Search Results (221)

Background: Hypertension is a serious global health issue affecting over one billion adults and leading to severe complications if left unmanaged. Despite medical advancements, only a fraction of patients effectively have their hypertension under control. Among the factors that hinder adherence to hypertensive drugs are the debilitating side effects of the drugs. The lack of adherence results in poorer patient outcomes as patients opt to live with their condition, instead of having to deal with the side effects. Hence, there is a need to discover new hypertension drug molecules with better side effects to increase patient treatment options. To this end, computational methods such as artificial intelligence (AI) have become an exciting option for modern drug discovery. AI-based computational drug discovery methods generate numerous new lead antihypertensive drug molecules. However, predicting their potential side effects remains a significant challenge because of the complexity of biological interactions and limited data on these molecules. Methods: This paper presents a machine learning approach to predict the potential side effects of computationally synthesised antihypertensive drug molecules based on their molecular properties, particularly functional groups. We curated a dataset combining information from the SIDER 4.1 and ChEMBL databases, enriched with molecular descriptors (logP, PSA, HBD, HBA) using RDKit. Results: Gradient Boosting gave the most stable generalisation, with a weighted F1 of 0.80, and AUC-ROC of 0.62 on the independent test set. SHAP analysis over the cross-validation folds showed polar surface area and logP contributing the largest global impact, followed by hydrogen bond counts. Conclusions: Functional group patterns, augmented with key ADMET descriptors, offer a first-pass screen for identifying side-effect risks in AI-designed antihypertensive leads. Full article

Background/Objectives: Gastric cancer (GC) remains a significant global health burden due to its high mortality rate and frequent diagnosis at advanced stages. This study aimed to identify reliable diagnostic biomarkers and elucidate molecular mechanisms underlying GC by integrating transcriptomic data from independent platforms and applying machine learning techniques. Methods: Two transcriptomic datasets from the Gene Expression Omnibus were analyzed: GSE26899 (microarray, n = 108) as the discovery dataset and GSE248612 (RNA-seq, n = 12) for validation. Differential expression analysis was conducted using limma and DESeq2, selecting genes with |log2FC| > 1 and adjusted p < 0.05. The top 200 differentially expressed genes (DEGs) were used to develop machine learning models (random forest, logistic regression, neural networks). Functional enrichment analyses (GO, KEGG, Hallmark) were applied to explore relevant biological pathways. Results: In GSE26899, 627 DEGs were identified (201 upregulated, 426 downregulated), with key genes including CST1, KIAA1199, TIMP1, MSLN, and ATP4A. The random forest model demonstrated excellent classification performance (AUC = 0.952). GSE248612 validation yielded 738 DEGs. Cross-platform comparison confirmed 55.6% concordance among core genes, highlighting CST1, TIMP1, KRT17, ATP4A, CHIA, KRT16, and CRABP2. Enrichment analyses revealed involvement in ECM–receptor interaction, PI3K-Akt signaling, EMT, and cell cycle. Conclusions: This integrative transcriptomic and machine learning framework effectively identified high-confidence biomarkers for GC. Notably, CST1, TIMP1, KRT16, and ATP4A emerged as consistent, biologically relevant candidates with strong diagnostic performance and potential clinical utility. These findings may aid early detection strategies and guide future therapeutic developments in gastric cancer. Full article

The escalating impacts of climate change pose significant threats to global agriculture, necessitating a rapid development of climate-resilient crop varieties. The integration of multi-omics technologies—such as genomics, transcriptomics, proteomics, metabolomics, and phenomics—has revolutionized our understanding of the intricate molecular networks that govern plant stress responses. Coupled with advanced predictive modeling approaches such as machine learning, deep learning, and multi-omics-assisted genomic selection, these integrated frameworks enable accurate genotype-to-phenotype predictions that accelerate breeding for augmented stress tolerance. This review comprehensively synthesizes the current strategies for multi-omics data integration, highlighting computational tools, conceptual frameworks, and challenges in harmonizing heterogeneous datasets. We examine the contribution of digital phenotyping platforms and environmental data in dissecting genotype-by-environment interactions critical for climate adaptation resilience. Further, we discuss technical, biological, and ethical challenges, encompassing computational bottlenecks, trait complexity, data standardization, and equitable data sharing. Finally, we outline future directions that prioritize scalable infrastructures, interpretability, and collaborative platforms to facilitate the deployment of multi-omics-guided breeding in diverse agroecological contexts. This integrative approach possesses transformative potential for the development of resilient crops, ensuring agricultural sustainability amidst increasing environmental volatility. Full article

Plasmodium falciparum is the causative agent of malaria, a parasitic disease that affects millions of people in terms of prevalence and is associated with hundreds of thousands of deaths. Current antimalarial medications, in addition to exhibiting moderate to serious adverse reactions, are not efficacious enough due to factors such as drug resistance. In silico approaches can speed up the discovery and design of new molecules with wide-spectrum antimalarial activity. Here, we report a unified computational methodology combining a perturbation theory machine learning model based on multilayer perceptron networks (PTML-MLP) and the fragment-based topological design (FBTD) approach for the prediction and design of novel molecules virtually exhibiting versatile antiplasmodial activity against diverse P. falciparum strains. Our PTML-MLP achieved an accuracy higher than 85%. We applied the FBTD approach to physicochemically and structurally interpret the PTML-MLP, subsequently extracting several suitable molecular fragments and designing new drug-like molecules. These designed molecules were predicted as multi-strain antiplasmodial inhibitors, thus representing promising chemical entities for future synthesis and biological experimentation. The present work confirms the potential of combining PTML modeling and FBTD for early antimalarial drug discovery while opening new horizons for extended computational applications for antimicrobial research and beyond. Full article

Background/Objectives: Artificial intelligence (AI) is transforming drug discovery and development by enhancing the speed and precision of identifying drug candidates and optimizing their efficacy. This review evaluates the application of AI in various stages of drug discovery, from hit identification to lead optimization, and its impact on clinical outcomes. The objective is to provide insights into the role of AI across therapeutic areas and assess its contributions to improving clinical trial efficiency and pharmaceutical outcomes. Methods: A systematic review followed PRISMA guidelines to analyze studies published between 2015 and 2025, focusing on AI in drug discovery and development. A comprehensive search was performed across multiple databases to identify studies employing AI techniques. The studies were categorized based on AI methods, clinical phase, and therapeutic area. The percentages of AI methods used, clinical phase stages, and the therapeutic regions were analyzed to identify trends. Results: AI methods included machine learning (ML) at 40.9%, molecular modeling and simulation (MMS) at 20.7%, and deep learning (DL) at 10.3%. Oncology accounted for the majority of studies (72.8%), followed by dermatology (5.8%) and neurology (5.2%). In clinical phases, 39.3% of studies were in the preclinical stage, 23.1% in Clinical Phase I, and 11.0% in the transitional phase. Clinical outcome reporting was observed in 45% of studies, with 97% reporting industry partnerships. Conclusions: AI significantly enhances drug discovery and development, improving drug efficacy and clinical trial outcomes. Future work should focus on expanding AI applications into underrepresented therapeutic areas and refining models to handle complex biological systems. Full article

This review investigates the novel idea that proteins catalyze chemical reactions through conformational changes driven by energy derived from their collisions with water molecules. Recent studies have suggested that proteins in solution undergo constant deformation due to collisions with water molecules, generating potential energy that can be harnessed for catalytic functions. We detail the existing evidence supporting this idea, including how structures in proteins such as α-helices and β-sheets facilitate energy conversion, how conformational changes can affect the ways in which substrates attach, and how reactions occur. Combining information from computer-based methods—such as molecular dynamics simulations and machine learning models (e.g., AlphaFold)—we suggest a more complete model for understanding how proteins function beyond simply looking at their fixed shapes. This emerging view has implications for drug design, enzyme engineering, and our fundamental understanding of biological catalysis. Full article

Background/Objectives: Lumbar disc herniation (LDH) is the most common condition associated with low back pain, and it adversely impacts individuals’ health. The interplay between energy metabolism and apoptosis is critical, as the loss of viable cells in the intervertebral disc (IVD) can lead to a cascade of degenerative changes. Efferocytosis is a key biological process that maintains homeostasis by removing apoptotic cells, resolving inflammation, and promoting tissue repair. Therefore, enhancing mitochondrial energy metabolism and efferocytosis function in IVD cells holds great promise as a potential therapeutic approach for LDH. Methods: In this study, energy metabolism and efferocytosis-related differentially expressed genes (EMERDEGs) were identified from the transcriptomic datasets of LDH. Machine learning approaches were used to identify key genes. Functional enrichment analyses were performed to elucidate the biological roles of these genes. The functions of the hub genes were validated by RT-qPCR. The CIBERSORT algorithm was used to compare immune infiltration between LDH and Control groups. Additionally, we used single-cell RNA sequencing dataset to analyze cell-specific expression of the hub genes. Results: By using bioinformatics methods, we identified six EMERDEGs hub genes (IL6R, TNF, MAPK13, ELANE, PLAUR, ABCA1) and verified them using RT-qPCR. Functional enrichment analysis revealed that these genes were primarily associated with inflammatory response, chemokine production, and cellular energy metabolism. Further, we identified candidate drugs as potential treatments for LDH. Additionally, in immune infiltration analysis, the abundance of activated dendritic cells, neutrophils, and gamma delta T cells varied significantly between the LDH group and Control group. The scRNA-seq analysis showed that these hub genes were mainly expressed in chondrocyte-like cells. Conclusions: The identified EMERDEG hub genes and pathways offer novel insights into the molecular mechanisms underlying LDH and suggest potential therapeutic targets. Full article

Chronobiotics represent a pharmacologically diverse group of substances, encompassing both experimental compounds and those utilized in clinical practice, which possess the capacity to modulate the parameters of circadian rhythms. These substances influence fluctuations in various physiological and biochemical processes, including the expression of core “clock” genes in model organisms and cell cultures, as well as the expression of clock-controlled genes. Despite their chemical heterogeneity, chronobiotics share the common ability to alter circadian dynamics. The concept of chronobiotic drugs has been recognized for over five decades, dating back to the discovery and detailed clinical characterization of the hormone melatonin. However, the field remains fragmented, lacking a unified classification system for these pharmacological agents. The current categorizations include natural chrononutrients, synthetic targeted circadian rhythm modulators, hypnotics, and chronobiotic hormones, yet no comprehensive repository of knowledge on chronobiotics exists. Addressing this gap, the development of the world’s first curated and continuously updated database of chronobiotic drugs—circadian rhythm modulators—accessible via the global Internet, represents a critical and timely objective for the fields of chronobiology, chronomedicine, and pharmacoinformatics/bioinformatics. The primary objective of this study is to construct a relational database, ChronobioticsDB, utilizing the Django framework and PostGreSQL as the database management system. The database will be accessible through a dedicated web interface and will be filled in with data on chronobiotics extracted and manually annotated from PubMed, Google Scholar, Scopus, and Web of Science articles. Each entry in the database will comprise a detailed compound card, featuring links to primary data sources, a molecular structure image, the compound’s chemical formula in machine-readable SMILES format, and its name according to IUPAC nomenclature. To enhance the depth and accuracy of the information, the database will be synchronized with external repositories such as ChemSpider, DrugBank, Chembl, ChEBI, Engage, UniProt, and PubChem. This integration will ensure the inclusion of up-to-date and comprehensive data on each chronobiotic. Furthermore, the biological and pharmacological relevance of the database will be augmented through synchronization with additional resources, including the FDA. In cases of overlapping data, compound cards will highlight the unique properties of each chronobiotic, thereby providing a robust and multifaceted resource for researchers and practitioners in the field. Full article

The methods utilized in the drug discovery pipeline routinely combine machine learning and deep learning algorithms to enhance the outputs. The generation of a drug target, through virtual screening and computational analysis of databases used for target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. Recent technological advances in human immunology have provided improved tools that allow a better understanding of the biological and molecular mechanisms leading to the protective human immune response to pathogens, inspiring new strategies for vaccine design. Immunoinformatics approaches are more beneficial, and thus there is a demand for modern technologies such as reverse vaccinology, structural vaccinology, and system approaches in developing potential vaccine candidates. System theory, defined as a set of machine learning, control theory, and optimization-based methods applied to networked systems, provides a unifying framework for modeling and analyzing biological complexity. In this review, we explore the application of such computational methods at every stage of the therapeutic pipeline, including lead discovery, optimization, and dosing, as well as vaccine target prediction and immunogen design. Here, we summarize the system theoretic methods which provide insights into developed approaches and their applications in rational drug discovery and vaccine formulations. The approaches ranged in the review yield accurate predictions and insights. This review is intended to serve as a resource for researchers seeking to understand, adopt, or build upon system theoretic techniques in drug and vaccine development, offering both conceptual foundations and practical directions. Full article

Fabry disease is a rare genetic disorder caused by deficient activity of the lysosomal enzyme alpha-galactosidase A (AGAL), resulting in the accumulation of globotriaosylceramides (Gb3) in tissues and organs. This buildup leads to progressive, multi-systemic complications that severely impact quality of life and can be life-threatening. Interpreting the functional consequences of missense variants in the GLA gene remains a significant challenge, especially in rare diseases where experimental evidence is scarce. In this study, we present an integrative computational framework that combines structural, interaction, pathogenicity, and stability data from both in silico tools and experimental sources, enriched through expert curation and structural analysis. Given the clinical relevance of pharmacological chaperones in Fabry disease, we focus in particular on the structural characteristics of variants classified as “amenable” to such treatments. Our multidimensional analysis—using tools such as AlphaMissense, EVE, FoldX, and ChimeraX—identifies key molecular features that distinguish amenable from non-amenable variants. We find that amenable mutations tend to preserve protein stability, while non-amenable ones are associated with structural destabilisation. By comparing AlphaMissense with alternative predictors rooted in evolutionary (EVE) and thermodynamic (FoldX) models, we explore the relative contribution of different biological paradigms to variant classification. Additionally, the investigation of outlier variants—where AlphaMissense predictions diverge from clinical annotations—highlights candidates for further experimental validation. These findings demonstrate how combining structural bioinformatics with machine learning–based predictions can improve missense variant interpretation and support precision medicine in rare genetic disorders. Full article

Long-range intercellular communication is essential for multicellular biological systems to regulate multiscale cell–cell interactions and maintain life. Growing evidence suggests that intercellular calcium waves (ICWs) act as a class of long-range signals that influence a broad spectrum of cellular functions and behaviors. Importantly, mechanical signals, ranging from single-molecule-scale to tissue-scale in vivo, can initiate and modulate ICWs in addition to relatively well-appreciated biochemical and bioelectrical signals. Despite these recent conceptual and experimental advances, the full nature of underpinning mechanotransduction mechanisms by which cells convert mechanical signals into ICW dynamics remains poorly understood. This review provides a systematic analysis of quantitative ICW dynamics around three main stages: initiation, propagation, and regeneration/relay. We highlight the landscape of upstream molecules and organelles that sense and respond to mechanical stimuli, including mechanosensitive membrane proteins and cytoskeletal machinery. We clarify the roles of downstream molecular networks that mediate signal release, spread, and amplification, including adenosine triphosphate (ATP) release, purinergic receptor activation, and gap junction (GJ) communication. Furthermore, we discuss the broad pathophysiological implications of ICWs, covering pathophysiological processes such as cancer metastasis, tissue repair, and developmental patterning. Finally, we summarize recent advances in optical imaging and artificial intelligence (AI)/machine learning (ML) technologies that reveal the precise spatial-temporal-functional dynamics of ICWs and ATP waves. By synthesizing these insights, we offer a comprehensive framework of ICW mechanobiology and propose new directions for mechano-therapeutic strategies in disease diagnosis, cancer immunotherapies, and drug discovery. Full article

Ischemic stroke (IS) is the leading cause of disability and death worldwide, and its high incidence, disability and recurrence rates impose a heavy economic burden on families and society. Recent studies have shown that oxidative stress plays a key role in the pathophysiological mechanisms of ischemic stroke, not only participating in the onset and development of neuronal damage in the acute phase but also significantly influencing the long-term prognosis of ischemic stroke through molecular mechanisms, such as epigenetic modifications. However, the potential targets of oxidative stress-related genes in IS and their mechanisms of action remain to be elucidated. The aim of this study was to systematically analyse the function and significance of oxidative stress-related genes in IS. We obtained IS-related gene expression datasets from the GEO database and integrated known oxidative stress-related genes from the Genecards database for cross-analysis. Multidimensional feature screening using unsupervised consensus clustering and a series of machine learning algorithms led to the identification of the signature gene GPX7. The correlation between this gene and immune cell infiltration was assessed using MCPcounter and a potential therapeutic agent, glutathione, was identified. Binding was verified by molecular docking (MD) analysis. In addition, single-cell RNA sequencing data were analysed to further reveal expression in different cell types and its biological significance. Finally, we performed in vivo experiments using the Wistar rat middle cerebral artery occlusion (MCAO) model, and the results indicated that GPX7 plays a key role in IS, providing a new theoretical basis and potential intervention target for the precise treatment of IS. Full article

Background: The skin, a complex organ vital for protecting the body against environmental challenges, undergoes a multifaceted wound healing process involving hemostasis, inflammation, proliferation, and remodeling. The transcription factor FOSL1 has been implicated in various cellular processes crucial for wound healing, including cell cycle regulation, differentiation, and apoptosis. We hypothesize that FOSL1 is a key regulator of wound healing processes. Objective: The objective of this study was to investigate the role of FOSL1 in cutaneous wound healing, identify the core signaling pathways involved, and assess FOSL1′s potential as a therapeutic target. Method: We utilized datasets from the Gene Expression Omnibus (GEO) and applied the ‘limma’ package to discern differentially expressed genes (DEGs). We intersected these DEGs with transcription factor-associated genes from the TRRUST database. Subsequently, we constructed Protein–Protein Interaction (PPI) networks via the STRING database. Machine learning algorithms were instrumental in identifying pivotal genes, a finding corroborated through animal modeling and Western blot analysis of tissue samples. To elucidate biological pathway activities from gene expression data, we deployed the ‘PROGENy’ package, complemented by machine learning for precise pathway identification. Furthermore, Gene Set Variation Analysis (GSVA) was executed across Hallmark, biological process (BP), molecular function (MF), and cellular component (CC) categories to deepen our understanding of the wound healing process. Results: Our analysis revealed that FOSL1 is significantly upregulated in wounded skin. The Mitogen-Activated Protein Kinase (MAPK) and Epidermal Growth Factor Receptor (EGFR) pathways were identified as significantly associated with FOSL1. GSVA identifies critical changes in wound healing processes like ‘apical junction’ and ‘epithelial–mesenchymal transition.’ The upregulation of ‘cytoplasm organization’ and ‘response to gravity’ suggests roles in cellular adaptation. Molecular function analysis indicates alterations in ‘cytokeratin filaments’ and ‘growth factor binding,’ which are key for tissue repair. Cellular component shifts in ‘postsynaptic cytosol’ and ‘endoplasmic reticulum’ suggest changes in communication and protein processing. Conclusions: Our study identifies FOSL1 as a potential regulator of cutaneous wound healing through its modulation of cellular signaling pathways, offering novel insights into the molecular control of tissue repair. These findings highlight FOSL1 as a promising therapeutic target to accelerate healing in chronic or impaired wounds. Full article

The expanding field of nutraceuticals and functional food science is increasingly turning to marine-derived bioactive compounds, particularly saponins, for their diverse pharmacological properties. These so-called thalassochemicals display distinctive structural features—such as sulfated glycosidic moieties and amphiphilic backbones—that underpin potent antitumor, hypolipidemic, antioxidant, and antimicrobial activities. In contrast to their terrestrial analogs, marine saponins remain underexplored, and their complexity poses analytical and functional challenges. This review provides a critical and integrative synthesis of recent advances in the structural elucidation, biological function, and technological application of marine saponins. Special emphasis is placed on the unresolved limitations in their isolation, characterization, and structural validation, including coelution of isomers, adduct formation in MS spectra, and lack of orthogonal techniques such as NMR or FTIR. We illustrate these limitations through original MS/MS data and propose experimental workflows to improve compound purity and identification fidelity. In addition to discussing known structure–activity relationships (SARs) and mechanisms of action, we extend the scope by integrating recent developments in computational modeling, including machine learning, molecular descriptors, and quantitative structure–activity relationship (QSAR) models. These tools offer new avenues for predicting saponin bioactivity, despite current limitations in available high-quality datasets. Furthermore, we include a classification and comparison of steroidal and triterpenoid saponins from marine versus terrestrial sources, complemented by detailed chemical schematics. We also address the impact of processing techniques, delivery systems, and bioavailability enhancements using encapsulation and nanocarriers. Finally, this review contextualizes these findings within the regulatory and sustainability frameworks that shape the future of saponin commercialization. By bridging analytical chemistry, computational biology, and food technology, this work establishes a roadmap for the targeted development of marine saponins as next-generation nutraceuticals and functional food ingredients. Full article

Microplastics (MPs), emerging contaminants of significant global concern, have a substantially increased environmental impact due to their biological persistence and accumulation in the body. Exposure to MPs has been associated with oxidative stress, systemic inflammation, and cellular dysfunction, notably affecting critical tissues such as the stomach, colon, and brain. This review explores the correlation between MPs and cancer risk along the gastric–colon–brain axis, identifying the signaling pathways altered by MP exposure. Furthermore, it highlights the role of functional nutrition and bioactive flavonoids—including chlorogenic acid, coumaric acid, and naringin—as well as the use of highly bioavailable combined polyphenol nanoparticles as potential detoxifying agents. Functional nutrients are effective in enhancing cellular resilience against reactive oxygen species (ROS) production and MP-induced toxicity, offering protective effects at the gastric, intestinal, and brain barriers. Activation of the Nrf2 pathway by bioactive compounds promotes the expression of detoxifying enzymes, suggesting a promising nutritional strategy to mitigate MP-related damage. This review underscores how functional nutrition may represent a viable therapeutic approach to reduce the harmful effects of MP exposure. The integration of advanced technologies—such as microfluidic systems, organ-on-a-chip platforms, and machine learning—and the identification of key molecular targets lay the foundation for developing preventive and personalized medicine strategies aimed at lowering the risk of environmentally induced carcinogenesis. Full article