Search Results (39)

Artificial intelligence (AI) has become a transformative tool in the field of molecular biosensing, enabling data-driven optimization in sensor design, signal processing, and real-time monitoring. AI promotes the discovery of biomarkers, the design of high-affinity receptors, and the rational engineering of sensing materials, thereby enhancing sensitivity, specificity, and detection accuracy. In the development of biosensors, AI-assisted strategies have accelerated the identification of novel molecular targets, guided the design of proteins and aptamers with enhanced binding performance, and optimized plasmonic and nanophotonic structures through forward prediction and inverse design frameworks. The integration of artificial intelligence has significantly enhanced the performance of various biosensing platforms, including optical, electrochemical, and microfluidic biosensors. It also enabled automatic feature extraction, noise reduction, dimensionality reduction, and multimodal data fusion, overcoming the challenges posed by complex signals, environmental interference, and device variations. These capabilities are particularly crucial for wearable molecular biosensors, as low signal strength, motion artifacts, and fluctuations in physiological conditions impose strict requirements on robustness and real-time reliability. This review systematically summarizes the latest advancements in AI-assisted molecular biosensors, highlighting representative sensing strategies and algorithms for wearable and real-time monitoring, and discusses the current challenges and future development opportunities of intelligent biosensing technologies. Full article

Human diseases are complex and arise from the coordinated action of multiple genes and their protein products. Genes’ behaviors extend beyond genetic variants, mutations, and differential expressions. Their coordinated activity across biological scales (molecules, cells, tissues, organs) produces emergent behaviors that shape health and disease. These emergent behaviors span time and space and are often hard to measure directly from observation when using standard experimental measurements. Yet these “hidden” or latent gene characteristics can be powerful drivers of disease. We propose a Mini-Galaxy Model (MGM), a systems-level AI-driven network framework that models cells as “mini-galaxies” composed of multilayered biological information, with each layer encoding a different dimension of genes’ behavior. Here, we delineate a strategy on how to construct and compare MGMs across health and disease and map their etiological relatedness. We also operationalize the MGM as a discovery platform for translational medicine, offering modules to allow target prioritization and editing. By reframing human diseases as the result of emergent behavior of multilayered multimode biological networks and their perturbations, the MGM yields actionable rules to streamline biomarker discovery, guide target selection and enable rational design of combinatorial interventions, and accelerate drug repurposing. Full article

Allosteric modulation has emerged as a powerful strategy for achieving superior selectivity and safety in drug discovery and protein function regulation. Unlike highly conserved orthosteric sites, allosteric pockets are structurally diverse and less evolutionarily constrained, making them particularly suitable for modulation by designed miniproteins. Miniproteins can provide extended binding interfaces and high affinity for shallow, dynamic, or cryptic regulatory surfaces that are often inaccessible to small molecules. Recent advances in artificial intelligence (AI) are transforming this field through deep learning-based structure prediction and generative modeling. These AI-driven approaches enable the identification of allosteric hotspots, characterization of conformational ensembles, and de novo design of structured miniprotein binders. They are rapidly expanding the landscape for designing selective modulators across diverse allosteric targets, including GPCRs, receptor tyrosine kinases, nuclear receptors, ion channels, and other protein–protein interaction systems. This review summarizes state-of-the-art AI-driven computational methodologies for designing miniproteins as potential allosteric modulators and discusses their current challenges and future opportunities in allosteric drug discovery. Full article

Antimicrobial resistance (AMR) poses an escalating global health crisis driven by multidrug-resistant ESKAPE pathogens and emerging fungal threats such as Candida auris (C. auris). In response to this urgent need for new therapeutic strategies, antimicrobial peptides (AMPs) represent a mechanistically distinct alternative to conventional antibiotics due to their membrane-targeting mechanisms and a reduced propensity for resistance development; however, clinical translation has been hindered by toxicity, instability and manufacturing constraints. Recent advances in artificial intelligence (AI) are reshaping AMP discovery and optimization. Machine learning (ML), deep learning (DL) and transformer-based protein language models now enable improved prediction of antimicrobial activity, selectivity, protease stability and host toxicity. Generative approaches, including variational autoencoders, diffusion models and reinforcement learning, facilitate de novo multi-objective peptide design and pathogen-directed optimization against resistant bacteria and multidrug-resistant fungal pathogens. Integrated design–test–learn pipelines are accelerating iterative peptide engineering by tightly coupling computational prediction with experimental validation. Clinically used peptide-derived antibiotics such as polymyxins and daptomycin demonstrate the therapeutic feasibility of peptide-based antimicrobials, while investigational peptides, including pexiganan, illustrate ongoing translational progress. Although no fully AI-designed AMP has yet achieved regulatory approval, the accelerating convergence of computational modeling and experimental validation suggests a rapidly evolving translational landscape. Advancing scalable, surveillance-informed AI frameworks that integrate resistance data, predictive safety modeling and delivery optimization will be essential to accelerate the clinical translation of next-generation, multi-objective AMPs against high-risk resistant pathogens. Full article

Conventional food security strategies have largely been formulated under assumptions of population growth, abundant agricultural labor, and stable global trade. However, many advanced economies—particularly in East Asia—are entering a shrinking-society context characterized by population decline, rapid aging, and regional depopulation. This paper argues that demographic shrinkage should be understood not as a peripheral trend but as a landscape-level structural pressure that destabilizes incumbent agri-food systems. Drawing on the Multi-Level Perspective (MLP), the study conceptualizes demographic shrinkage as a cumulative force that erodes the labor base, productive capacity, and institutional stability of food systems, thereby weakening regime path dependence. Building on this framework, it advances Food Security 3.0 as a theory-driven contribution to sustainability research. Food Security 3.0 reconceptualizes food security under shrinkage conditions as a problem of systemic resilience rather than production expansion or import diversification, and theorizes food technology—including smart and automated agriculture, alternative proteins, and AI-enabled supply chains—as transitional infrastructure enabling regime reconfiguration under structural constraints. By integrating demographic change, socio-technical transitions, and governance, the study reframes food security as a question of resilience-oriented system design, strategic self-reliance, and integrated food-system governance. While anchored in the East Asian experience, the framework offers theoretical and policy-relevant insights for shrinking societies confronting overlapping demographic, climatic, and geopolitical pressures. Full article

Background: The growing recognition of shared molecular pathways and molecular signatures between cardiovascular diseases and cancer has motivated interest in exploring antihypertensive-associated chemical space for oncological applications. Concurrently, artificial intelligence (AI)-driven molecular generation has enabled the rapid creation of virtual lead candidates for specific therapeutic indications, although their broader biological interaction profiles often remain unexplored. Methods: In this paper, we explore the computational screening of a library of AI-generated antihypertensive virtual lead compounds to evaluate their polypharmacological anticancer potential. The compounds were originally designed and prioritized for modulating $\mathsf{\beta }$-adrenergic receptors but are here re-evaluated in a cancer-focused context using a multi-stage in silico approach. We chose five (5) known cancer target proteins and performed compound profiling for drug-likeness, pharmacokinetic suitability, and safety. Docking simulations, binding free energy estimates, molecular interaction mapping, and pharmacophore modeling were used to evaluate the molecules’ interactions with the cancer-linked protein targets. We employed the binding free energy estimates of the ligand–protein complexes to determine compounds with polypharmacological anticancer potential. In addition, molecular dynamics simulations of some of the compounds with polypharmacological anticancer potential were employed to evaluate binding stability and dynamic behavior of selected ligand–target complexes. Results: Several compounds showed good docking scores, physicochemical characteristics, and pharmacokinetic profiles. Also, the results reveal that several AI-generated antihypertensive virtual leads exhibit favorable multi-target binding profiles, with consistent docking affinities and stable interaction networks across multiple cancer-related targets. Conclusions: Our findings suggest that several of the hypothetically evaluated compounds exhibit favorable physicochemical properties, acceptable predicted pharmacokinetic and safety profiles, and consistent predicted binding affinities across multiple cancer-relevant targets. Full article

The onset of proteolysis targeting chimeras (PROTACs) has reshaped the entire context of targeted cancer therapy by offering a novel approach for the selective degradation of disease-causing proteins, overcoming the limitations of traditional occupancy-driven inhibition. This heterobifunctional technology recruits endogenous E3 ubiquitin ligases to mark proteins of interest (POI) for proteosomal degradation via the ubiquitin-proteasome system (UPS). Unlike conventional inhibitors, PROTACs function catalytically and can target previously “undruggable proteins”, such as transcription factors, scaffold proteins, and non-enzymatic regulators, offering potential to overcome acquired resistance and achieve potent efficacy at sub-stoichiometric doses. The review explores the latest innovations in PROTAC design, including E3 ligase selection, linker chemistry, and ligand optimization, while highlighting promising preclinical and clinical candidates against oncogenic drivers, anti-apoptotic factors (BCL-xL), and nuclear hormone receptors. Furthermore, we critically examine key translational challenges, such as pharmacokinetics, off-target effects, and resistance mechanisms, and discuss viable solutions, including dual E3 ligase engagement, novel modalities like AUTACs/ATTECs, LYTACs, and AI-driven design. As the field rapidly evolves from foundational to clinical application, PROTACs are redefining therapeutic possibilities, offering a robust, flexible, and scalable framework for the future of precision oncology. Full article

Background/Objectives: Contemporary food systems face dual imperatives of ensuring nutritional adequacy while minimizing environmental resource consumption, yet conventional dietary assessment methodologies inadequately integrate these competing objectives. This simulation-based proof-of-concept study developed an artificial intelligence-driven computational framework synthesizing nutritional evaluation, environmental footprint quantification, and economic accessibility assessment. Methods: The analytical architecture integrated random forest classification, dimensionality reduction, and scenario-based optimization across a simulated population cohort of 1500 individuals. Food composition data encompassed 55 representative foods across eight categories linked with greenhouse gas emissions, water use, and price parameters. Four dietary patterns (Mediterranean, Western, Plant-based, Mixed) were characterized across nutrient adequacy, greenhouse gas emissions, water consumption, and economic cost. Results: Random forest classification achieved 39.1% accuracy, with cost, greenhouse gas emissions, and water consumption emerging as the most discriminating features. Dietary patterns exhibited convergent macronutrient profiles (protein 108.8–112.8 g per day, 4% variation) despite categorical distinctions, while calcium inadequacy pervaded all patterns (867–927.5 mg per day, 7–13% below requirements). Environmental footprints demonstrated limited differentiation (greenhouse gas 3.73–3.96 kg CO₂e per day, 6% range). Bootstrap resampling (n = 1000) confirmed narrow confidence intervals, with NHANES validation revealing substantial energy intake deviations (38–58% above observed means) attributable to adequacy-prioritized design rather than observed consumption patterns. Scenario modeling identified seasonally flexible dietary configurations maintaining micronutrient and protein adequacy while reducing water use to 87% of baseline at modest cost increases. Conclusions: This framework establishes a validated computational infrastructure for integrated dietary assessment benchmarked against sustainability thresholds and epidemiological reference data, demonstrating the feasibility of AI-driven evaluation of dietary patterns across nutritional, environmental, and economic dimensions. Full article

Scaffold-aware artificial intelligence (AI) models enable systematic exploration of chemical space conditioned on protein-interacting ligands, yet the representational principles governing their behavior remain poorly understood. The computational representation of structurally complex kinase small molecules remains a formidable challenge due to the high conservation of ATP active site architecture across the kinome and the topological complexity of structural scaffolds in current generative AI frameworks. In this study, we present a diagnostic, modular and chemistry-first generative framework for design of targeted SRC kinase ligands by integrating ChemVAE-based latent space modeling, a chemically interpretable structural similarity metric (Kinase Likelihood Score), Bayesian optimization, and cluster-guided local neighborhood sampling. Using a comprehensive dataset of protein kinase ligands, we examine scaffold topology, latent-space geometry, and model-driven generative trajectories. We show that chemically distinct scaffolds can converge toward overlapping latent representations, revealing intrinsic degeneracy in scaffold encoding, while specific topological motifs function as organizing anchors that constrain generative diversification. The results demonstrate that kinase scaffolds spanning 37 protein kinase families spontaneously organize into a coherent, low-dimensional manifold in latent space, with SRC-like scaffolds acting as a structural “hub” that enables rational scaffold transformation. Our local sampling approach successfully converts scaffolds from other kinase families (notably LCK) into novel SRC-like chemotypes, with LCK-derived molecules accounting for ~40% of high-similarity outputs. However, both generative strategies reveal a critical limitation: SMILES-based representations systematically fail to recover multi-ring aromatic systems—a topological hallmark of kinase chemotypes—despite ring count being a top feature in our structural similarity metric. This “representation gap” demonstrates that no amount of scoring refinement can compensate for a generative engine that cannot access topologically constrained regions. By diagnosing these constraints within a transparent pipeline and reframing scaffold-aware ligand design as a problem of molecular representation our work provides a conceptual framework for interpreting generative model behavior and for guiding the incorporation of structural priors into future molecular AI architectures. Full article

Dual-target drug design addresses complex diseases and drug resistance, yet existing computational approaches struggle with simultaneous multi-protein optimization. This study presents SFG-Drug, a novel dual-target molecular generation model combining Monte Carlo tree search with gated recurrent unit neural networks for simultaneous MEK1 and mTOR targeting. The methodology employed DigFrag digital fragmentation on ZINC-250k dataset, integrated low-frequency masking techniques for enhanced diversity, and utilized molecular docking scores as reward functions. Comprehensive evaluation on MOSES benchmark demonstrated superior performance compared to state-of-the-art methods, achieving perfect validity (1.000), uniqueness (1.000), and novelty (1.000) scores with highest internal diversity indices (0.878 for IntDiv1, 0.860 for IntDiv2). Over 90% of generated molecules exhibited favorable binding affinity with both targets, showing optimal drug-like properties including QED values in [0.2, 0.7] range and high synthetic accessibility scores. Generated compounds demonstrated structural novelty with Tanimoto coefficients below 0.25 compared to known inhibitors while maintaining dual-target binding capability. The SFG-Drug model successfully bridges the gap between computational prediction and practical drug discovery, offering significant potential for developing new dual-target therapeutic agents and advancing AI-driven pharmaceutical research methodologies. Full article

Bt Cry toxins remain the cornerstone of transgenic crop protection against Lepidopteran pests, yet field-evolved resistance, particularly in invasive species such as Spodoptera frugiperda and Helicoverpa armigera, can threaten their long-term efficacy. This review presents a comprehensive and unified mechanistic framework that synthesizes current understanding of Bt Cry toxin modes of action and the complex, multilayered regulatory mechanisms of field-evolved resistance. Beyond the classical pore-formation model, emerging evidence highlights signal transduction cascades, immune evasion via suppression of Toll/IMD pathways, and tripartite toxin–host–microbiota interactions that can dynamically modulate protoxin activation and receptor accessibility. Resistance arises from target-site alterations (e.g., ABCC2/ABCC3, Cadherin mutations), altered midgut protease profiles, enhanced immune regeneration, and microbiota-mediated detoxification, orchestrated by transcription factor networks (GATA, FoxA, FTZ-F1), constitutive MAPK hyperactivation (especially MAP4K4-driven cascades), along with preliminary emerging findings on non-coding RNA involvement. Countermeasures now integrate synergistic Cry/Vip pyramiding, CRISPR/Cas9-validated receptor knockouts revealing functional redundancy, Domain III chimerization (e.g., Cry1A.105), phage-assisted continuous evolution (PACE), and the emerging application of AlphaFold3 for structure-guided rational redesign of resistance-breaking variants. Future sustainability hinges on system-level integration of single-cell transcriptomics, midgut-specific CRISPR screens, microbiome engineering, and AI-accelerated protein design to preempt resistance trajectories and secure Bt biotechnology within integrated resistance and pest management frameworks. Full article

This review examines the application of Artificial Intelligence (AI) in the discovery and optimisation of neuroprotective natural products (NPs) for neurodegenerative diseases (NDDs), emphasising the transition from general computational drug discovery to AI-specific approaches designed to address the chemical complexity and bioactivity profiles of natural compounds. The discussion encompasses relevant datasets, AI models, illustrative case studies, and emerging protein and biological targets that may serve as potential points of intervention for the prevention and treatment of NDDs. The review is organised to guide the reader from foundational knowledge to applied strategies; it begins by outlining the chemical and biological principles underlying neuroprotective NPs, then presents AI-driven computational frameworks for NP discovery, followed by a detailed examination of recent case studies in NDDs. Subsequent sections address the key challenges, opportunities, and future directions in the field, concluding with an evaluation of prospects for interdisciplinary collaboration across medicinal chemistry, neuroscience, and artificial intelligence. Full article

Enzyme engineering drives innovation in biotechnology, medicine, and industry, yet conventional approaches remain limited by labour-intensive workflows, high costs, and narrow sequence diversity. Artificial intelligence (AI) is revolutionising this field by enabling rapid, precise, and data-driven enzyme design. Machine learning and deep learning models such as AlphaFold2, RoseTTAFold, ProGen, and ESM-2 accurately predict enzyme structure, stability, and catalytic function, facilitating rational mutagenesis and optimisation. Generative models, including ProteinGAN and variational autoencoders, enable de novo sequence creation with customised activity, while reinforcement learning enhances mutation selection and functional prediction. Hybrid AI–experimental workflows combine predictive modelling with high-throughput screening, accelerating discovery and reducing experimental demand. These strategies have led to the development of synthetic “synzymes” capable of catalysing non-natural reactions, broadening applications in pharmaceuticals, biofuels, and environmental remediation. The integration of AI-based retrosynthesis and pathway modelling further advances metabolic and process optimisation. Together, these innovations signify a shift from empirical, trial-and-error methods to predictive, computationally guided design. The novelty of this work lies in presenting a unified synthesis of emerging AI methodologies that collectively define the next generation of enzyme engineering, enabling the creation of sustainable, efficient, and functionally versatile biocatalysts. Full article

Current food systems are highly complex, with interdependencies across regions, resources, and actors, and conventional food production is a major contributor to climate change. Transitioning to sustainable protein sources is therefore critical to meet the nutritional needs of a growing global population while reducing environmental pressures. Filamentous fungi present a promising solution by converting agro-industrial side streams into mycoproteins—nutrient-dense, sustainable proteins with a carbon footprint more than ten times lower than beef. This review evaluates the potential of mycoproteins derived from fungi cultivated on low-cost substrates, focusing on their role in advancing sustainable food systems. Evidence indicates that mycoproteins are rich in protein (13.6–71% dw), complete amino acids, fiber (4.8–25% dw), essential minerals, polyphenols, and vitamins while maintaining low fat and moderate carbohydrate content. Fermentation efficiency and product quality depend on substrate type, nutrient availability, and fungal strain, with advances in bioreactor design and AI-driven optimization enhancing scalability and traceability. Supported by emerging regulatory frameworks, mycoproteins can reduce reliance on animal-derived proteins, valorize agricultural by-products, and contribute to climate-resilient, nutritionally rich diets. Integration into innovative food products offers opportunities to meet consumer preferences while promoting environmentally sustainable, socially equitable, and economically viable food systems within planetary boundaries. Full article

Proteolysis Targeting Chimeras (PROTACs) represent a transformative modality in drug discovery, enabling the selective degradation of disease-relevant proteins through the ubiquitin proteasome system. Despite their therapeutic promise, the rational design of PROTACs remains a complex and resource-intensive process, involving multiple parameters such as target and ligase compatibility, ternary complex formation, linker optimization, and degradation efficiency. Recent advances in artificial intelligence (AI) have provided new strategies to address these obstacles, ranging from structure-based modeling of ternary complexes to degradability prediction, generative linker design, and pharmacokinetic property estimation. This review aims to explore how AI can be leveraged directly or indirectly in the PROTAC development pipeline. First, we analyze existing applications of AI, such as ternary complex structure prediction, degradability prediction, linker design, and ADME prediction. We further discuss how other approaches from the related fields may be adapted to address the challenges of PROTAC discovery. Lastly, we discuss challenges that current AI models face, such as limited data, poor interpretability, and low generalizability. Taken together, overcoming these barriers will enable AI-driven strategies to accelerate PROTAC discovery and provide a more rational framework for targeted protein degrader development. Full article