC was grim, but advancements in targeted therapies and immunotherapy are transforming treatment paradigms. This review encapsulates significant developments in molecularly targeted agents and immune checkpoint inhibitors, emphasising their clinical efficacy, resistance mechanisms, and innovative therapeutic combinations. A thorough literature search was conducted across PubMed, ClinicalTrials.gov, and other global intellectual property databases to highlight pivotal advancements in lung cancer therapies. Each section focuses on therapies targeting key molecular drivers such as Epidermal Growth Factor Receptor (EGFR), Anaplastic Lymphoma Kinase (ALK), and others, alongside immunotherapies targeting critical pathways like PD-1 and PD-L1. In genetically defined NSCLC subsets, targeted therapies such as Osimertinib and Lorlatinib have significantly enhanced progression-free survival. Similarly, immune checkpoint inhibitors like Nivolumab have redefined care standards, particularly in PD-L1-positive tumours. Nevertheless, challenges such as acquired resistance persist, prompting ongoing exploration of combination strategies and next-generation agents. This article also reviews pivotal clinical trials and newly filed patents, reflecting current and emerging commercial interests. The integration of molecular diagnostics and precision therapies is reshaping lung cancer treatment, with prospective directions aiming to overcome resistance and broaden access to innovative therapies worldwide, including from India and other developing regions.</p>","PeriodicalId":93962,"journal":{"name":"Current drug discovery technologies","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148908322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Therapeutic Potential of Alstonia scholaris (L.) R. Br. in Diabetic Wound Management: An Ethnomedicinal Perspective.","authors":"Santwana Palai, Sarita Jena, Biswakanth Kar, Ritun Patra, Pravas Ranjan Sahoo, Satyajita Sadangi, Kautuk Kumar Sardar","doi":"10.2174/0115701638468670260817115618","DOIUrl":"https://doi.org/10.2174/0115701638468670260817115618","url":null,"abstract":"<p><p>Nowadays, diabetic wounds are a major concern in the medical and animal fields due to delayed wound healing, as reactive oxygen species play a crucial role in the healing process. Pro-inflammatory molecules, such as tumor necrosis factor (TNF-α), matrix metalloproteinase 9 (MMP-9), and interleukin (IL)-1 β, are most prevalent in diabetic wounds, along with anti-inflammatory mediators and cytokines, which exhibit dysregulated macrophage responses. Although various plants exhibit strong anti-inflammatory, antibacterial, and antidiabetic properties, only a few have been used as effective therapeutic agents for diabetic wounds. More plants need to be explored for their effects on diabetic wounds. Alstonia scholaris (L.) R. Br. is Devil's tree, which is a rich source of various alkaloids, such as tubotaiwine, picralinal, and 16-formyl-5-methoxystrictamine, making it a potential therapeutic agent against diabetic wounds. The relevant articles showing the pharmacological effects of the tree, A. scholaris (L.) R. Br. were searched in search engines like Google Scholar, Web of Science databases, and PubMed. It also shows numerous biological properties, such as antioxidant, anti-inflammatory, antibacterial, antifungal, stimulant, carminative, stomachic, hemostatic, inhibitory cytokine, wound-healing, hypoglycemic, improving collagen and hydroxyproline synthesis, repair of skin tears, increasing granulation strength, and helping in wound contraction in models. More research on A. scholaris is needed to prove it as a potential therapeutic regimen against diabetic wounds. Therefore, this review highlights the pharmacological properties and biological effects of A. scholaris, demonstrating its potential as a candidate for the treatment of diabetic wounds.</p>","PeriodicalId":93962,"journal":{"name":"Current drug discovery technologies","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"QSAR Modeling of 1,3,4-Oxadiazole Derivatives for Predicting Anticancer Activity Using SO-PLS Analysis.","authors":"Vijayalakshmi M K, Srinivasan R","doi":"10.2174/0115701638474547260730115509","DOIUrl":"https://doi.org/10.2174/0115701638474547260730115509","url":null,"abstract":"<p><strong>Introduction: </strong>The study aimed to develop a QSAR model for a series of 1,3,4- oxadiazole derivatives to identify molecular determinants influencing anticancer activity and provide a predictive framework for rational drug design.</p><p><strong>Methods: </strong>A QSAR model was constructed using the Simulated Orthogonal Projections to Latent Structures (SO-PLS) approach. Five molecular field descriptors, steric (gauss_s), electrostatic (gauss_e), hydrophobic (gauss_h), hydrogen-bond acceptor (gauss_a), and hydrogen-bond donor (gauss_d), were employed. The model was developed on a training set of 36 compounds and evaluated using Partial Least Squares regression with up to five latent factors. Y-randomization was applied to assess model robustness.</p><p><strong>Results: </strong>The model demonstrated an excellent fit (R² = 0.931) and acceptable predictive ability (R²-CV = 0.478), with Y-randomization confirming robustness (R²-scrambled = 0.679). Field contribution analysis revealed steric (29.52%) and hydrophobic (24.92%) effects as the primary determinants of biological activity, followed by hydrogen-bond donor (18.81%) and acceptor (18.02%) contributions, while electrostatic interactions (8.73%) were less influential. Regression coefficient analysis identified molecular regions where targeted substitutions could enhance activity.</p><p><strong>Discussion: </strong>The findings highlight the critical role of steric and hydrophobic interactions in modulating the anticancer activity of oxadiazole derivatives. Strategic molecular modifications guided by field contributions and regression analysis can improve potency, supporting rational design of novel oxadiazole-based anticancer agents.</p><p><strong>Conclusion: </strong>This QSAR model provides a robust predictive tool for designing 1,3, 4-oxadiazole derivatives with enhanced anticancer activity.</p>","PeriodicalId":93962,"journal":{"name":"Current drug discovery technologies","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148809603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In Silico Multitarget Profiling of Betulinic Acid: ADME/Protox-II Toxicity, Network Pharmacology, and Molecular Docking for Diabetic Osteoporosis.","authors":"Sheenam Sharma, Rishabh Chaudhary, Chirag Sharma, Swarna Dabral, Seema Bansal, Neelam Sharma, Sumeet Gupta","doi":"10.2174/0115701638476828260801050704","DOIUrl":"https://doi.org/10.2174/0115701638476828260801050704","url":null,"abstract":"<p><strong>Introduction: </strong>Diabetic osteoporosis (DO) is a chronic consequence of diabetes mellitus marked by disrupted bone metabolism and elevated fracture risk, with no effective strategies currently available. Betulinic acid (BA) is a natural pentacyclic triterpenoid demonstrating anti-diabetic, anti-inflammatory, and osteoprotective effects. This research aimed to clarify the molecular mechanisms of BA in DO using network pharmacology, molecular docking, and molecular dynamics simulations.</p><p><strong>Methods: </strong>The drug-likeness and pharmacokinetic characteristics of BA were assessed using Swiss ADME, while ProTox-II forecasted oral toxicity. Potential targets of BA and those associated with DO were identified from several databases, and overlapping targets were used to construct a protein-protein interaction (PPI) network in the STRING database, which was then analyzed in Cytoscape 3.7.2. Essential modules were found with MCODE. ShinyGO was used to conduct Gene Ontology Biological Process and Kyoto Encyclopedia of Genes and Genomes pathway enrichment studies. Molecular docking of central targets was performed with AMDock, whereas simulation studies of PPARγ were performed with the Schrödinger software suite.</p><p><strong>Results: </strong>BA complied with Lipinski's rule of five and exhibited a mild oral toxicity profile. Network analysis revealed RXRA, RXRG, RXRB, PTGS2, PPARA, PPARG, PPARD & CYP19A1 as major targets, which were docked with BA to predict binding affinities. Enrichment analysis further revealed BA's role in lipid and glucose metabolism regulation, osteoblast differentiation & modulation of inflammatory pathways. Further, Molecular dynamics validated robust binding affinity of PPARγ in DO.</p><p><strong>Discussion: </strong>The multi-target interactions of BA, especially with key signaling pathways, indicate a broad involvement in the metabolic and inflammatory processes contributing to the development of DO.</p><p><strong>Conclusion: </strong>BA is a secure multi-target treatment option for DO, providing molecular insights for forthcoming experimental validation.</p>","PeriodicalId":93962,"journal":{"name":"Current drug discovery technologies","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148809557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cannabidiol as a Promising Anti-Inflammatory Agent Targeting TYK2: Molecular Docking and Dynamic Simulation Approaches for Therapeutic Applications in Multiple Sclerosis.","authors":"Fatiha Bousselham, Sanae Baghrous, Ikram Ghicha, Youness Kadil, Vigniako Roussaint Dossou-Yovo, Imane Rahmoune, Houda Filali","doi":"10.2174/0115701638440479260724062842","DOIUrl":"https://doi.org/10.2174/0115701638440479260724062842","url":null,"abstract":"<p><strong>Introduction: </strong>Multiple sclerosis (MS) involves chronic inflammation driven by dysregulation of the JAK-STAT pathway. This study aimed to evaluate the potential interaction of natural cannabinoids with TYK2, with a focus on cannabidiol (CBD), using computational in silico approaches.</p><p><strong>Methods: </strong>A combined molecular docking and molecular dynamics (MD) workflow was used. Cannabinoid ligands and the TYK2 structure (JH2 domain) were prepared and docked in MOE, followed by 20 ns MD simulations under NVT and NPT conditions in GROMACS. Complex stability and ligand-protein interactions were analysed.</p><p><strong>Results: </strong>CBD showed a competitive docking score (-7.31 kcal/mol) and a refined RMSD of 0.9198 Å. MD simulations revealed a stable CBD-TYK2 complex, with RMSD fluctuations of 0.15-0.20 nm, which were lower than those observed for the reference inhibitor, deucravacitinib. RMSF analysis revealed a slight increase in local flexibility in specific regions without affecting the overall stability of the protein. The radius of gyration remained stable throughout the simulation, indicating that the protein's compactness was preserved. Hydrogen bond analysis showed fewer but transient interactions for CBD, consistent with a binding mode dominated by hydrophobic interactions.</p><p><strong>Discussion: </strong>These computational results suggest that CBD interacts stably with TYK2 through a distinct binding mode compared to the reference inhibitor, deucravacitinib, while maintaining the structural integrity of the protein.</p><p><strong>Conclusion: </strong>CBD showed a stable predicted interaction with TYK2, supporting its potential as a candidate for further in vitro and in vivo studies in the context of MS-related inflammation.</p>","PeriodicalId":93962,"journal":{"name":"Current drug discovery technologies","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148708771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pharmacogenetics and Pharmacogenomics as Enabling Technologies for Precision Drug Development and Personalized Therapy.","authors":"Nikolet Pavlova, Dimitrios Kaloudas, Martina Traykovska, Robert Penchovsky","doi":"10.2174/0115701638494182260723063335","DOIUrl":"https://doi.org/10.2174/0115701638494182260723063335","url":null,"abstract":"<p><p>Differences among individuals in susceptibility to adverse drug reactions are strongly influenced by genetic variation affecting drug metabolism, transport, immune response, and pharmacodynamics pathways, presenting both challenges and opportunities for drug discovery and development. Genetic and genomic variation can modify pharmacokinetics, pharmacodynamics, immunemediated toxicity, treatment resistance, and biomarker-defined therapeutic benefit, making pharmacogenetics and pharmacogenomics central components of precision medicine. The primary aim of this review is to synthesize how pharmacogenetics and pharmacogenomics function as enabling technologies across the pharmaceutical and clinical domains, with emphasis on biomarker-guided drug development, patient stratification, safety optimization, regulatory translation, precision oncology, sex-related variability, population diversity, artificial intelligence, and multi-omics integration. The review also elucidates the distinction between single-gene pharmacogenetics and genome-wide pharmacogenomics and discusses the practical requirements, procedures, advantages, and challenges associated with implementation. Future progress will depend on harmonized standards, diverse population datasets, preemptive testing linked to electronic health records, explainable artificial intelligence, multi-omics validation, and equitable access to genomic prescribing tools, ensuring that precision therapeutics improve drug safety and efficacy across populations.</p>","PeriodicalId":93962,"journal":{"name":"Current drug discovery technologies","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148708719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI in Pharma R&D: Bridging Breakthroughs and Future Frontiers.","authors":"Dmitry A Kudlay, Oleg V Satyshev, Natalia P Doktorova, Nikolay Yu Nikolenko, Andrey A Svistunov","doi":"10.2174/0115701638418225260706154736","DOIUrl":"https://doi.org/10.2174/0115701638418225260706154736","url":null,"abstract":"<p><p>The review is devoted to the use of artificial intelligence (AI) in scientific research and development to create new or repurpose authorized drug products, as well as to the use of AI-based solutions to discover new biomarkers and shorten the time to diagnosis of various diseases. The following areas of AI application are considered: the search for new pharmacologically active substances, the development of formulations and drug production technology, preclinical trials, and intelligent diagnostics (identification of new biomarkers; development of software products to interpret research results and increase diagnostic accuracy). Examples of AI use by leading pharmaceutical companies and a list of the most popular AI models in drug development are provided. The revolutionary contribution of AI in drug discovery lies in reducing the time to identify new drug candidate molecules by more rapidly identifying potential biotargets, performing virtual screening, optimizing promising candidates based on predictive data on pharmacokinetic and toxicological profiles, and searching for the optimal way to synthesize potential drugs. In addition, another area of AI application is the development of drug-delivery devices and systems that improve patient compliance and usability. This paper presents examples of AI use in intelligent diagnostics that prove their high accuracy and time efficiency compared to conventional methods of diagnostics, risk assessment, and prognosis in oncology, cardiology, and other areas of medicine. The implementation of AI technologies in medicine is intensifying, raising questions of ethics, the quality and adequacy of data, the effectiveness and safety of results for patients, personnel competence and readiness for change, as well as issues related to intellectual property rights.</p>","PeriodicalId":93962,"journal":{"name":"Current drug discovery technologies","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148708773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phytochemical-based Neuroprotection and In-silico Docking-driven Identification of Active Natural Compounds to Combat Neuropathy.","authors":"Swathi V, Chandru R, Shabna Roupal Morais, Latha S, Sailendra Kumar Mahanta, Sisir Nandi","doi":"10.2174/0115701638469363260629064516","DOIUrl":"https://doi.org/10.2174/0115701638469363260629064516","url":null,"abstract":"<p><strong>Introduction: </strong>Neuropathic pain, a devastating neurological disorder attributed to impairment or malfunctioning of the somatosensory system, affecting 10% of the world population. Current therapy emphasizes symptomatic management, featuring high-order side effects. Phytocompounds as neuroprotective agents are of growing interest, and can be screened using structure-based docking, and can act upon a variety of pathways with fewer adverse effects.</p><p><strong>Methods: </strong>A comprehensive literature survey was conducted covering studies published between 2000 and 2022 using scientific databases including PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar. The neuroprotective potential of medicinal plants and their bioactive phytochemicals was screened using in silico experiments targeting neuropathy-related molecular targets, followed by the evaluation of in vitro and in vivo activities.</p><p><strong>Results: </strong>The studies showed that phytochemicals have multitarget neuroprotective activities, including antioxidant activity, modulation of neurotransmitter signaling, inhibition of inflammatory mediators, and modulation of neuropathic signaling ion channels. Several phytochemicals demonstrated notable binding affinities in docking studies, including icariin with NMDA receptors (-12.646 kcal/mol), aegeline with MAO-A (-10.06 kcal/mol) and MAO-B (-10.09 kcal/mol), and zerumbone with cannabinoid receptors CB1 (-7.80 kcal/mol) and CB2 (-9.40 kcal/mol). Other compounds, such as chlorogenic acid, myricetin, rutin, and piperine, also exhibited significant interactions with key neuropathic targets involved in neuroinflammation and pain signaling pathways.</p><p><strong>Discussion: </strong>Docking studies identified that icariin, aegeline, and zerumbone are predicted lead molecules because they can interact with targets of interest, including NMDA, MAO, and CB receptors.</p><p><strong>Conclusion: </strong>Phytochemical neuroprotective drugs offer a promising approach to managing neuropathy. Integration of molecular docking approaches with experimental pharmacological studies provides a powerful strategy for identifying bioactive natural compounds with therapeutic potential. These findings support advancing phytochemicals as lead candidates for the development of safer, more effective treatments for neuropathy and neuropathic pain.</p>","PeriodicalId":93962,"journal":{"name":"Current drug discovery technologies","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148551625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}