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.
Precision Medicine at the Nanoscale: Nanorobots for HIV Detection and Targeted Therapeutic Intervention
While current antiretroviral therapies effectively suppress HIV, they fail to eradicate it because the virus hides in inaccessible anatomical reservoirs like the brain. Nanorobots present a paradigm shift. Engineered at the nanoscale, these systems can cross formidable biological barriers to reach these hidden viral sanctuaries. By utilizing surface targeting ligands to recognize specific markers on infected cells, nanorobots ensure highly selective drug delivery. This precise targeting minimizes systemic toxicity and maximizes therapeutic impact directly at the site of viral activity. Although challenges in scaling and long-term biocompatibility persist, nanorobotics offers a viable path from lifelong viral suppression to complete eradication.
Bridging the Gap between Pharmacy Education and NHS Workforce Needs
The transformation of pharmacy practice within the National Health Service (NHS) demands a workforce equipped with advanced clinical, prescribing, and digital competencies. However, disparities persist between traditional pharmacy education and contemporary workforce requirements. This article critically examines these gaps and proposes evidence-based strategies, including curriculum reform, experiential learning, interprofessional education, and policy alignment, to bridge the divide between academia and practice
Emerging Innovations in Natural Product Research for Modern Drug Development
Natural products remain one of the most important sources of therapeutic agents and continue to contribute significantly to modern drug discovery and development. A substantial number of approved pharmaceuticals are derived directly or indirectly from natural sources such as plants, microorganisms, fungi, and marine organisms. These compounds possess exceptional structural diversity and biological activities that make them valuable templates for drug development. However, conventional natural product research faces several challenges, including low metabolite yield, lengthy extraction procedures, rediscovery of known compounds, and difficulties in large-scale production. Recent technological advances have transformed natural product research into a multidisciplinary and data-driven field. Artificial intelligence and machine learning improve the prediction of biological activity, toxicity, and pharmacokinetic properties, thereby accelerating compound screening. Genome mining and synthetic biology facilitate the discovery and engineering of biosynthetic pathways responsible for novel metabolite production. Metabolomics and multi-omics technologies provide comprehensive insights into metabolic networks and enhance compound characterization. Marine biotechnology continues to reveal structurally unique molecules with promising therapeutic applications. In addition, nanotechnology-based delivery systems improve the solubility, stability, bioavailability, and targeting efficiency of natural compounds. The integration of these innovative technologies is accelerating drug discovery while supporting sustainability and precision in pharmaceutical development. This review summarizes recent advances in natural product research and discusses their contributions, challenges, and future prospects in the development of effective therapeutic agents.
Next-Generation Multifunctional Stimuli-Responsive Self-Healing Hydrogels for Precision Therapeutics and Advanced Regenerative Tissue Engineering
This report provides a comprehensive structure and content outline for a review paper on next-generation multifunctional stimuli-responsive self-healing hydrogels. It begins with an introduction covering the evolution from conventional to smart hydrogels, the concepts of stimuli-responsiveness and self-healing, and their importance in precision therapeutics and tissue engineering. The core sections detail the fundamentals of self-healing mechanisms and stimuli-responsive principles, the materials (natural/synthetic polymers, nanomaterials) and advanced engineering/fabrication strategies used. It extensively covers biomedical applications in precision therapeutics (e.g., controlled drug delivery, cancer therapy) and regenerative tissue engineering (e.g., skin, bone, neural, cardiac repair). The report concludes with discussions on emerging functionalities (e.g., thernostics, AI- assisted design), biological safety, clinical translation challenges, and future perspectives, fully addressing the user's specified title and sub-headings.
Bio-informatic Approaches in Agada Tantra: Applications of Omics Data, Network Pharmacology and Computational Toxicology for Poison Management
Agada Tantra, the toxicology branch of Ayurveda, addresses animate and inanimate poisons and includes preventive, diagnostic, and therapeutic approaches for toxic exposures.[cite:30] Contemporary toxicology increasingly relies on bioinformatic methods that integrate omics datasets, biological networks, and computational modeling to improve mechanistic understanding and safety prediction.[cite:28][cite:32] This article examines how omics, network pharmacology, and computational toxicology can be adapted to Agada Tantra for precision poison management, antidote discovery, and translational evidence building.[cite:24][cite:32] A narrative integrative methodology is proposed that combines classical Ayurvedic toxicology concepts with toxicogenomics, metabolomics, QSAR, PBPK modeling, and network-based target analysis.[cite:30][cite:28][cite:32] The resulting framework supports earlier hazard identification, better characterization of toxin–host interactions, rational evaluation of multi-component antidotal formulations, and improved prioritization for laboratory and clinical validation.[cite:24][cite:32]
Early Prediction of Cardiac Diseases Using Artificial Intelligence, Mattibanana Extract, Molecular Docking, and Novel Drug Formulation
Cardiovascular diseases (CVDs), including myocardial infarction and angina pectoris, are major causes of global mortality and morbidity. Delayed diagnosis, unhealthy lifestyle, stress, diabetes, hypertension, and hyperlipidemia significantly increase cardiac risk. Early prediction and effective treatment are essential for reducing complications and improving patient survival. This article proposes an integrated approach combining Artificial Intelligence (AI), phytopharmaceutical research, molecular docking, and novel drug formulation using Mattibanana plant extract as a natural cardioprotective agent. The AI-based predictive system analyzes clinical and physiological parameters such as ECG signals, blood pressure, cholesterol levels, glucose levels, heart rate variability, age, family history, obesity, smoking habits, and stress biomarkers. Machine learning and deep learning techniques including Random Forest, Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN) are used to identify hidden patterns and predict the risk of myocardial infarction and angina at an early stage. The system aims to provide rapid, accurate, and low-cost cardiac risk assessment. Simultaneously, phytochemical constituents present in Mattibanana extract are screened for cardioprotective activity. Molecular docking studies are performed against cardiovascular targets such as angiotensin-converting enzyme (ACE), HMG-CoA reductase, and inflammatory proteins to evaluate binding affinity and therapeutic potential. Selected compounds are further analyzed using ADMET prediction to determine drug-likeness and safety profiles. Lead compounds with favorable properties are proposed for development into novel formulations such as tablets, nanoparticles, or nanoemulsions. This multidisciplinary approach provides a promising strategy for preventive cardiology and plant-based drug discovery.
Integration of AI-Driven Design with Nanotechnology for Accelerated Drug Development
Drug discovery and development is a complex, time-consuming, and costly process with high failure rates, necessitating innovative strategies to improve efficiency and success. In recent years, Artificial Intelligence (AI) has emerged as a transformative tool in pharmaceutical research, enabling rapid data analysis, predictive modeling, and intelligent decision-making across various stages of drug development. Simultaneously, nanotechnology has revolutionized drug delivery systems by enhancing solubility, stability, bioavailability, and targeted delivery of therapeutic agents through advanced nanocarriers such as polymeric nanoparticles, liposomes, and nanosuspensions. This review aims to explore the integration of AI-driven design approaches with nanotechnology to accelerate drug development. The synergistic combination of AI and nanotechnology offers significant advantages, including optimized formulation design, precise control over nanoparticle characteristics, improved prediction of drug loading and release profiles, and enhanced targeting efficiency. AI techniques such as machine learning and deep learning facilitate the rational design of nanocarriers, reducing experimental burden and enabling data-driven decision-making. The review highlights key applications of AI-integrated nanotechnology in cancer therapy, infectious disease management, personalized medicine, and gene delivery systems. Despite these advancements, challenges such as data limitations, model interpretability, regulatory barriers, and scalability remain critical concerns. The convergence of AI and nanotechnology represents a promising frontier in modern pharmaceutics, with the potential to significantly shorten development timelines, reduce costs, and improve therapeutic outcomes. Future research focusing on interdisciplinary collaboration and robust regulatory frameworks will be essential to translate these innovations into clinical practice.
AI-Driven Drug Discovery: A Critical Evaluation of Model Reliability, Data Bias, and Clinical Translation
Artificial Intelligence (AI) is rapidly transforming the landscape of drug discovery by enabling faster and more efficient identification of potential therapeutic candidates. Advanced computational techniques such as machine learning (ML), deep learning (DL), and natural language processing (NLP) have demonstrated significant potential in analyzing complex biological datasets, predicting drug-target interactions, and optimizing lead compounds. Despite these advancements, several critical challenges hinder the widespread adoption of AI in pharmaceutical research. Model reliability remains a major concern due to issues such as overfitting, limited generalizability, and lack of interpretability. Additionally, biases present in training datasets can result in skewed predictions, thereby affecting the safety and efficacy of AI-generated drug candidates. Furthermore, the translation of AI-based discoveries from computational models to clinical applications continues to face substantial barriers, including regulatory constraints and validation complexities. This review critically examines these challenges, emphasizing the need for robust validation strategies, high-quality datasets, and interdisciplinary collaboration. Addressing these limitations is essential for realizing the full potential of AI in developing safe, effective, and accessible therapeutics.
Comprehensive Overview of “Artificial Intelligence in Drug Development and Drug Discovery
Artificial intelligence (AI) is reshaping pharmaceutical research by tackling long timelines, high costs, and frequent failures in drug development. The global AI in drug discovery market, worth $1.72 billion in 2024, is expected to reach $8.5–16.5 billion by 2030–2034, supported by massive investments exceeding $60 billion in the last decade. Today, nearly all major pharmaceutical companies are integrating AI into their R&D pipelines. Applications span target identification, drug design, protein structure prediction, virtual screening, ADMET profiling, and clinical trial optimization. Break throughs such as AlphaFold for protein structures, generative design for molecules, and AI-enhanced clinical trials have already shown measurable impact—cutting recruitment times and allowing adaptive protocols. Real-world successes, including Insilico Medicine’s INS018-055 and Exscientia’s oncology candidates, highlight how AI-driven drugs are progressing faster into clinical testing with higher success potential. Regulators like the FDA are also advancing new frameworks to ensure transparency and reliability in AI applications. While challenges remain—such as biased datasets, model interpretability, and integration into established workflows—emerging technologies like federated learning, multimodal AI, and autonomous labs signal even greater advances ahead. Ultimately, AI is steering drug discovery away from trial-and-error toward predictive, data-driven development, unlocking safer and more effective therapies.
Molecular Docking Studies of Phytoconstituents for Neuroprotection in Alzheimer’s Disease
Alzheimer’s disease (AD) is a neurodegenerative disorder marked by cognitive and behavioural impairment that causes memory loss and cognitive decline. This review uses molecular docking against the human acetylcholinesterase enzyme (PDB ID: 4EY7) to investigate the neuroprotective potential of phytochemicals, such as terpenes, alkaloids and polyphenols. Compounds with better binding affinities (up to -10.8 kcal/mol) and dual-site interactions at the Catalytic Anionic Site (CAS) and Peripheral Anionic Site (PAS) are highlighted by analysis, such as Pterygospermine and Huperzine A. This work emphasizes the function of phytochemicals as multi-target therapeutic leads that can reduce oxidative stress and neuroinflammation by assessing molecular dynamics and binding orientations.
Engineering Smart Polymeric Nanosystems for Multi- Targeted Drug Delivery in Chronic Diseases
Chronic diseases including cancer, diabetes mellitus, cardiovascular disorders, and neurodegenerative conditions constitute the leading cause of global morbidity and mortality, yet their pharmacological management remains constrained by the non-specific biodistribution, poor bioavailability, and systemic toxicity inherent to conventional drug delivery systems. Over the past two decades, polymeric nanosystems have emerged as a transformative platform capable of encapsulating diverse therapeutic payloads, controlling release kinetics, and navigating complex biological barriers. The concept of "smart" nanosystems extends this paradigm further: by integrating stimuli-responsive moieties sensitive to pH, temperature, redox gradients, or enzymatic activity these carriers can achieve spatiotemporal precision in drug release that conventional systems cannot approximate. Multi-targeted strategies, combining passive accumulation via the enhanced permeability and retention (EPR) effect with active ligand– receptor recognition and intracellular delivery mechanisms, offer the prospect of simultaneously addressing multiple pathological pathways within a single nanoscale architecture. This review synthesizes the current literature on the design, engineering, preparation, and characterization of smart polymeric nanosystems, and critically evaluates their therapeutic applications across major chronic diseases. We examine recent advances including AI-assisted polymer design, theranostic integration, and hybrid nanocarrier architectures, while honestly appraising the translational challenges of scale-up manufacturing, regulatory complexity, and clinical validation. Looking forward, the convergence of smart polymeric nanotechnology with personalized medicine, gene therapy, and bioelectronics portends a paradigm shift toward truly adaptive, patient-specific therapeutic systems.
AI- and LLM-Driven Drug Discovery Pipeline for Predictive Evaluation of Nimbolide and Salannin in Oral Squamous Cell Carcinoma
Background: Oral squamous cell carcinoma (OSCC) is a major malignancy associated with complex molecular pathways involving inflammation, oxidative stress, immune dysregulation, and abnormal cell proliferation. Conventional drug discovery approaches are often time-consuming and less effective for multi-target diseases. Recent advances in artificial intelligence (AI) and large language models (LLMs) have enabled systematic exploration of phytochemicals such as neem-derived compounds. Objective: To develop an AI- and LLM-driven integrative research pipeline using the Swalife platform to evaluate the therapeutic potential of neem bioactives, particularly nimbolide and salannin, in OSCC. Methods: A multi-stage AI-assisted workflow was implemented using literature mining from PubMed, Google Scholar, and ScienceDirect, followed by LLM-assisted data extraction using Perplexity AI. Six modules were applied: target identification, lead optimization, in vitro design, in vivo design, clinical pharmacovigilance, and market/IPR analysis. Outputs were structured into HTML datasets and integrated into predictive and preventive medicine frameworks. Results: Nimbolide and salannin showed multi-target activity across major OSCC pathways, including NF-κB, STAT3, PI3K/Akt, and EGFR. Approximately 70–80% target overlap was observed between neem bioactives and OSCC molecular pathways. Predictive modeling confirmed strong anti-inflammatory, pro-apoptotic, anti- angiogenic, and immunomodulatory effects. Conclusion: The AI- and LLM-driven Swalife pipeline effectively bridges mechanistic discovery and predictive medicine. Nimbolide and salannin demonstrate strong potential as multi-target adjunct therapies in OSCC, supporting further experimental and clinical validation.
AI- and LLM-Driven Drug Discovery to Predictive Therapeutics: Investigating Nimbolide and Azadirachtin in Oral Squamous Cell Carcinoma Using Swalife Research Platforms
Oral squamous cell carcinoma (OSCC) remains a leading cause of cancer mortality, driven by dysregulated pathways such as NF-κB, STAT3, PI3K/AKT, and MAPK. Nimbolide and azadirachtin, limonoids from Azadirachta indica (neem), exhibit promising preclinical anticancer effects by modulating these pathways, inhibiting proliferation, and inducing apoptosis in OSCC models. This review synthesizes published literature evidence, highlights AI/LLM roles in discovery via Swalife platforms for network pharmacology and predictive modeling, addresses pharmacokinetics/formulation challenges, and notes the absence of clinical trials. Swalife-based AI tools are proposed to enable structured hypothesis generation and multi- target analysis.
AI Assisted Mechanistic and Translational Evaluation of Moringa oleifera in Alzheimer’s disease: A Preclinical-to-Clinical Study
Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterized by amyloid-β deposition, tau pathology, oxidative stress, and chronic neuroinflammation. Current therapeutic options provide only symptomatic relief, underscoring the need for novel, multi-target strategies. This study investigates the potential of Moringa oleifera as a phytotherapeutic candidate for AD by systematically reviewing preclinical and translational evidence. The plant is rich in bioactive compounds, particularly flavonoids such as quercetin and kaempferol, which demonstrate antioxidant, anti-inflammatory, and cholinesterase inhibitory properties. Computational analyses, including molecular docking and pharmacokinetic predictions, suggest favorable drug-like characteristics and interactions with key AD-related pathways, including NF-κB, Nrf2, and amyloidogenic mechanisms. Experimental studies in vitro and in vivo consistently report neuroprotective effects, including reduced oxidative stress, decreased amyloid burden, improved cognitive performance, and modulation of inflammatory mediators. Despite these promising findings, clinical validation remains limited, with challenges related to standardized formulations and the absence of large-scale human trials. Overall, Moringa oleifera emerges as a promising adjunct candidate for AD management, warranting further translational and clinical investigations to establish its therapeutic efficacy and safety in humans.
AI- and LLM-Driven Discovery to Predictive Medicine of Curcumin and Demethoxycurcumin in Oral Squamous Cell Carcinoma Using Swalife Research Platforms
Background: Oral Squamous Cell Carcinoma (OSCC) is a major global health burden, particularly in developing countries like India, with strong associations to tobacco use, chronic inflammation, and metabolic dysregulation. Conventional therapies often face limitations such as toxicity, recurrence, and resistance. Curcumin and its analog demethoxycurcumin, derived from Curcuma longa, have demonstrated multi-target anticancer potential. However, systematic integration of AI-driven approaches for mechanistic discovery to predictive medicine remains underexplored. Objective: To develop an AI- and large language model (LLM)-driven integrated research pipeline using Swalife platforms to evaluate the mechanistic, preclinical, clinical, and predictive potential of curcumin and demethoxycurcumin in OSCC. Methods: A multi-stage workflow was employed combining literature mining (PubMed, Google Scholar, ScienceDirect), AI-driven prompt-based analysis (Perplexity AI), and Swalife research tools. Six major models were developed: Target & Mechanism, Lead Identification & Optimization, In vitro Design, In vivo Design, Clinical & Pharmacovigilance (PV), and Market & IPR. Data were structured into standardized datasets and subjected to cross-validation and consistency checks to improve reliability. This study was conducted as an AI- assisted, in silico analysis based on systematic literature mining and biomedical databases. No new in vitro, in vivo, or clinical experiments were performed. Data were synthesized using AI tools and validated through cross- checking with published studies. Results: Curcumin has been reported to demonstrate multi-target modulation across key OSCC pathways including NF-κB, PI3K/Akt, STAT3, and Wnt/β-catenin. Conclusion: This study presents an AI-driven integrated framework for mapping curcumin and demethoxycurcumin from molecular mechanisms to predictive medicine in OSCC. This workflow demonstrates a scalable and reproducible model for drug discovery and translational research using natural compounds.
AI Assisted Mechanistic and Translational Evaluation of Triticum aestivum in Alzheimer’s disease: A Preclinical-to-Clinical Study
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder marked by amyloid-β accumulation, tau hyperphosphorylation, oxidative stress, neuroinflammation, and synaptic dysfunction. extensive research, effective disease-modifying therapy remains limited. Wheatgrass (Triticum aestivum) has garnered attention as a rich source of bioactive phytochemicals, particularly apigenin, which exhibits antioxidant, anti-inflammatory, and neuroprotective properties. current evidence on the potential role of wheatgrass-derived apigenin in AD management, with an emphasis on its interaction with glycogen synthase kinase-3β (GSK-3β) and related signalling pathways. Available preclinical studies indicate that apigenin improves cognitive performance, reduces oxidative damage, suppresses inflammatory mediators, and attenuates tau phosphorylation and amyloidogenic processing. Mechanistically, its neuroprotective effects involve activation of the PI3K/Akt pathway, inhibition of GSK-3β, modulation of BDNF/CREB signalling, and preservation of neuronal integrity. Wheatgrass extract has also demonstrated memory-enhancing and anti-inflammatory effects in experimental models of cognitive impairment, suggesting that its therapeutic potential may extend beyond apigenin alone. However, the evidence remains preclinical, and issues such as low oral bioavailability, limited blood–brain barrier penetration, and metabolic instability continue to restrict clinical translation. Therefore, further pharmacokinetic optimization, standardized formulation development, and well-designed clinical studies are required to validate its efficacy in humans. Overall, wheatgrass-derived apigenin represents a promising natural candidate for multi-target intervention in AD.
AI Assisted Mechanistic and Translational Evaluation of Turmeric in Breast Cancer: A Preclinical-to-Clinical Study
Breast cancer continues to be a biologically diverse and therapeutically complex malignancy, marked by intricate signaling networks, variability among subtypes, and mechanisms of adaptive resistance. There is growing evidence that underscores the necessity for multi-target therapeutic approaches that can modulate interconnected oncogenic pathways. Turmeric (Curcuma longa), a medicinal plant that has been extensively researched, contains bioactive phytoconstituents such as curcumin, demethoxycurcumin, and turmerone, which are known for their anti-inflammatory, antioxidant, and anticancer effects. Nevertheless, there is a scarcity of systematic translational frameworks for the structured assessment of these compounds in the context of breast cancer. This study introduces an AI-assisted translational drug discovery framework that integrates large language models (LLMs) and scientific prompting to assess turmeric-derived compounds in breast cancer. The framework is divided into six modular domains: target identification and mechanistic mapping, lead optimization, design of in vitro validation, in vivo translational modeling, development of clinical strategies, and alignment with regulatory and commercial standards. The structured prompt-driven interrogation facilitates hypothesis generation, prioritization of pathways, and mapping of compound-target interactions within a systems biology framework. AI-driven analysis has pinpointed crucial oncogenic pathways, including PI3K/AKT/mTOR, MAPK/ERK, NF-κB, and apoptotic regulators, as primary targets of interest. Phytoconstituents derived from turmeric have shown compatibility with multiple targets, thereby endorsing a polypharmacological approach. The downstream components included ADMET profiling, docking simulations, frameworks for experimental validation, pharmacovigilance strategies, and positioning of intellectual property, which collectively ensure a seamless transition from molecular inference to translational viability. This research establishes a scalable and integrative framework, supported by AI, for the discovery of phytochemical drugs aimed at breast cancer. By merging computational intelligence with structured translational modeling, this methodology propels turmeric-derived compounds from the generation of mechanistic hypotheses towards the potential development of therapeutics, while underscoring the importance of both experimental and clinical validation.
Translational Evaluation of Beetroot-Derived Betanin in Oral Cancer Using a Sequential AI-Guided Evidence Framework
Oral cancer, particularly oral squamous cell carcinoma (OSCC), remains a major global health challenge due to its high morbidity, late-stage diagnosis, and limited therapeutic efficacy. The disease is characterized by complex and interconnected molecular mechanisms, including dysregulation of PI3K/AKT/mTOR, MAPK/ERK, NF-κB, and STAT3 signaling pathways, along with oxidative stress imbalance, immune evasion, and resistance to apoptosis. These multifactorial processes necessitate the development of multi-target therapeutic strategies. Beetroot (Beta vulgaris), a nutritionally rich medicinal plant, contains diverse bioactive compounds such as betalains (notably betanin), flavonoids, and phenolic acids, which exhibit antioxidant, anti-inflammatory, and anticancer properties. Despite increasing evidence supporting its anticancer potential, systematic evaluation of beetroot-derived compounds in oral cancer remains limited. In this study, a structured AI-guided translational framework was employed to investigate the therapeutic potential of beetroot-derived phytoconstituents in oral cancer. The framework was organized into six sequential modules, including target identification, lead optimization, in vitro validation design, in vivo translational modeling, clinical strategy development, and market regulatory alignment. Mechanistic insights revealed that beetroot bioactives modulate key oncogenic pathways by inhibiting NF-κB and STAT3 signaling, inducing apoptosis via caspase activation, regulating oxidative stress through Nrf2 pathways, and suppressing angiogenesis by downregulating VEGF expression. Network-based intersection analysis demonstrated a significant overlap between oral cancer-associated genes and beetroot targets, supporting the biological plausibility of these interactions. Overall, this study establishes a comprehensive and scalable framework for evaluating beetroot-derived compounds as multi-target therapeutic candidates in oral cancer. Although current evidence remains largely preclinical, the findings highlight strong mechanistic relevance and translational potential, warranting further experimental and clinical validation.
AI and LLM-Driven Drug Discovery to Predictive Medicine of Demethoxycurcumin and Bisdemethoxycurcumin in Oral Squamous Cell Carcinoma Using Swalife Research Platform
Oral squamous cell carcinoma (OSCC) burdens India with ~144,000 annual cases driven by tobacco/betel-induced NF-κB inflammation, PI3K-Akt survival, and MAPK invasion pathways. This 12-week computational internship at Swalife Biotech systematically addressed the research gap: no structured AI-network pharmacology workflow existed for prioritizing Curcuma longa bioactives in OSCC. Objective: Deploy AI/LLM-driven (DeepSeek, Perplexity) 10-module pipeline to generate mechanistic hypotheses and prioritize leads from literature data. Methods: Sequential workflow—(1) literature mining (curcuminoids/terpenoids), (2) LLM comparison, (3-4) target/lead identification, (5-10) mechanistic/clinical/PV/market/IPR/predictive modules—deployed ~120 structured prompts mapping 132+ compound-target-pathway interactions. Results: Demethoxycurcumin (DMC; IC50 ~15μM, Akt/mTOR/PD-L1) and bisdemethoxycurcumin (BDMC; MMP2/9-JNK/p38) emerged as leads covering 82% OSCC pathways. Literature confirms apoptosis (4NQO- mice tumor reduction), stability advantages over curcumin, and mucositis adjunct potential (meta-analysis p=0.03). Generated Hypotheses: (1) DMC/BDMC nanoformulations enhance OSCC chemoprevention (4NQO trials); (2) NF-κB/EMT synergy with cisplatin (CAL-27 spheroids); (3) Phase I window-of-opportunity safety pre-surgery. Conclusion: Swalife's AI-pipeline demonstrates computational hypothesis generation—not experimental validation—streamlining herbal complexity to preclinical readiness. This reproducible workflow bridges network pharmacology to predictive medicine, warranting wet-lab confirmation of prioritized DMC/BDMC as multi-target OSCC candidates.
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.
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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.