Biopress Journal of Computational Life Sciences (BJCLS)
Biopress Journal of computational Life Sciences is a cutting-edge, peer-reviewed international journal focused on integrating Artificial Intelligence into the world of Life Sciences. We aim to accelerate discoveries and innovations by bridging the gap between AI technologies and biological research. Our mission is to promote innovative methodologies and computational frameworks that are revolutionizing biological research and healthcare.
AI-Driven Prompt Engineering in Drug Discovery: The SwaLife AI Discovery Suite as an Intelligent Ideation Platform
Drug discovery remains constrained by high costs, extended timelines, and inefficient ideation processes despite advances in computational biology. The early stages of drug discovery-hypothesis formulation and experimental design-remain largely manual, subjective, and time-consuming. The SwaLife AI Discovery Suite represents a paradigm shift by integrating artificial intelligence into the conceptual design phase of drug discovery through automated, structured prompt generation. Rather than performing simulations or analyses, this platform generates context-specific scientific prompts that guide researchers through target identification, lead discovery, optimization, and preclinical planning. By bridging the gap between computational insights and experimental execution, the suite enhances reproducibility, standardizes workflows, and accelerates scientific ideation across pharmaceutical and academic research environments. This review examines the architecture, applications, advantages, and future scope of AI-powered prompt engineering in drug discovery, positioning it as a transformative tool for the next generation of therapeutic development.
Advances in Biomedical Literature Mining: A Comparative Review of Tools and the Emergence of the Swalife AI-Powered Platform
The biomedical literature continues to expand at an unprecedented rate, creating significant challenges for researchers to efficiently extract actionable knowledge. Traditional literature mining tools often focus on narrow domains or lack integration of natural product research, limiting their utility in drug discovery. Artificial intelligence (AI) and natural language processing (NLP) have revolutionized biomedical text mining, enabling higher accuracy and broader scope. This review critically evaluates major biomedical literature mining platforms, highlighting their strengths and limitations, especially concerning herbal therapeutics and protein target analysis. We introduce the Swalife AI-powered tool, which uniquely integrates herb–disease–protein mining with AIdriven filtering and dynamic network visualization. Swalife addresses critical gaps by delivering a comprehensive, exportable platform tailored to natural product research, expediting drug discovery and systems pharmacology investigations.
Role of Quantum Chemistry in Computational Science
Since its inception in the early 1900s, quantum chemistry has been a field of study that uses computational mechanics to better understand how atoms and molecules behave. It has aided scientists in comprehending important chemical processes like molecular structure, chemical bonding, and chemical reactions. The Schrodinger equation, which lies at the heart of quantum chemistry, allows scientists to forecast the behaviour of particles and the characteristics of molecular systems. Chemical systems may now be studied more precisely than ever before because to the development of computational approaches like density functional theory (DFT) and other calculation methods. These techniques aid in the design of novel materials and catalysts for use in science and industry, as well as the prediction of molecular characteristics. Furthermore, the simulation of intricate chemical systems and the accuracy of predictions have been increased by the integration of quantum chemistry with computer technologies and new developments in quantum computing. Algorithms and computer technology will make quantum chemical computations more measurable and effective, even though they can still be continuously improved in principle. All things considered, quantum chemistry serves as a link between physics, chemistry, and computer science and is crucial to cutting-edge contemporary research and technological advancements. The purposes and uses of quantum chemistry, as well as the significance of computational science in pharmacy and drug development, are the main topics of this paper.
Thinking Molecules, Learning Algorithms: How Computational Intelligence Is Inventing the Next Generation of Drugs
Drug discovery is undergoing a fundamental intellectual shift, moving away from intuition-dominated laboratory exploration toward algorithm-driven reasoning capable of learning from biological complexity at scale. Advances in computational science and artificial intelligence have transformed pharmaceutical research into a predictive discipline, where hypotheses are generated, tested, and refined within digital environments before entering the experimental pipeline. By integrating bioinformatics, network pharmacology, molecular docking, and simulation based modeling, modern drug discovery now interprets diseases as interconnected systems rather than isolated targets, enabling the rational design of multi-target and mechanism-informed therapeutics. Artificial intelligence extends far beyond automation by uncovering hidden patterns within high-dimensional chemical and biological data, optimizing molecular structures, anticipating pharmacokinetic behavior, and identifying toxicity risks at the earliest stages of development. These data-centric strategies significantly compress discovery timelines, reduce attrition rates, and enhance decision confidence across preclinical phases. Moreover, the convergence of machine learning with computational chemistry and systems biology supports scalable exploration of chemical space while maintaining translational relevance. As pharmaceutical innovation increasingly aligns with precision medicine and AI-driven healthcare, computational intelligence emerges not merely as a supporting tool but as a conceptual framework redefining how drugs are conceived, evaluated, and optimized. This review critically examines the evolution, capabilities, and future trajectory of computational science and artificial intelligence in drug discovery, highlighting their role in reshaping pharmaceutical innovation toward efficiency, reproducibility, and clinically meaningful outcomes.
The Role of Clinical Trials with AI in the Herbal Industry & Modern Healthcare
Herbal medicine, deeply rooted in traditional healing system such as ayurveda, siddha, and Unani is experiencing a transformative shift towards scientific validation through clinical trials. This review examines the evolving role of clinical trials in herbal industry, emphasizing India’s rich heritage within in a global context. Its highlight key challenges including phytochemical complexity, variability of raw materials, placebo design, regulatory fragmentation, and funding limitation. The integration of advance technologies like artificial intelligence and blockchain is explored as a mean to enhance trial design, standardization, and traceability. Clinical trials are essential for establishing safety, efficacy, regulatory compliance, and market credibility, thereby bridging traditional knowledge with modern evidence-based healthcare. Future direction focuses on harmonized international standard personalized herbal medicine, innovative delivery system, and culturally sensitive trial models to ensure the global acceptance and scientific. The evolution of position clinical trials as the cornerstone for transforming traditional herbal wisdom into validated, globally integrated healthcare solutions. AI serves as bridge, converting traditional knowledge into data driven procedure, even as traditional trials frequently struggle with the multicomponent, multi-target, character of herbs.
An Imaging Perspective on AI's Function in the Lung Cancer Diagnosis and Detection
Due in large part to late diagnosis, and the primary cause of cancer-related death globally is still lung cancer, primarily as a result of late detection and ineffective treatment at later stages. When it comes to lung cancer screening, detection, diagnosis, and staging, medical imaging is essential; yet, Growing data volumes, inter-observer heterogeneity, and the modest presence of early-stage disease pose challenges to conventional picture interpretation. Artificial intelligence (AI), which makes automated, quantitative, and repeatable analysis possible, has become a potent instrument in recent years to improve imaging-based lung cancer assessment. The review provides an in-depth overview of the role of artificial intelligence in the diagnosis and detection of lung cancer through the imaging perspective. We discuss the way machine learning and deep learning methods are being applied to the primary imaging modalities, including computed tomography, low-dose CT, positron emission tomography-computed tomography, and chest radiography. We consider such significant AI-based application like TNM staging, segmentation, benign-malignant classification, pulmonary nodule recognition, and tumour characterisation. Radiomics and multimodal AI make personalised decision-making and non-invasive tumour characterisation possible. Also, the clinical effects of AI on the optimization of screening, workflow, diagnostic accuracy, and early detection are summarized in this paper. New solutions such as explainable AI and multimodal data integration are evaluated with seriousness and the limitations that exist today in terms of data heterogeneity, model generalizability, interpretability, and regulatory issues are also discussed. On balance, AI can dramatically transform the frame of imaging of lung cancer by helping to diagnose lung cancer earlier and make more precise clinical decisions. In order to realize the full clinical advantages of AI-based imaging in lung cancer treatment, clear models need to be developed, continuous validation is required, and the seamless integration into clinical pathways is needed.
From Molecules to Medicine: AI-Driven Translational Science
Fundamental laboratory discoveries and their application in clinical practice is connected by translational science. The growing synergy between translational science, AI, computational tools, and medicinal chemistry has brought a paradigm shift in drug discovery and development. Medicinal chemistry forms the molecular basis for the rational design and optimization of drug candidates, while computational strategies and AI-based tools enhance target identification, lead refinement, and the prediction of pharmacokinetic and safety characteristics. This review examines the contribution of AI-driven computational medicinal chemistry to translational science, highlighting its role in shortening development timelines, increasing success rates, and supporting personalized and precision medicine. Recent innovations, existing challenges, and future prospects for integrating AI with experimental and clinical workflows are also discussed, emphasizing their relevance to next-generation healthcare.
Artificial Intelligence in Drug Discovery: From Computational Target Identification to Clinical Translation
Artificial intelligence (AI) has become an integral component of modern drug discovery, emerging as a structured and data-driven approach to address long-standing challenges such as rising development costs, extended timelines, and high failure rates. Traditional drug development processes remain inefficient, with many candidates failing in late-stage clinical trials due to inadequate efficacy, safety concerns, or unfavourable pharmacokinetics. Recent progress in computational capabilities, coupled with the rapid growth of large-scale biomedical and clinical datasets, has enabled broader and more reliable application of AI methodologies, including machine learning (ML), deep learning (DL), and natural language processing (NLP), across the drug discovery pipeline. These approaches support the integration and analysis of complex, high-dimensional data generated from genomics, proteomics, transcriptomics, chemical libraries, and real-world clinical sources. This review outlines the application of artificial intelligence (AI) across the drug discovery pipeline, from target identification and lead optimisation to preclinical evaluation and clinical translation, with emphasis on virtual screening, de novo design, and pharmacokinetic and toxicity prediction.
Role of Artificial Intelligence in Lc-Ms/Ms Metabolomics
Artificial intelligence is transforming LC‑MS/MS metabolomics by automating data handling and improving metabolite identification, leading to higher sensitivity, reproducibility, and clinical relevance. Machine learning and deep learning enhance baseline correction, noise reduction, peak detection, deconvolution, and retention‑time alignment, reducing manual curation. Tools such as PeakBot, MS2DeepScore, MIST‑CF, SingleFrag, and CFM‑ID strengthen spectral matching, fragmentation prediction, and de novo annotation, expanding detectable metabolites. AI‑driven feature selection and classification support robust biomarker discovery, disease diagnosis, and multi‑omics integration for systems‑level pathway insights and precision medicine. Remaining challenges include limited annotated data, inter‑laboratory variability, model opacity, computational cost, and lack of standardized benchmarks. Future priorities are unified, explainable end‑to‑end AI workflows, shared LC‑MS/MS repositories with community benchmarks, and accessible cloud platforms to enable routine AI‑driven metabolomics in research and clinical practice.
Role of Artificial Intelligence in Green Analytical Technique
Green analytical chemistry (GAC) has been accelerated by the growing necessity to mitigate environmental degradation and other health hazards posed by conventional analytical processes. The discipline is aimed at minimizing chemical waste and energy consumption and reliance on dangerous substances. The present review explains the usage of Artificial Intelligence (AI) in collaboration with GAC to enhance its sustainability, precision, and efficiency in its analytical operations. The article describes the twelve postulates of GAC, dwells upon the developments in the green chromatography, green spectroscopy, and green extraction techniques, new approaches to gauging of greenness, including GAPI, and the Analytical Eco-Scale. The AI innovation is more concentrated in such aspects as optimization of methods, automation, minimization of waste, and environmental observation. In green analytical chemistry, the application of AI presents a new direction of sustainable operations despite the drawbacks such as high costs, regulatory issues, and data availability. A combination of green principles and digital intelligence is not only enhancing the ability to make analyses but also uniting the chemical sciences in the process of building a greener, safer, and smarter world.
Copyright (c) 2026 Biopress Journal of Computational Life Sciences (BJCLS)
Artificial Intelligence in Modern Diagnostic Systems: From Explainable Models to Clinical and Industrial Applications
Artificial Intelligence (AI) has emerged as the central engine of next-generation diagnostic systems, reshaping medical imaging, precision agriculture, and intelligent industrial maintenance. This review presents a unified technical perspective on computer-aided diagnosis (CAD) empowered by deep learning and Explainable AI (XAI). We synthesize methodologies ranging from convolutional neural networks (CNN), transfer learning, hybrid multimodal fusion, to generative language assistants. Mathematical foundations of learning, optimization, and attribution are detailed alongside pseudocode for reproducible pipelines. Comparative tables analyze algorithms across accuracy, interpretability, computational cost, and regulatory readiness.
Artificial Intelligence and Quantum Dots: Design, Optimization, and Applications
Artificial Intelligence (AI) has revolutionised the production of Quantum Dots (QDs), providing solutions to problems such as variability in the materials used to produce QDs, toxicity from elements such as Lead and Cadmium, and the short lifetime of QDs. AI and Machine Learning (ML) provide the opportunity to improve the reverse engineering of QDs and batch production of QDs, which has decreased the number of experimental trials needed to develop QDs, especially for mass production, by more than 90%. The types of algorithms used in this process include Random Forest, Support Vector Regression (SVR), Graph Neural Networks, and Generative Adversarial Networks (GANs). These algorithms aid in predicting attributes such as Bandgap and Quantum Yield and developing alternative non-toxic materials used to create QDs. Furthermore, AI can be integrated into automated synthesis platforms, such as Microfluidics, to allow for QDs to be produced continuously in real-time with uniform properties throughout the production cycle. AI also uses computer vision techniques for the measurement of QD size and defects to reduce production errors and increase manufacturing throughput. In the area of biomedicine, AI provides the potential to create QDs that facilitate drug delivery and imaging, thus providing safer, less toxic alternatives. Future advancements in QD technology may include concepts such as digital twins, reinforcement learning, and explainable AI.
Transcriptomic Signatures of Chemoprevention: RNA-seq Insights from Carcinogen-Induced Animal Models
Cancer chemoprevention represents a critical strategy for reducing cancer incidence through the prevention, delay, or suppression of tumor development using bioactive agents. High-throughput RNA sequencing (RNA-seq) technology has revolutionized our ability to characterize transcriptomic signatures associated with carcinogen-induced transformation and chemoprevention in animal models. This review synthesizes current knowledge on how RNA-seq-derived transcriptomic profiling from carcinogen-induced animal models reveals molecular mechanisms of chemoprevention, identifies predictive biomarkers, and enables the discovery of novel therapeutic targets. We examine established animal models including the DMBA/TPA mouse skin carcinogenesis model, 4NQO-induced oral carcinogenesis, and benzo(a)pyrene exposure systems, alongside state-of-the-art RNA-seq methodologies for differential gene expression analysis, pathway enrichment, and functional annotation. Particular emphasis is placed on phytochemical chemopreventive agents including sulforaphane, curcumin, and ursolic acid, their mechanisms of action at the transcriptomic level, and the identification of key molecular pathways including NRF2-ARE antioxidant signaling, NF-κB inflammatory pathways, MAPK/ERK cascades, and apoptotic regulatory mechanisms. We discuss advanced computational approaches including weighted gene co-expression network analysis (WGCNA), machine learning classifiers, and single-cell RNA-seq for unraveling tumor microenvironment dynamics. Finally, we address challenges in translating transcriptomic discoveries from animal models to clinical biomarker development, quality control considerations, and future perspectives for precision cancer prevention.
RNA-seq–Based Mechanistic Evaluation of Phytochemicals and Small Molecules in Experimental Cancer Models
High-throughput RNA sequencing (RNA-seq) has emerged as a powerful tool for deciphering the molecular mechanisms underlying phytochemical and small molecule-mediated cancer suppression. This comprehensive review synthesizes current knowledge on integrating RNA-seq-derived transcriptomic analysis with mechanistic evaluation of phytochemical and synthetic compound effects on apoptosis, inflammation, oxidative stress, and epithelial-mesenchymal transition (EMT) in experimental cancer models. We examine how transcriptomic profiling links gene expression changes to known biochemical and cellular mechanisms of action, with particular emphasis on major phytochemicals including curcumin, resveratrol, and sulforaphane. Integration of pathway enrichment analyses using KEGG (Kyoto Encyclopedia of Genes and Genomes) and Gene Ontology (GO) databases enables systematic identification of affected molecular pathways and biological processes. We discuss methodological approaches for connecting RNA-seq signatures to functional phenotypes, validation strategies for mechanistic hypotheses, and challenges in translating transcriptomic findings to therapeutic development. The review emphasizes how multi-level transcriptomic analysis encompassing individual gene expression, pathway enrichment, gene co-expression networks, and temporal dynamics provides comprehensive mechanistic insights superior to single-methodology approaches. Finally, we address future directions including integration of multi-omics data, spatial transcriptomics for tumor microenvironment characterization, and machine learning approaches for signature discovery and mechanism prediction.
Dose-Dependent Transcriptomic Responses to Chemopreventive Agents in Rodent Cancer Models
The efficacy of cancer chemopreventive agents depends critically on dose, yet the mechanistic relationship between concentration and transcriptomic response remains incompletely characterized. This comprehensive review synthesizes current knowledge on dose-dependent transcriptomic responses to chemopreventive agents in rodent cancer models, examining how RNA-seq-derived gene expression patterns change across concentration ranges. We examine biphasic and hormetic dose-response curves, the concept of transcriptomic points of departure (tPOD), and benchmark dose (BMD) modeling approaches for identifying threshold doses. Particular emphasis is placed on phytochemical agents including sulforaphane, resveratrol, and garlic-derived organosulfur compounds, as well as synthetic chemopreventive agents evaluated in carcinogen-induced models including DMBA/TPA-initiated skin carcinogenesis, 4NQO-induced oral carcinogenesis, and lung adenocarcinoma models. The review discusses how dose-dependent transcriptomic analysis reveals threshold effects for pathway activation, identifies optimal dose ranges for chemoprevention efficacy, and establishes molecular mechanisms underlying dose escalation and dose-limiting toxicity. Critical considerations including sample size requirements, dose spacing strategies, statistical modeling approaches, and mechanistic interpretation of nonlinear dose-response relationships are addressed. Finally, we discuss applications of dose-response transcriptomics to personalized chemoprevention strategies and regulatory safety assessment based on transcriptomic points of departure.
Comparative RNA-seq Analysis of Single Compounds versus Polyherbal Formulations in Cancer Prevention
Cancer prevention through dietary and botanical interventions relies increasingly on multicomponent polyherbal formulations rather than individual phytochemicals, yet mechanistic comparisons remain limited. This comprehensive review synthesizes current knowledge on transcriptomic analysis comparing single compounds versus polyherbal formulations in cancer prevention models, emphasizing synergistic versus antagonistic gene regulation at the network level. We examine how RNA-seq reveals coordinated transcriptional changes across broader pathways in polyherbal preparations compared to single compound treatments, identify molecular mechanisms underlying synergy and antagonism, and discuss network pharmacology approaches for rationalizing herb combinations. Particular emphasis is placed on traditional polyherbal formulations including Ayurvedic preparations (Triphala, Kanchnar guggulu), Chinese herbal medicines, and novel phytochemical combinations demonstrating enhanced efficacy. The review discusses Bliss independence and Loewe additivity models for quantifying synergy at the transcriptomic level, cellular and molecular context dependence of synergistic responses, and how single compounds often demonstrate antagonistic interactions with conventional chemotherapy while polyherbal formulations bypass such antagonism through alternative pathway engagement. Critical challenges in standardization, regulatory approval, and post-marketing surveillance of polyherbal formulations are addressed, alongside future perspectives for rational polypharmacy design and personalized combination approaches based on individual transcriptomic profiles.
From Bench to Biomarkers: RNA-seq-Driven Discovery of Chemoprevention Biomarkers in Animal Models
The successful clinical development of cancer chemopreventive agents critically depends on mechanistic understanding and accompanying pharmacodynamic (PD) biomarkers providing early readouts of biological activity in target tissues and circulation. RNA sequencing (RNA-seq) has revolutionized biomarker discovery in preclinical cancer chemoprevention models, enabling unbiased transcriptomic analysis identifying mechanism-informed molecular signatures substantially more sensitive than traditional phenotypic endpoints. This comprehensive review synthesizes knowledge on RNA-seq-driven biomarker discovery in animal models for cancer prevention, emphasizing translational validation strategies bridging bench to clinic. We examine cross-species conservation of transcriptomic signatures derived from rodent chemoprevention models to human disease, discuss practical validation methodologies including quantitative PCR (qPCR), immunohistochemistry (IHC), and emerging liquid biopsy approaches, and address critical challenges in establishing clinical relevance of preclinically discovered biomarkers. Specific emphasis is placed on tissue versus circulating biomarkers, temporal dynamics of biomarker changes during prevention interventions, context-dependent biomarker effectiveness, and integration of tumor microenvironment factors influencing biomarker interpretation. Case studies demonstrate successful biomarker translation including withaferin A-induced p21 expression, oral squamous cell carcinoma transcriptional signatures predicting metastatic risk, and circulating nucleic acid biomarkers for non-invasive cancer monitoring. The review discusses FDA biomarker qualification processes, window-of-opportunity clinical trial designs leveraging preclinical insights, and emerging machine learning approaches for biomarker interpretation and patient stratification in prevention trials.
Formulation And Evaluation of Limonene-Loaded Solid Lipid Nanoparticles against Candida albicans
Limonene-loaded solid lipid nanoparticles (SLNs) represent a promising approach in combating Candida albicans, a pathogenic yeast responsible for a wide range of infections. Limonene, a natural monoterpene found in citrus oils, exhibits potent antifungal activity, particularly against Candida albicans, a major cause of fungal infections in immunocompromised individuals. Preformulation studies confirmed the identity, purity, and compatibility of the drug with selected excipients. Based on solubility screening, Glyceryl monostearate (GMS) and oleic acid were selected as lipid phases, while Tween 20 and Span 60 served as surfactants. The SLNs were prepared using the high-pressure homogenization technique and optimized via a 2² factorial design using Design Expert® software. In vitro release of limonene from gel was studied using a Franz diffusion cell method, and the recovered samples were analyzed using UV spectroscopy at 280 nm wavelength. The optimized formulation showed favorable particle size, zeta potential, and high entrapment efficiency, indicating good stability and nano-size range. Antifungal activity studies comparing placebo SLNs and limonene-loaded SLNs revealed that the latter exhibited good antifungal activity and efficient diffusion out of the lipid matrix. Limonene-loaded SLNs demonstrated strong potential as an antifungal formulation against Candida albicans. However, regulatory and reproducibility concerns remain due to the complexity of the formulation and lack of standardized testing methods. Addressing these challenges requires careful optimization of formulation components, manufacturing processes, and quality control protocols.
Limonene-loaded solid lipid nanoparticles (SLNs)
In silico identification and evaluation of bioactive plant product constituents of Glycyrrhiza inflata using structure-based drug design and ADMET analysis on HSV- glycoprotein D
Introduction Viral infections remain a persistent global health burden, with limited therapeutic options often leading to drug resistance. Natural remedies, particularly phytochemicals, are emerging as valuable alternatives in antiviral research. Glycyrrhiza inflata, a traditional medicinal plant, contains bioactive compounds such as glycyrrhizin, Liquiritin which has been reported to exhibit antiviral effects. Herpes simplex virus utilizes glycoprotein D (gD) for host cell entry, making this protein a key molecular target for drug discovery. Methods Five phytoconstituents from Glycyrrhiza inflata were analysed using computational drug design techniques. Molecular docking was performed against HSV glycoprotein D (PDB ID: 5CVM) through AutoDock Vina, for ligand preparation where 2D structure design and energy minimization molecular visualization using Discovery studio BIOVIA to assess binding interactions. Additionally, ADMET analysis and toxicity prediction was conducted to evaluate pharmacokinetic suitability, including ADMET, and toxicity profiles as well as bioactivity evaluation using molinspiration. Results Docking simulations demonstrated that all selected plant constituents were able to interact with glycoprotein D, with Liquiritin (M5) showing the most favorable binding affinity and stable interactions. The ADMET evaluation further indicated good drug-likeness, acceptable oral bioavailability, and low predicted toxicity, highlighting as a promising therapeutic candidate.
Ensuring Drug Safety beyond Trials: “The Promise of Real-World Evidence.”
Introduction Drug safety is an important patient; clinician and regulatory agencies concern which is closely associated with ADR and drug interaction. Pharmacovigilance plays a critical role in ensuring medication safety and efficacy through the detection, assessment, understanding, and prevention of adverse effects and other drug-related problems. This is achieved with the help of RWE which tells us what actually happen when drug is used by uncontrolled population However, pharmacovigilance continues to face challenges such as underreporting of ADRs, heterogeneity of data sources, and the increasing demands associated with accelerated drug approvals. To address these issues, real-world evidence (RWE) has emerged as a valuable contributor, offering deeper insights into drug safety and efficacy. Analysing this data helps us to creating real world evidence which helps doctor, companies and regulators to know about safety and efficacy of drugs. Methods RWE strengthens pharmacovigilance practices by utilizing data from multiple real-life sources including electronic health records, patient registries, and social media platforms. Through big data analytics and artificial intelligence, these diverse datasets are processed to detect, monitor, and analyse ADRs more effectively. This approach facilitates a more comprehensive evaluation of drug safety and supports timely decision-making by regulatory agencies. Integration of RWE into existing pharmacovigilance systems provides an enhanced model that complements traditional clinical trial data and enriches safety monitoring throughout the product lifecycle. Conclusion Pharmacovigilance is a scientific method based on real world data and real world evidence. It helps in early detection and better understanding of adverse drug reactions. It allows more accurate safety monitoring, which ensures drug safety beyond clinical trials.
HPTLC Fingerprinting and Evaluation of Wound Healing Potential of Lotus Leaf Extract-Loaded Nanofiber Patches
The leaves underwent successive extraction with ethanol to obtain a diverse range of bioactive compounds. The fingerprinting of ethanol extracts of Nelumbo nucifera leaves confirmed the separation of distinct phytochemicals according to their polarity index based on the number of separated compounds, colour, and Rf values visualized under UV light at 254 nm. 10 compounds were separated in the ethanolic extract (Rf 0.20–0.87). At 366 nm wavelength in the ethanolic extract, 7 compounds get separated (Rf 0.19–0.84). The ethanol extract was incorporated into nanofibers using polymers PVP K90 and ethyl cellulose. Wound healing efficacy was assessed in an incision model in Wistar rats across four groups: direct extract application, extract-loaded nanofiber patches, placebo nanofibers, and untreated control. Nanofiber patches demonstrated the highest wound closure (99 % by Day 12) and fastest epithelialization (Day 9), followed by direct extract (85 %, Day 11), placebo (70 %, Day 14), and control (65 %, Day 16). These findings suggest that both lotus extract and its nanofiber formulation enhance wound healing, with nanofibers offering improved sustained release and therapeutic benefits.
Nanotechnology-Enabled Drug Delivery: Emerging Platforms, Challenges, and Future Perspectives
Nanotechnology has transformed drug delivery systems by leveraging nanoscale materials to achieve precise, targeted therapeutic administration, thereby enhancing efficacy while reducing adverse effects. Key platforms include nanoparticles, liposomes, micelles, and hydrogels, which enable site-specific delivery for chronic ailments such as cancer, cardiovascular diseases, and neurological disorders. These systems improve drug bioavailability, solubility, and stability, allowing controlled release and the ability to traverse biological barriers like the blood-brain barrier. Methods Recent advancements emphasize smart, stimuli-responsive nanomaterials that react to pH, temperature, or enzymes for on-demand delivery. Integration with green chemistry principles—using natural biomaterials like plant extracts or microorganisms—promotes eco-friendly synthesis, minimizing toxicity and environmental impact in "green nanomedicine." Applications extend to imaging-guided theranostics, combining diagnostics and treatment. Results Nanotechnology-enabled drug delivery systems demonstrate improved drug targeting and controlled release, effectively overcoming biological barriers and enhancing therapeutic outcomes. These approaches show promise in applications ranging from chronic disease treatment to theranostics, offering multifunctionality and sustainable synthesis methods. Conclusion However, challenges persist, including scalability, regulatory approval, long-term biocompatibility, and potential immunogenicity. Future directions involve multifunctional hybrids, personalized medicine via AI-optimized designs, and clinical translation to address unmet needs in global health. This comprehensive overview underscores nanomedicine's potential to revolutionize patient care through innovative, sustainable, and precisely targeted therapeutic strategies.
INNOVATIVE 7-O-SUBSTITUTED CHRYSIN DERIVATIVES AS VEGFR-2 INHIBITORS: A NATURAL PRODUCT-BASED ANTICANCER THERAPY
Introduction VEGFR-2, critical target in cancer therapy, facilitating tumor angiogenesis, yet existing inhibitors face toxicity and resistance issues. Chrysin, flavonoid with anticancer properties, has VEGFR-2 characteristics but suffers from poor pharmacokinetics. A series of 7-O-substituted chrysin derivatives was designed to enhance binding and drug-like properties by incorporating key hydrogen-bonding groups and hydrophobic elements, informed by VEGFR-2 structural analysis. To design and assess novel chrysin derivatives through computational predictions and molecular docking; synthesize and characterize selected derivatives; and evaluate their antioxidant and anticancer activities in vitro, to identify effective candidates that exhibit favorable pharmacokinetics and safety. Methods Chrysin derivatives featuring alkylamino and ester substituents were designed using molecular docking against VEGFR-2. ADMET profiling was conducted to anticipate pharmacokinetics and toxicity. Selected derivatives were synthesized via alkylation and esterification, and characterized using UV, IR, NMR, and mass spectrometry. Antioxidant activity evaluated using DPPH assay, while anticancer efficacy against MCF-7 cells assessed through cell viability assays, comparing IC50 values with those of ascorbic acid and sorafenib. Results Docking studies indicated strong binding affinities, particularly for ester derivatives. ADMET predictions suggested favorable drug-like characteristics. C7 demonstrated remarkable antioxidant activity, exceeding both chrysin and ascorbic acid. In anticancer tests, C7 and C8 displayed significant cytotoxicity (IC50 = 1.0 and 1.5 µM), outperforming chrysin and nearing sorafenib efficacy. Conclusion The improved bioactivity and predicted safety of C7 and C8 highlight efficacy of rational structural modifications in optimizing natural products. Their dual antioxidant and anticancer properties underscore their potential as lead compounds for VEGFR-2-targeted breast cancer treatments. Chrysin derivatives, particularly C7 and C8, exhibit promising VEGFR-2 inhibitory activity and therapeutic potential, supporting their further investigation as multifunctional anticancer agents. Building on these findings, future efforts will focus on developing suitable formulations to improve their solubility, stability, and bioavailability
Formulation and Evaluation of Polyherbal Ethosomal Gel
The present study aims to formulate and evaluate a polyherbal ethosomal gel using ethanolicic extracts of Nyctanthes arbor-tristis, Curcuma longa, and Moringa concanensis for its anti-inflammatory potential. Preliminary phytochemical screening confirmed the presence of bioactive constituents such as alkaloids, flavonoids, phenols, tannins, saponins, and proteins, which are known to contribute to anti-inflammatory and healing activities. Ethosomes were selected as a novel vesicular drug delivery system to enhance the dermal penetration of active phytoconstituents. Ethosomal solutions of plant extracts were prepared and evaluated for key parameters such as vesicle size, zeta potential, and appearance. The ethosomal suspensions exhibited good stability, optimal zeta potential indicating repulsive forces between vesicles, and uniform vesicle size favorable for transdermal delivery. These ethosomal suspensions were further incorporated into a gel base using suitable gelling agents to obtain a polyherbal ethosomal gel. The gel formulations were evaluated for various physicochemical parameters including color, odor, pH, viscosity, spreadability, texture, washability, and extrudability. The results showed that the gel was semisolid, non-irritant, smooth, and had acceptable spreadability and viscosity for easy application. In-vitro anti-inflammatory activity was assessed by the protein denaturation method. The gel exhibited concentration-dependent inhibition of protein denaturation, showing significant results when compared to the standard drug diclofenac sodium.
Molecular Docking, Cytotoxicity and Antioxidant Evaluation of Bioactive Compounds from Capparis zeylanica Linn, Stem.
The present study investigates the phytochemical composition and pharmacological potential of Capparis zeylanica Linn stem, a medicinal plant traditionally employed in Ayurveda and Unani medicine. Phytochemical screening confirmed the presence of alkaloids, flavonoids and phenolic compounds, which were subsequently isolated and characterized using chromatographic and spectroscopic techniques. Molecular docking studies revealed strong binding affinities of the isolated compound with both wild-type and mutant forms of epidermal growth factor receptor (EGFR) kinase, suggesting significant anticancer potential. Pharmacological evaluation through the MTT assay demonstrated notable cytotoxicity of the ethanolic extract against HCC827, H1975 and HT-29 cancer cell lines. Western blot analysis further confirmed the inhibition of EGFR autophosphorylation, validating its mechanistic role in suppressing tumor growth and progression. Furthermore, antioxidant activity, assessed by the DPPH radical scavenging assay, demonstrated marked free-radical neutralizing capacity, underscoring its potential to alleviate oxidative stress. Overall, the findings establish C. zeylanica stem as a valuable source of bioactive compounds exhibiting dual pharmacological properties—anticancer and antioxidant. This study not only provides scientific evidence supporting its traditional applications but also highlights its importance in modern drug discovery, particularly for the development of targeted anticancer therapeutics and antioxidant-based formulations.
Artificial intelligence (AI) is rapidly reshaping modern medicine, especially with the growing availability of digital health records, powerful computing systems, and large-scale genomic data. In clinical settings, AI tools now support diagnostic decision-making, help manage electronic health records, and even guide robotic systems used in surgery and patient care. In genomics, AI has become particularly valuable for interpreting the massive datasets produced by next-generation sequencing. These approaches help identify structural variants, analyze non-coding mutations, and integrate information from multiple omics layers to better understand disease mechanisms. Machine-learning algorithms are also improving the accuracy of read alignment, variant calling, and prediction of how specific genetic changes influence disease. AI has shown impressive success in fields like oncology and medical imaging, often matching or surpassing expert-level performance. It is also accelerating biomarker discovery by allowing researchers to study genome, proteome, and metabolome data at the same time, rather than relying on traditional single-marker methods. In neurodevelopmental disorders, AI has helped analyze complex sequencing datasets and improved interpretation of genetic variants linked to conditions such as autism and epilepsy. Despite its potential, the use of AI in genomics faces important challenges. These include the need for high-quality data, difficulties in interpreting complex model outputs, ethical concerns around privacy, risks of algorithmic bias, and the high computational resources required. While AI offers significant opportunities to advance precision medicine, its best use will come from combining powerful computational tools with skilled human expertise to ensure accuracy, fairness, and responsible clinical application.
Artificial Intelligence in Medicinal Chemistry: A Comprehensive Review
Artificial intelligence (AI) has developed as a transformative force in medicinal chemistry, redesigning early drug discovery, hit identification, lead optimisation, and synthetic route planning. Effective exploration of chemical space and determination of key molecular properties has been enabled by advances in machine learning (ML), deep learning (DL), graph neural networks (GNNs), and generative models (such as variational autoencoders and transformer-based chemical language models). AI now plays a vital role across virtual screening, de-novo design, reaction prediction, retrosynthesis planning, ADMET profiling, and structure-based drug design. Remains challenging despite its rapid progress which includes data quality issues, model interpretability, experimental validation gaps, and integration into existing medicinal chemistry workflows. This review summarises the current AI methodologies, major applications, advantages, limitations and future opportunities in medicinal chemistry.
AI – Driven Drug Discovery and Development
Artificial intelligence has transformed drug discovery and development by decreasing cost and improves successful outcome. Artificial intelligence various processes for identification of drug efficacy and includes machine learning and natural language processing. It is help to analyze large data sets and quick identification(1). Artificial intelligence also predicts bio pharmaceutics such as absorption, bioavailability and excretion, pharmacokinetics and toxicology. Artificial intelligence also improves clinical trials with minimizing risk. Artificial intelligence deals with challenges such as data quality and compliance challenges. The use of artificial intelligence increase in many sectors mainly the pharmaceutical industry. Artificial intelligence improves drug productivity, remove work load on humans. Artificial intelligence helps in overall quality of product(2)
AI-Driven Epitranscriptomic Therapeutics: Integrating RNA Modifications into Next-Generation Drug Discovery
The convergence of artificial intelligence (AI) and epitranscriptomics is redefining the future landscape of therapeutic innovation by transforming how RNA modifications are decoded, interpreted, and targeted for drug discovery. Epitranscriptomic marks such as m⁶A, m⁵C, Ψ, and ac⁴C form a dynamic biochemical “RNA language” that governs transcript stability, translation, and cellular fate, yet their complexity has historically remained inaccessible to conventional molecular tools. Advanced AI frameworks—ranging from transformer-driven modification callers to generative deep-learning engines—now enable real-time identification of rare and cryptic RNA marks directly from raw nanopore signals, uncovering disease-specific epicode signatures with unprecedented resolution. These computational models further predict structural consequences of modified RNA, simulate RNA–drug interactions, and design precision inhibitors for writers, readers, and erasers such as METTL3, FTO, and YTH-domain proteins. AI-guided CRISPR–Cas13 epitranscriptome editing expands therapeutic possibilities by enabling reversible, non-genomic correction of pathogenic RNA modification patterns. Collectively, these innovations are propelling RNA-centric pharmacology beyond traditional genomics, establishing modification-aware digital RNA twins, autonomous RNA drug-design pipelines, and personalized epitranscriptomic therapies. This review synthesizes emerging breakthroughs, ongoing challenges, ethical considerations, and future directions that position AI-driven epitranscriptomic therapeutics as a transformative axis in next-generation drug discovery and precision medicine.
Digital Twin Technology in Biopharmaceutical Research: Creating Predictive Models of Human Biology
Digital twin (DT) technology represents a revolutionary frontier in biopharmaceutical research, enabling the creation of high-fidelity, predictive simulations of human biology. By integrating multi-dimensional datasets—ranging from genomics, transcriptomics, proteomics, and metabolomics to real-time clinical and wearable device data—digital twins provide a dynamic, virtual counterpart of individual patients or populations. Artificial intelligence and machine learning algorithms empower these models to simulate complex physiological processes, anticipate drug responses, and predict off-target effects with unprecedented precision. In drug discovery and development, DTs facilitate in silico preclinical trials, optimize pharmacokinetic and pharmacodynamic properties, and accelerate lead compound selection while minimizing resource-intensive laboratory experiments. Personalized medicine applications are further enhanced, as DTs allow for individualized therapy simulations, dose adjustments, and risk prediction, reducing adverse events and improving therapeutic outcomes. Despite these advantages, challenges remain, including data integration from heterogeneous sources, computational limitations, model validation, and ethical considerations around predictive patient modelling. Looking forward, the convergence of AI-enhanced digital twins with IoT-enabled monitoring, real-world evidence, and cloud-based computational platforms promises to transform translational research, enabling a future where drug development is faster, safer, and tailored to the biological uniqueness of each patient.