Drug Discovery and Molecular Docking (DDMD)
Drug Discovery and Molecular Docking (DDMD) is an International, Peer-reviewed, Open-access journal dedicated to advancing research in computational drug design, molecular docking, and pharmaceutical sciences. Our mission is to provide a platform for scientists, researchers, and industry professionals to share groundbreaking discoveries that enhance drug development and therapeutic innovations.
Phosphodiesterase-5 Inhibitors in Erectile Dysfunction: Human Penile Sexual Physiology and AI/ML Insights into CBAD and Sex-Chromosome Genetic Syndromes
Erectile dysfunction (ED) is a multifactorial disorder with a vascular, endothelial, neurological, endocrine and psychological etiology. This review summarizes human physiology during penile sexual stimulation and of erection, focusing on the nitric oxide–cyclic guanosine monophosphate (NO–cGMP) pathway and the therapeutic potential of phosphodiesterase-5 inhibitors (PDE5Is). The efficacy and clinical significance of sildenafil, tadalafil, vardenafil and avanafil is discussed in detail, along with their safety information. The review also explores congenital bilateral absence of the vas (CBAVD), Jacobs syndrome (47, XYY), De la Chapelle syndrome (46, XX testicular DSD) and Klinefelter syndrome (47, XXY) as it relates to male sexual and reproductive health. Human evidence shows that the outcome of infertility and erectile function may be different. Emerging uses of AI/ML applications are also considered, with the most human evidence so far reported for Klinefelter syndrome, and with limited evidence for CBAVD and rarer sex-chromosome syndromes. The review underscores the need for a deeper exploration of the potential synergy between sexual medicine, reproductive genetics, and AI/ML to provide a more personalized assessment and management of the disease.
Erectile dysfunction, PDE5 inhibitors, penile erection, CBAVD, Klinefelter syndrome, Jacobs syndrome, De la Chapelle syndrome, artificial intelligence, machine learning.
Copyright (c) 2026 Drug Discovery and Molecular Docking (DDMD)
Progeria, Sanfilippo Syndrome, Epidermolysis Bullosa, & Cdkl5 Deficiency: AI-Driven 3D Facial Morphometry, Advanced Gene Therapies, Biomarkers, Genetic Pathologies, Etiology, Epidemiology, Diagnostic Odyssey, Machine Learning
Rare genetic disorders, such as Hutchinson–Gilford Progeria Syndrome (HGPS), Sanfilippo Syndrome (MPS III), Epidermolysis Bullosa (EB) and CDKL5 Deficiency Disorder (CDKL5), are often genetically complex, phenotypically variable and late diagnosed, leading to difficulties in diagnosis and treatment. The use of recent advances in artificial intelligence (AI), machine learning (ML), multi-omics technologies, and the discovery of biomarkers has enhanced early detection of disease, genotype–phenotype correlation, and precision diagnostics. This review covers the latest advancements in AI-assisted diagnosis, genetic pathology, biomarker identification, and the development of novel therapeutic strategies like gene editing, gene replacement therapy, RNA-based therapeutics, and AI-driven drug discovery. The review also touches upon the relevance of explainable AI, clinical decision support systems and integrated multi-omics for personalized medicine. Although progress has been made, there are still many obstacles, including small patient cohorts, disease diversity, efficacy and safety of therapy, ethical issues and regulatory standardization. In conclusion, the combination of AI and cutting-edge molecular medicine holds significant promise for improving the diagnosis, management, and individualized treatment of rare genetic diseases.
Structure-Activity Relationship and Molecular Docking-Guided Development of Andrographolide-Based Antidiabetic Agents
Andrographolide was a type diterpenoid lactone situated in Andrographis paniculata. It has gained attention because it might assist diabetics. Its application, on the other hand, is restricted due to its low oral bioavailability, rapid metabolism and very poor stability in vivo. In this report, we used a rational design strategy to generate semisynthetic andrographolide derivatives (SADRs) and tested for their potency toward targeted enzymes in the glucose metabolism. we synthesised two analogues (P-1 to P-4) and solved the three structures. Molecular docking studies with α-glucosidase and α-amylase reveal the binding mode of our compounds, showing how they inhibit both enzymes. The stable hydrogen bonds and hydrophobic interactions of derivative P-2 and P-3 with component parts of the enzyme are responsible for their strong binding affenity. This means they inhibited the enzyme better than the other compound. The laboratory enzyme inhibition test supported this result, illustrating that P-2 andP-3 had the strong dual inhibitory effect as measuring the standard drug acarbose. In vivo: All experiments were performed on streptozotocin-induced diabetic rats (n=10 per group) versus untreated controls. Design pointed experiments randomised did contain blinded ANOVA confirmed significant, dose-dependent reductions in blood glucose, increased insulin secretion and large decreases in HbA1c were also statistically significant for P–2 at higher doses. The small sample size and lack of long-term follow-up are some significant limitations. Further structure–activity relationship analysis revealed that the maintenance of lactone ring, ideal positioning of hydroxyl groups and balanced hydrophobic–hydrophilic properties are essential for anti-diabitic effectiveness. In silico ADME and toxicity prediction suggested an attractive pharmacokinetic profile, high GI absorption, minimal inhibition of cytochrome P450 enzymes, and reasonably good safety margins for the most actives. In conclusion, these findings demonstrate the potential of structurally optimised andrographolide derivatives as anti-diabetic agents with multi-target activity and underscore the benefit of combining computational docking, structure–activity relationship studies, and biological validation in drug discovery based on natural products.
Andrographolide, Andrographis paniculata, anti-diabetic activity, α-glucosidase, α-amylase, molecular docking, ADME/toxicity, structure–activity relationship, semisynthetic derivatives, natural product optimisation
Sickle Cell Disease, Thalassemia, Leukemia, Lymphoma, Myelodysplastic Syndromes, Hemophilia, Anemia, Eosinophilia, Thrombocytopenia: Pathogens, Pathophysiology, Etiology, Epidemiology, AI, ML Modeling, 3D Fingerprinting, Hematologic Disorders
Hematologic disorders refer to a broad range of both genetic and non-genetic disorders involving the red blood cells, white blood cells, platelets, coagulation factors, and bone marrow, such as sickle cell disease (SCD), thalassemia, leukemia, lymphoma, myelodysplastic disorders, hemophilia, anemia, eosinophilia, and thrombocytopenia. The review highlights contemporary evidence in humans about the etiology, pathophysiology, epidemiology, diagnosis, and computational implications of these disorders. Special attention is paid to the application of artificial intelligence (AI), machine learning (ML), digital pathology, genomics, and multimodal data fusion to their diagnostics and prognosis. The review further describes three-dimensional (3D) morphologic and molecular fingerprinting as an alternative computational method to analyze the spatial morphology of cells and tissues beyond standard microscopy. While AI shows great promise when combined with clinical, laboratory, molecular, and imaging data, its broad application in the clinical setting is constrained by issues such as heterogeneous databases, insufficient multicenter validation, poor model interpretability, and inadequate reporting. Going forward, there needs to be standardization of human databases, explainable AI, prospective validation, and federated learning that protects patient privacy.
Hematologic disorders; Artificial intelligence (AI); Machine learning (ML); Digital hematology; Precision medicine; 3D morphologic fingerprinting; Leukemia; Sickle cell disease (SCD).
Molecular Docking and Target-Based Drug Design of Bioactive Compounds from Withania somnifera Against Breast Cancer Targets
Breast cancer is the most frequently diagnosed malignancy and the second leading cause of cancer mortality among women worldwide, despite advances in screening, surgery, radiotherapy, endocrine therapy and targeted biological agents. PARC) continued to seek new, safer and multitargeted therapeutics, driven by resistance to existing drugs, toxicity, tumor heterogeneity and the high cost of treatment. Medicinal plants are a valuable source of natural products with bioactive scaffolds that can be rationally optimized through modern computational and structure-based design methodologies. Withania somnifera (L.) Dunal, widely known as Ashwagandha, is a primary ingredient of the Ayurvedic system of medication and has been shown to be a key source of steroidal lactones åknown as withanolidesåand other active ingredients such as withanosides, sitoindosides and alkaloids that possess various pharmacological effects and influence anticancer activity. In cellular and animal models, before clinical studies or in vitro work of withaferin A, withanolide A, withanone (withania withanolides), ashwagandhanolide and other constituents have demonstrated inhibition of proliferation; induction of apoptosis; suppression of metastasis; and sensitization to chemotherapeutics through the modulation of multiple signaling pathways including NF‑κB, STAT3, Notch, PI3K/Akt/mTOR and ER signaling. Furthermore, recent advances in molecular docking, molecular dynamics (MD) simulations, quantitative structure activity relationship (QSAR) modeling and network pharmacology have facilitated systematic screening of W. somnifera bioactives against a multitude of protein targets implicated in breast cancer pathogenesis such as ERα, HER2, EGFR, phosphatidylinositol‑3‑kinase α (PI3Kα), topoisomerase IIα(TOP2A), 17β‑hydroxysteroid dehydrogenase‑1(17β-HSD1), tumor protein p73 as well as Bcl-2 family members and others. A recent work stands out in this context, where ashwagandhanolide and withanolide sulfoxide were found to be multitarget inhibitors of ERα, 17β‑HSD1, TOP2A and p73 in a PLOS ONE study performed by Lamminga et al., which used docking, MD simulation and ADMET prediction. An article in Drug Design, Development and Therapy (2017), performed docking, QSAR, and ADMET analyses on withanolide analogs for breast cancer targets and outlined structural features associated with activity. This review collates and summarizes the recent bioinformatics data for molecular docking and target-based drug discovery using bioactive compounds of W. somnifera, against various breast cancer targets. It outlines breast cancer molecular subtypes and therapeutic targets, W. somnifera phytochemistry and anticancer pharmacology, big docking, and modeling studies on ER, HER2/EGFR, PI3K/Akt, topoisomerases, apoptotic regulators such as caspase 8/9 (CASP8/CASP9), BCL-2-associated agonist of cell death (BAD), apoptosis-regulating protein PRAP4P and others. It provides a detailed analysis of the alignment between in silico predictions and experimental data, considers ADMET and drug-likeness factors, and emphasizes future strategies to bridge docking with network pharmacology-based approaches, multi-omics frameworks, as well as by utilizing cutting-edge drug-delivery systems to translate W. somnifera phytochemicals into clinically relevant breast cancer therapeutics.
Withania somnifera; Breast cancer; Molecular docking; Withanolides; Target-based drug design
Hutchinson-Gilford Progeria Syndrome (HGPS): AI Models, 3D Morphometric Fingerprinting, Etiology, Epidemiology, Next-Generation Molecular Therapeutics, Microcephalic Osteodysplastic Primordial Dwarfism-II (MOPD-II)
Hutchinson-Gilford Progeria Syndrome (HGPS) and Microcephalic Osteodysplastic Primordial Dwarfism Type II (MOPD-II) are ultra-rare genetic syndromes associated with severe growth abnormalities, multisystem involvement and distinct molecular mechanisms. While the mutations of HGPS cause the accumulation of progerin and accelerated aging, the mutations for MOPD-II affect the function of the centrosome and proliferation of the cells. This review is aimed at summarizing the etiology, epidemiology, molecular pathology, and clinical manifestations of both disorders, and discussing recent developments in the field of artificial intelligence (AI), genomic analysis, three-dimensional morphometric fingerprinting, digital phenotyping, and multi-omics integration to better diagnose and characterize the disease. It also contrasts the clinical and molecular characteristics of HGPS and MOPD-II and touches on new treatment concepts, such as RNA-based approaches, antisense oligonucleotides and precision medicine with the help of artificial intelligence. The issues of clinical translation, small patient numbers and standardisation of data are also discussed. Overall, the integration of AI with molecular genetics and precision medicine offers promising opportunities for earlier diagnosis, personalized treatment, and improved clinical outcomes in these ultra-rare genetic disorders.
Hutchinson-Gilford Progeria Syndrome (HGPS); Microcephalic Osteodysplastic Primordial Dwarfism Type II (MOPD-II); Artificial Intelligence; 3D Morphometric Fingerprinting; Precision Medicine; Multi-Omics; Gene Editing.
Advances in Herbal Nootropics for Dementia: Targeting Oxidative Stress, Neuroinflammation, and Cognitive Decline
Herbal nootropics are gaining attention for their multi-target strategy in dementia, which offers neuroprotection along with antioxidant and anti-inflammatory actions. The current therapies only provide symptomatic relief without halting or altering disease progression, which occurs due to a complex interplay of amyloid and tau pathology, oxidative stress, deficiency of neurotransmitters, neuroinflammation and vascular damage. The worldwide growing burden of dementia highlights the need for safer, affordable and mechanistically broader therapies. Herbal nootropics like Bacopa monnieri, Gingko biloba, Curcuma longa and many more acts on variety of targets such as acetylcholinesterase inhibition, modulation of signalling but primarily they enhance cognition by modulating neurotransmission, improve brain energy metabolism, support neuroplasticity and reduce oxidative and inflammatory stress. Preclinical research demonstrates improvements in learning and memory from herbal nootropics. However, the lack of standardization and large-scale trials restrict their clinical translation. Future work should focus on standardized extracts, interaction assessments and rational integration with conventional treatment for evidence-based roles of herbal nootropics in dementia.
Herbal nootropics; vitamin B12; vascular damage; neuroplasticity; Bacopa monnieri
Oxygen Homeostasis and Dysregulation: Alveolar Failure, Etiology, Pathophysiology, AI & ML Models, 3D Fingerprinting, Epidermology, Computational Frameworks in Hypoxemia, Hypoxia, and Oxygen Toxicity
Maintaining oxygen homeostasis is crucial to the proper functioning of the body, which depends on the integrated control of pulmonary ventilation, alveolar gas exchange, oxygen transport via circulation, and cellular-level metabolism. Any disturbance in this regard results in hypoxemia, hypoxia, alveolar dysfunction, and oxygen toxicity, among others, which lead to the development of several conditions. This review provides an overview of the physiological fundamentals of oxygen homeostasis, the causes and pathophysiology of oxygen imbalance, and its main clinical presentations like acute hypoxemic respiratory failure, chronic hypoxia, and oxygen toxicity. The latest breakthroughs in Artificial Intelligence (AI), Machine Learning (ML), computational physiology, digital twins, and three-dimensional (3D) physiological fingerprints for oxygen diseases are also discussed in this review. The emerging computational models show significant promise in helping detect respiratory degradation at an early stage, improving oxygen therapy, and enabling accurate clinical decision-making. Model validation, data standardization, explainability, and clinical translation have been key challenges discussed in the review. In general, combining physiological insight and intelligent computational techniques is a promising approach toward precision respiratory medicine.
Oxygen homeostasis; Hypoxemia; Hypoxia; Artificial intelligence; Digital twins; Precision respiratory medicine.
Design, Degradation Mechanisms, and Drug Release Kinetics of Biodegradable Polymers in Advanced Drug Delivery Systems: Applications, Challenges, and Future
Biodegradable polymers have stood out among other templated macromolecular materials as fundamental components of advanced drug delivery systems, providing controllable, sustained and site-specific release of therapeutics with lower toxic side effects. We present a complete review of the design concepts, degradation principles and drug release kinetics of natural and synthetic biodegradables like PLGA, PCL, chitosan, and alginate. The adaptability of naturally occurring substances allows them to be fabricated into nanoparticles, microspheres, hydrogels and scaffolds for different clinical applications such as cancer therapy, vaccine delivery, gene therapy and tissue engineering. This review covers hydrolytic and enzymatic degradation mechanisms, surface or bulk erosion behavior of polymers, and parameters affecting polymer degradation and drug release kinetics. These polymers have been used in FDA-approved formulations, which are shown in a few case studies to result in both improved therapeutic efficacy and patient compliance. It discusses challenges including inter- and intra-protein degradation rate variability, formulation stability issues, scale-up of manufacturing process difficulties, and regulatory hurdles. The continuing advances at the emerging frontiers of smart, stimuli-responsive systems, hybrid polymers, AI-assisted design, and personalized medicine suggest a bright future for biodegradable polymers in engineering precision and sustainable therapeutics.
biodegradable polymers, drug delivery, controlled release, polymer degradation, nanoparticles, PLGA, polymeric microspheres, stimuli-responsive polymers, personalized medicine, pharmaceutical formulation.
Nanotechnology-Enabled Phytopharmacology: Advanced Nanocarrier Strategies for Enhancing Bioavailability, Targeted Delivery, and Clinical Translation of Plant-Derived Therapeutics
Phytochemicals derived from medicinal plants have attracted considerable attention owing to their diverse pharmacological activities, including anticancer, anti-inflammatory, antimicrobial, antioxidant, and neuroprotective effects. Despite their promising therapeutic potential, the clinical application of many phytochemicals remains limited due to poor aqueous solubility, low bioavailability, rapid metabolism, chemical instability, and insufficient accumulation at target sites. Recent advances in nanotechnology have provided innovative strategies to overcome these challenges through the development of sophisticated nanocarrier systems capable of enhancing the delivery and therapeutic performance of plant-derived bioactive compounds. Various nanocarrier platforms, including lipid-based nanoparticles, polymeric nanoparticles, inorganic nanomaterials, and biomimetic delivery systems, have demonstrated significant potential in improving phytochemical solubility, stability, pharmacokinetics, and tissue-specific targeting. These nano systems protect bioactive molecules from premature degradation, facilitate controlled and sustained drug release, and enhance cellular uptake through passive and active targeting mechanisms. Nano formulations such as curcumin-loaded PLGA nanoparticles, quercetin-loaded lipid nanocarriers, and stimuli-responsive delivery systems have shown superior bioavailability, enhanced therapeutic efficacy, and improved biodistribution profiles in both preclinical and clinical investigations. In addition to improving therapeutic outcomes, multifunctional nanoplatforms enable the simultaneous delivery of multiple phytochemicals and diagnostic agents, paving the way for theragnostic applications and precision medicine approaches. Nevertheless, several challenges continue to hinder the widespread clinical translation of phytochemical nanomedicines, including formulation stability, large-scale manufacturing, batch-to-batch reproducibility, long-term safety assessment, immunogenicity concerns, and regulatory standardization. Future developments are expected to focus on smart and stimuli-responsive nanocarriers, artificial intelligence-assisted formulation design, personalized phytopharmaceutical strategies, and environmentally sustainable synthesis approaches. Overall, the convergence of nanotechnology and phytopharmacology represents a transformative advancement in natural product-based therapeutics, offering new opportunities to maximize the therapeutic potential of phytochemicals while improving safety, efficacy, and patient outcomes.
Phytochemicals, nanocarriers, nanotechnology, bioavailability, targeted delivery, polymeric nanoparticles, lipid-based carriers, natural product
Prussian Blue (Radiogardase) in Nuclear Medicine: New Approaches to Treating Internal Radioactive Toxicity-Mechanisms and Efficacy in Radionuclide Contamination
Internal radioactivity pollution is a major issue in nuclear medicine and environmental protection since the radionuclides cesium and thallium can accumulate in the body. This review aims to discuss the application of Prussian Blue (Radiogardase) as a useful decorporation agent, but with reference to animal models alone. The analysis of rodents and livestock shows that Prussian Blue dramatically improves fecal excretion of radionuclides, decreases biological half-life, and restricts tissue absorption using its ion-exchange process in the gastrointestinal tract. Pharmacokinetic studies indicate that there is little absorption into the systemic circulation and that early administration is crucial to the best effect and species differences affect the metabolism and excretion of radionuclides. Also, novel modalities like nanostructured formulations and combination therapies have the potential to enhance the treatment outcomes. The application of animal studies also emphasizes its significant use in the environmental and agricultural context to minimize the transfer of radionuclides to milk and meat, hence food safety. Toxicological assessments assure high safety, low side effects and positive therapeutic index. Non-etheless, drawbacks as variability in dosing regimens and lack of long-term animal studies suggest the necessity of additional studies. In general, Prussian Blue is a very effective and safe approach to dealing with internal radioactive toxicity and radionuclide contamination.
Prussian Blue, Radiogardase, Radionuclide Contamination, Ion-Exchange Mechanism, Animal Models, Pharmacokinetics, Decorporation Therapy, Radioactive Toxicity
Boundaries in flux: Climate Change's Impact on the Global Distribution of Vector-Borne Diseases(VBD), Histology, Pathogenesis, Modern Threats and Control
More and more, scientists are acknowledging that climate change is a major ecological factor that influences the spread and transmission of diseases carried by vectors in animals. The habitats of vectors, the length of the growing season, and the rate of disease growth in arthropod vectors are all being impacted by the changing climate. The effects of climate change on the disease systems of livestock, wildlife, and companion animals are summarized in this review, which draws on fifteen important studies. The results show that as temperatures rise, vectors have a better chance of survival and can spread to higher latitudes and higher elevations. Notable instances of redistribution caused by climate change include tick-borne diseases, ruminant bluetongue epidemics, and the extension of mosquito ranges. Estimating the future magnitude of diseases is fraught with uncertainty because to methodological heterogeneity and a lack of long-term data, even if models that forecast future weather conditions indicate that climate suitability zones will continue to expand. In order to protect animal health and ecological stability, it is crucial to implement climate-informed veterinary surveillance and enhance predictive modeling. The research suggests that climate change amplifies the transmission of illness in animal vector systems.
Climate change, Animal vector-borne diseases, Tick-borne infection, Bluetongue virus; Mosquito range expansion, Wildlife host–parasite interactions
Advances in Nano-Immunotherapy: Pharmaceutical Formulation Strategies for Enhanced Immune Targeting
Nano-immunotherapy leverages advanced nanocarrier systems to overcome limitations of conventional immunotherapies, providing precise immune modulation through targeted delivery of antigens, immunomodulators, and genetic material. Lipid-based, polymeric, inorganic, and hybrid nanocarriers enable controlled release, enhanced bioavailability, and site-specific immune activation, optimizing therapeutic efficacy while minimizing systemic toxicity. Pharmaceutical formulation strategies, including particle engineering, surface functionalization, and payload optimization, are critical to enhancing immune targeting and pharmacokinetics. Clinically, nano-immunotherapeutics have demonstrated remarkable success in vaccines, cancer immunotherapy, and genetic disease treatment, exemplified by mRNA-LNP COVID-19 vaccines and liposomal chemotherapies. Despite challenges in manufacturing, stability, and regulatory approval, emerging trends such as AI-driven design, personalized formulations, and integration with gene-editing technologies forecast a future of precision nano-immunotherapy with broad clinical impact.
Nano-immunotherapy, mRNA-LNP COVID-19 vaccines, Gene-editing technologies
Biologically Inspired Drug Delivery Systems: Pharmaceutical Insights Lessons from Nature
Bio-inspired drug delivery systems are a new frontier in the history of pharmaceutical innovation, in which the strategic knowledge of natural biological processes will inform the delivery of therapeutic agents with greater precision, safety, and efficacy. The goal of this study is to design and develop novel bio-inspired carriers that could be modeled after cell membrane mimicry, exosome-mediated delivery, and toxin-derived targeting peptides and assess their physicochemical properties, targeting efficiency, and biocompatibility in the appropriate animal models. The study also examines the in vivo pharmacokinetics, biodistribution profiles, and therapeutic performance of these bio-inspired systems and compares the results to the traditional drug delivery methods to ascertain the relative enhancement of these systems regarding their stability, site specific accumulation, and sustained release profiles. Extensive preclinical studies, such as: histopathology, biochemical, and molecular evaluations are done to assess the safety, immunogenicity and prolonged biological interactions. This program aims to determine the therapeutic advantage and clinical viability of bio-inspired drug delivery platforms by combining up principles of biomimetic design and strict translational examination designs, thus playing a part in the creation of the following-generation pharmaceutical systems with improved targeting capacity, reduced systemic toxicity, and improved clinical outcomes.
Bio-inspired drug delivery; Biomimetic nanocarriers; Exosome-based transport; Targeted drug delivery; Pharmacokinetics and biodistribution
AI-Enabled Nanopharmaceutical Design: Predictive Modelling for Next-Generation Therapeutic
Artificial intelligence (AI) is revolutionizing nanopharmaceutical research by enabling predictive modelling and data-driven optimization of nanoparticle drug delivery systems. Nanopharmaceuticals leverage diverse nanoscale carriers—including liposomes, polymeric nanoparticles, dendrimers, metallic nanoparticles, and micelles—to enhance targeted drug delivery, improve bioavailability, minimize toxicity, and enable site-specific therapeutic action. Traditional nanomedicine formulation faces challenges due to complex multidimensional variables and nonlinear biological interactions. AI-driven approaches, encompassing machine learning, deep learning, and reinforcement learning, provide powerful frameworks to analyze large datasets, identify critical formulation parameters, predict nanoparticle physicochemical and biological behavior, and optimize design workflows. These predictive models accelerate discovery by reducing trial-and-error experimentation, improving manufacturability, and enhancing clinical translation. AI also facilitates personalized nanomedicine by integrating molecular, omics, and clinical data to forecast patient-specific therapeutic responses. Addressing challenges related to data quality, interpretability, safety, and regulatory compliance, AI-enabled nanopharmaceutical design promises to transform drug development into a precision engineering discipline—delivering safer, more effective, and tailored therapies to patients.
Nano Pharmaceuticals, AI, Biological, Nanomedicine, Safety.
Biodegradable Polymers in Drug Delivery: Design, Degradation, and Drug Release Dynamics
Biodegradable polymers have emerged as essential components in advanced drug delivery systems, enabling controlled, sustained, and site-specific therapeutic release while minimizing systemic toxicity. This comprehensive review covers the design principles, degradation mechanisms, and drug release dynamics of natural and synthetic biodegradable polymers such as PLGA, PCL, chitosan, and alginate. Their adaptability allows fabrication into nanoparticles, microspheres, hydrogels, and scaffolds tailored to various clinical needs, including cancer therapy, vaccine delivery, gene therapy, and tissue engineering. The review discusses hydrolytic and enzymatic degradation processes, surface versus bulk erosion behaviors, and factors influencing polymer degradation and drug release kinetics. Case studies highlight FDA-approved formulations leveraging these polymers for enhanced therapeutic efficacy and patient compliance. Challenges such as variability in degradation rates, formulation stability, manufacturing scale-up, and regulatory hurdles are addressed. Emerging frontiers in smart stimuli-responsive systems, hybrid polymers, AI-driven design, and personalized medicine underscore the future potential of biodegradable polymers as cornerstones of precision and sustainable therapeutics.
Biodegradable Polymers, Drug Delivery, Controlled Release, Polymer Degradation, Nanoparticles, PLGA, Polymeric Microspheres, Stimuli-Responsive Polymers, Personalized Medicine, Pharmaceutical Formulation.
Bio-inspired Drug Delivery Systems: Lessons from Nature for Pharmaceutical Innovation
Bioinspired drug delivery systems represent a rapidly evolving frontier in pharmaceutical science, leveraging mechanisms observed in nature to enhance therapeutic efficacy and safety. By emulating biological processes such as cellular uptake, natural carrier trafficking, and stimuli-responsive release, these systems offer innovative solutions for targeted and controlled drug delivery. Significant advancements have been made in the design of biomimetic nanocarriers, including exosome-mimetic vesicles, polymeric nanoparticles, and liposomes, enabling precise delivery to specific tissues while minimizing off-target effects. Despite these promising developments, challenges remain, including biocompatibility, manufacturing scalability, and regulatory hurdles. Future perspectives suggest the integration of computational modeling, artificial intelligence, and hybrid natural-synthetic platforms to further optimize drug delivery and personalize therapeutic interventions. Overall, bioinspired approaches provide a compelling framework for advancing pharmaceutical innovation and translating nature’s efficiency into clinically viable therapies.
Bioinspired Drug Delivery, Biomimetic Systems, Nanocarriers, Targeted Therapy, Controlled Release, Pharmaceutical Innovation
AI in Pharmacovigilance: Predictive Analytics for Drug Safety and Post-Marketing Surveillance
Artificial intelligence (AI) is revolutionizing pharmacovigilance (PV) by enabling faster, more accurate detection and management of adverse drug reactions (ADRs). Advanced AI systems, including machine learning algorithms, large language models, and deep-signal detection tools, are being integrated into regulatory frameworks and industry workflows to enhance drug safety monitoring. Platforms such as WHO-UMC VigiLyze and the FDA Sentinel Initiative demonstrate the practical application of AI in improving signal detection, risk prediction, and proactive safety interventions. Despite its promise, AI adoption in PV presents challenges, including algorithmic bias, explainability, data privacy concerns, and integration with legacy systems. Future trends point toward autonomous AI-driven PV, federated learning for global safety analysis, hybrid human-AI decision-making, and predictive digital twins for individualized ADR risk assessment. Collectively, these innovations are poised to transform pharmacovigilance into a more intelligent, efficient, and proactive discipline.
Artificial Intelligence, Pharmacovigilance, Adverse Drug Reactions, Machine Learning, Signal Detection, Predictive Modeling, FDA Sentinel, WHO-UMC Vigilyze, Digital Twins, Drug Safety
CRISPR Base-Editing for Correcting Monogenic Mutations: Optimization and Off-Target Profiling
CRISPR base-editing has now emerged as a fast, accurate, and least disruptive technology to correct monogenic mutations in animal models, demonstrating direct A-G and C-T base conversions, without formation of two-stranded breaks. This review summarizes recent advances in optimization of base-editing systems, delivery, and optimization of off-target profiling of mice, zebrafish, rabbits, and livestock. The major results are reported on the development of better versions of the EsCs: BE4max, ABE8e, and high-fidelity Cas9 enzymes can be used to obtain better on-target specificity, whereas AAV vectors, LNPs, and mRNA-based delivery are important in enabling tissue-specific editing. Species-specific variability in off-target studies due to chromatin context and deaminase behavior raises the importance of standardized profiling pipelines. Although it has not been used in therapeutic contexts, despite considerable demonstrated capabilities in metabolic, ocular, and neuromuscular disease models, there are still challenges, including limitations in the use of PAM by bystanders, mosaicism, and long-term safety. All in all, this review provides basis on the possibility of further refinement of editors, safer methods of delivery, and ethical biosafety systems to develop CRISPR base-editing into effective preclinical and translational practices.
CRISPR Base Editing, Monogenic Mutations, Animal Models, Adenine Base Editors, Cytosine Base Editors, Off-Target Profiling, AAV Delivery, Genome Engineering
Copyright (c) 2025 Drug Discovery and Molecular Docking (DDMD)
From Docking to Bench: An Experimental Validation Pipeline for Top Docking Hits from Medicinal Plants
Computational biology methods, including virtual docking, which are a prediction of the interaction between phytochemicals and disease-relevant target proteins, can be used to speed up the discovery of bioactive compounds in medicinal plants. In silico predictions however need experimental validation to determine biological efficacy. This study provides a systematic pipeline, which combines molecular docking and in vivo validation to determine the interesting antioxidant and anti-inflammatory compounds. Phytochemicals of Curcuma longa, Withania somnifera, and Gymnema sylvestre were docked against oxidative stress-related and inflammatory target proteins, and the docking hits upon which the phytotoxins Curcumin, Withaferin A, and Gymnemic Acid ranked highest were investigated using Wistar rats. Findings indicated that oxidative stress markers (MDA) and pro-inflammatory cytokine (TNF- 6, IL- 6) were significantly reduced, and antioxidant enzyme activity (SOD) was elevated, as predicted by the affinities of docking. The results show that there is a high correlation between the computational predictions and experimental results, which confirms that the pipeline is a secure method to use plant-derived compounds to predict what they can be used in preclinical drug development. This combined approach provides a generalizable platform on which to connect in silico screening and in vivo validation to enable the identification of plant-based therapies to address oxidative stress and inflammation.
Molecular Docking, Medicinal Plants, Curcumin, Withaferin A, Gymnemic Acid, Antioxidant Activity, Anti-Inflammatory Activity
In Silico Screening and Molecular Docking of Phytochemicals Against SARS-Cov-2 Main Protease: Prioritizing Leads For In Vitro Testing
A particularly urgent need for safe and effective treatment medicines has been brought to light by the COVID-19 pandemic, which was caused by SARS-CoV-2. Because of their phytochemicals, medicinal plants have a long history of usage in traditional medicine and could serve as a suitable substitute due to their low toxicity and wide range of bioactivities. Using in silico screening and molecular docking, this study evaluates the inhibitory effect of 60 phytochemicals against SARS-CoV-2's major protease (Mpro), an enzyme necessary for virus replication. Potential candidates were identified by analyzing the chemicals' affinities, molecular interactions, and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties. Findings indicated that Quercetin, Curcumin and Epigallocatechin have potentially good binding affinities and good interaction patterns with Mpro and high gastrointestinal absorption and low predicted toxicity which suggested that they could be used in further animal-based in vitro studies. These phytochemicals are emphasized in the study as possible lead compounds in the development of new, plant-based antiviral agents against SARS-CoV-2 and the study shows the usefulness of computational methodology in speeding up early-stage drug discovery.
SARS-CoV-2, COVID-19, Phytochemicals, Molecular Docking, In Silico Screening, Main Protease (Mpro), ADMET Analysis
Single-Cell Transcriptomics to Map Cellular Heterogeneity in Tissue Regeneration Models
Single-cell transcriptomics has also revolutionized regenerative biology by offering a resolution never before achieved into both the cellular and molecular pathways mediating tissue repair in different animal models. Through profiling of thousands of individual cells throughout the regenerative process, this technology has revealed widespread cell heterogeneity, lineage-biased progenitor subclusters, active dedifferentiation processes, and pivotal immune interactions between niche that have been lost by bulk estimates. Zebrafish, axolotl, planarian, and mammalian tissue studies demonstrating conservation and species-specificity of stem-cell plasticity, immune modulation and main signaling pathways, Wnt, Fgf, Notch and Tgf- beta, provide insights into conserved and species-specific mechanisms of these developmental changes. Recent methodological progress such as optimised dissociation-based assays, single-nucleus RNA sequencing and the ability to compute lineage relations and find uncommon regenerative cell states have facilitated the reconstitution of lineages and the recovery of rare regenerative cell states. In spite of issues associated with dissociation artifacts, the loss of spatial context, and the variation between studies, the single-cell technologies still transform the knowledge of the regeneration and promise a lucrative translational outlook on generating regenerative therapies that are more specific. This is a review that compiles the recent evidence, analyzes the methodology strengths and weaknesses, and reports the future prospects in bringing together multi-omics, spatial mapping, lineage tracing and cross-species comparisons to complement the mechanistic aspect and hastens the applications of regenerative medicine.
Single-Cell RNA Sequencing, Tissue Regeneration, Stem-Cell Plasticity, Immune Regulation, Lineage Trajectories, Spatial Transcriptomics, Multi-Omics, Regenerative Medicine.
Structure-Based Docking to Identify Anti-Inflammatory Phytochemicals Binding to Cox-2 and 5-Lox
The process of inflammation is a complicated physiological reaction to noxious stimuli and is the work of cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX), and is thought to be the cause of chronic inflammatory diseases, including arthritis, asthma, and inflammatory bowel disease. Phytochemicals such as flavonoids, terpenoids, alkaloids have been proposed to be a promising multi-target anti-inflammatory agent, which is safer than traditional NSAIDs because it is associated with gastrointestinal, renal, and cardiovascular side effects. The preclinical animal studies prove that such compounds are effective in reducing paw edema, leukocyte infiltration, and pro-inflammatory cytokines. The complementary structure-based molecular docking experiments identify that there is great hydrogen bonding, hydrophobic and high binding affinities with the important catalytic residues of COX-2 and 5-LOX, which support their dual-inhibitory capacity. Also, phytochemicals of various classes could be used with the help of combinatorics to obtain additive or synergistic effects, to increase efficacy with minimal doses and toxicity. The lack of bioavailability, species-specific differences, and long-term safety data are some challenges that require optimization of formulations and translation studies. The combination of in silico docking with in vivo preclinical models is, in general, a powerful platform of prioritization of phytochemical candidates and their progression to safe and effective multi-targeted anti-inflammatory drugs.
Phytochemicals, Anti-inflammatory, COX-2, 5-LOX, Flavonoids, Terpenoids, Structure-based Docking, Preclinical Studies
Use of pesticides has many harmful sites those may affect the nature in a great way. The chemicals those are used in the pesticides may affect the human body in different times. It may be the causes of skin cancer, tumors, changes in genetics, disorders of blood and nerves and different issue those may affect the livelihood of human nature. To save plants from insects and to enhance the productivity of plants, chemical and pesticides are used. This is not accounted by the body of plants and sometime it is accumulated through the “food chain system” that is effectively being supplied to the human body. Among all the chemicals, some of them have tremendous effect that can push human being a great danger.
Pesticides, chemical, diseases, human body, harmful, cancer, plants, food chain. Insects, effects.
Exploring the differences and similarities between ionic and covalent bonds
The study seeks to explain the similarities between the ionic and covalent bonds. The study aims to explain the advantages and disadvantages of both ionic and covalent bonds. Secondary sources of data such as scholarly articles, online brochures are used as references.
Ions, Covalent, Polar compounds, Electrons
Dangers of Chiral Class Drugs: Ibuprofen
The study conducted explores the dangers posed by Ibuprofen, a well-known chiral drug used for its anti-inflammatory properties. The study focuses on how Ibuprofen, despite all its advantages, can pose a significant threat to human health. The research is mainly based on secondary data. Sources such as scholarly articles by medical practitioners, online news journals, articles, annual reports regarding drugs and online brochures are used for reference to complete this research work.
Drugs, Anti-inflammation, Chiral, Liver, Ibuprofen
A Critical Evaluation of The Difference Between Organic and Inorganic Compounds
In the Science and Technology considered inorganic and organic elements in chemistry that affirm the existence and the concept of this study. Organic and Inorganic compounds explained are created and separated in the plan that is proposed appropriate grade. Including human and animal organisms, they mostly create organic and inorganic substances. The organic compounds are made with specific protein chains, lipid structure, carbohydrates and hormones. On other hands, the inorganic compounds are creating specific molecules, such as water and essential minerals. Normally, organic compounds are textures that involve covalent carbon bonds, hydrogen bonds, and oxygen and nitrogen bonds. Carbon atoms are mainly stranded on a particular structure, which creates various diversity of organic element. Everything on Earth is explained as a breathing creature that depends on organic materials. Again, inorganic compounds are made with two or more molecules than carbon compounds
Alkanes, Alkenes, Alkyl, Alkynes, Allotropes, Amorphous, Covalent Bonds, Metals, Metal Hydrides And Metal Sulphides.
Evaluation On Substance Abuse & Chemical Changes in Human Body
The development of clinical diagnosis has depended on the amount of substance has taken in the Human Body. In recent years, more issues in the working places have allowed medical researchers to develop strategies to meet patient’expectations, improve planning to protect their mental health, and provide specific guidelines for using advanced drugs to reduce the stress level of the patients. This research aims to evaluate how chemical are impacting the human bodies in various ways. The study enhances the understanding of the importance of prescription drug usages that helps to uplift the mood of the patients. In this context, the role of chemical drugs among the patients for the betterment of their physical as well as mental health has been identified in this study. Furthermore, it has been found that the several patient’s from all over the world taking prescription based substance to avoid painful psychological syndrome.
Usage of drugs, E-pharmacy, chemical changes, neurobiology, different dimension of substance, blockchain system.
Structure-Based Drug Discovery Using Molecular Dynamics and Docking Techniques
Structure-based drug discovery (SBDD) has emerged as an important means of identifying novel therapeutic candidates using the 3-dimensional structure of target proteins. In this work, molecular docking and molecular dynamics (MD) simulations were applied to 1000 compounds against a target protein aiming to identify potential inhibitors with strong binding affinity and stable binding interactions with the target protein. The top 10 ranked compounds from the docking analysis were further evaluated by applying 100 ns MD simulations to investigate the dynamic stability of the selected compounds according to standard analysis protocols using root mean square deviation (RMSD) and calculation of stable hydrogen bonding interactions. Compound C-102 was identified as the most promising candidate with the lowest RMSD and the greatest average number of hydrogen bonds confirming better stability of the complex with the target protein. Statistical analyses confirmed persistence of significant differences in the stability of the compounds from the selected candidates. Overall study findings support concerns determined using the docking and MD simulation approaches used in this work can fit into the drug discovery process in the early stages and signify a robust, reproducible approach to lead identification and maturation.
Structure-Based Drug Discovery, Molecular Docking, Molecular Dynamics Simulation, Binding Affinity, Protein-Ligand Interaction, Drug Design
Computational Drug Discovery: Integrating AI and Molecular Docking for Lead Optimization
The landscape of pharmaceutical research and development has undergone a paradigm shift from the application of artificial intelligence (AI), molecular docking and the Internet of Things (IoT) to the drug discovery process, creating computational fast pathways. This new computational path way creates the opportunity to overcome the problems associated with the traditional drug development process in terms of cost, timing and low probability of success by gaining speed in lead identification, optimization and validation. AI engines like deep learning and generative algorithms uniquely and rapidly analyze large human curated pharmaceutical datasets that evaluate ADMET properties as well as identify new drug-like small molecules. Molecular docking is synergistically aligned with AI because it can simulate drug-target interactions and allows researchers to rank candidate compounds based on binding affinities to a target of interest. Additionally, IoT acts in concert with AI and molecular docking to collect real-time monitoring data to capture accurate experimental baseline data in a preclinical setting, furthering the ability to improve accuracy, reduce reproducibility errors and raise ethical standards to a higher level using intelligent laboratory automation and continuous physiological monitoring of animal models. All three approaches are viable disruptive technologies for translation drug development in a substantially faster manner and it will strengthen data driven decision making; generate much less redundancy, and accelerate regulatory transparency. While challenges remain related to data quality and infrastructure challenges associated with AI, IoT and molecular docking presents early signaling therefore there is a presiding change in the direction of drug development towards greater speed and precision at a significantly lower cost.
Rational drug design, Medicinal chemistry, CNS drug development, Blood–brain barrier, Fragment-based drug design, Prodrug strategies