Search Results (1,077)

Circular RNAs (circRNAs) have emerged as a transformative class of RNA therapeutics, distinguished by their closed-loop structure conferring nuclease resistance, reduced immunogenicity, and sustained translational activity. While challenges in pharmacokinetic control and manufacturing standardization require resolution, emerging synergies between computational design tools and modular delivery platforms are accelerating clinical translation. In this review, we synthesize recent advances in circRNA therapeutics, with a focused analysis of their stability and immunogenic properties in vaccine and drug development. Notably, key synthesis strategies, delivery platforms, and AI-driven optimization methods enabling scalable production are discussed. Moreover, we summarize preclinical and emerging clinical studies that underscore the potential of circRNA in vaccine development and protein replacement therapies. As both a promising expression vehicle and programmable regulatory molecule, circRNA represents a versatile platform poised to advance next-generation biologics and precision medicine. Full article

Antimicrobial peptides (AMPs) provide a robust alternative to conventional antibiotics, combating escalating microbial resistance through their diverse functions and broad pathogen-targeting abilities. While current deep learning technologies enhance AMP generation, they face challenges in developing multifunctional AMPs due to intricate amino acid interdependencies and limited consideration of diverse functional activities. To overcome this challenge, we introduce a novel de novo multifunctional AMP design framework that enhances a Feedback Generative Adversarial Network (FBGAN) by integrating a global quantitative AMP activity regression module and a multifunctional-attribute integrated prediction module. This integrated approach not only facilitates the automated generation of potential AMP candidates, but also optimizes the network’s ability to assess their multifunctionality. Initially, by integrating an effective pre-trained regression and classification model with feedback-loop mechanisms, our model can not only identify potential valid AMP candidates, but also optimizes computational predictions of Minimum Inhibitory Concentration (MIC) values. Subsequently, we employ a combinatorial predictor to simultaneously identify and predict five multifunctional AMP bioactivities, enabling the generation of multifunctional AMPs. The experimental results demonstrate the efficiency of generating AMPs with multiple enhanced antimicrobial properties, indicating that our work can provide a valuable reference for combating multi-drug-resistant infections. Full article

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: Insomnia and excessive sleepiness are significant health problems with a complex etiology, increasingly affecting young people, especially students. This study aimed to assess the prevalence of sleep disturbances and patterns of psychoactive drug use among female Polish students. We also explored the potential impact of the COVID-19 pandemic on sleep behaviors. We hypothesized that sleep disorders are common in this group, that medical students are more likely to experience insomnia and excessive sleepiness, and that the pandemic has exacerbated both sleep disturbances and substance use. Methods: This cross-sectional study utilized a custom survey designed using standardized questionnaires—the Athens Insomnia Scale and Epworth Sleepiness Scale—that was distributed online using the Computer-Assisted Web Interviewing method. A total of 11,988 responses were collected from 31 January 2016 to 1 January 2021. Inclusion criteria were being female, having a college student status, and giving informed consent. Results: Among the 11,988 participants, alcohol use declined after the pandemic began (p = 0.001), while sedative use increased (p < 0.001). Insomnia (AIS) was associated with study year, university profile, and field of study (p < 0.001), with the highest rates in first-year and non-medical students. It was more common among users of sedatives, psychostimulants, and multiple substances. No significant change in insomnia was found before and after the pandemic. Excessive sleepiness (ESS) peaked in first-year and medical students. It decreased during the pandemic (p < 0.001) and was linked to the use of alcohol, psychostimulants, cannabinoids, and multiple substances. Conclusions: These findings highlight that female students are particularly vulnerable to sleep disorders. The influence of the COVID-19 pandemic on sleep disturbances remains inconclusive. Given the varied results in the existing literature, further research is needed. Full article

Resistance of various bacterial pathogens to the activity of clinically approved drugs currently leads to serious infections, rapid spread of difficult-to-treat diseases, and even death. Taking the threats for human health in mind, researchers are focused on the isolation and characterization of novel natural products, including plant secondary metabolites. These molecules serve as inspiration and a suitable structural platform in the design and development of novel semi-synthetic and synthetic derivatives. All considered compounds have to be adequately evaluated in silico, in vitro, and in vivo using relevant approaches. The current review paper briefly focuses on the chemical and metabolic properties of resveratrol (1), as well as its oligomeric structures, viniferins, and viniferin-based molecules. The core scaffolds of these compounds contain so-called privileged structures, which are also present in many clinically approved drugs, indicating that those natural, properly substituted semi-synthetic, and synthetic molecules can provide a notably broad spectrum of beneficial pharmacological activities, including very impressive antimicrobial efficiency. Except for spectral verification of their structures, these compounds suffer from the determination or prediction of other structural and physicochemical characteristics. Therefore, the structure–activity relationships for specific dihydrodimeric and dimeric viniferins, their bioisosteres, and derivatives with notable efficacy in vitro, especially against chosen Gram-positive bacterial strains, are summarized. In addition, a set of descriptors related to their structural, physicochemical, pharmacokinetic, and toxicological properties is generated using various computational tools. The obtained values are compared to those of clinically approved drugs. The particular relationships between these in silico parameters are also explored. Full article

Background/Objectives: Decision-analytic Bayesian approaches are ideally suited for designing clinical trials. They have been used increasingly over the last 30 years in developing medical devices and drugs. A prototype trial is a bandit problem in which treating participants is as important as treating patients in clinical practice after the trial. Methods: This article chronicles the use of the Bayesian approach in clinical trials motivated by bandit problems. It provides a comprehensive historical and practical review of Bayesian adaptive trials, with a focus on bandit-inspired designs. Results: The 20th century saw advances in Bayesian methodology involving computer simulation. In the 21st century, methods motivated by bandit problems have been applied in designing scores of actual clinical trials. Fifteen such trials are described. By far the most important Bayesian contributions in clinical trials are the abilities to observe the accumulating results and to modify the future course of the trial on the basis of these observations. In the spirit of artificial intelligence, algorithms are programmed to learn the optimal treatment assignments over the remainder of the trial. Conclusions: Bayesian trials are still nascent and represent a small minority of clinical trials, but their existence is changing the way investigators, regulators, and government and industry sponsors view innovation in clinical trials. Full article

Polypharmacology, the rational design of small molecules that act on multiple therapeutic targets, offers a transformative approach to overcome biological redundancy, network compensation, and drug resistance. This review outlines the scientific rationale for polypharmacology, highlighting its success across oncology, neurodegeneration, metabolic disorders, and infectious diseases. Emphasis is placed on how polypharmacological agents can synergize therapeutic effects, reduce adverse events, and improve patient compliance compared to combination therapies. We also explore how computational methods—spanning ligand-based modeling, structure-based docking, network pharmacology, and systems biology—enable target selection and multi-target ligand prediction. Recent advances in artificial intelligence (AI), particularly deep learning, reinforcement learning, and generative models, have further accelerated the discovery and optimization of multi-target agents. These AI-driven platforms are capable of de novo design of dual and multi-target compounds, some of which have demonstrated biological efficacy in vitro. Finally, we discuss the integration of omics data, CRISPR functional screens, and pathway simulations in guiding multi-target design, as well as the challenges and limitations of current AI approaches. Looking ahead, AI-enabled polypharmacology is poised to become a cornerstone of next-generation drug discovery, with potential to deliver more effective therapies tailored to the complexity of human disease. Full article

Developing a new drug costs approximately one to three billion dollars and takes around ten years; however, this process has only a ten percent success rate. To address this issue, new technologies that combine artificial intelligence (AI) and quantum computing can be leveraged in the pharmaceutical industry. The RSA cryptographic algorithm, developed by Rivest, Shamir, and Adleman in 1977, is one of the most widely used public-key encryption schemes in modern digital security. Its security foundation lies in the computational difficulty of factoring the product of two large prime numbers, a problem considered intractable for classical computers when the key size is sufficiently large (e.g., 2048 bits or more). A future application of using a detailed structural model of a protein is that digital drug design can be used to predict potential drug candidates, thereby reducing or eliminating the need for time-consuming laboratory and animal testing. Knowing the molecular structure of a possible candidate drug can provide insights into how drugs interact with targets at an atomic level, at significantly lower expenditures, and with maximum effectiveness. AI and quantum computers can rapidly screen out potential new drug candidates, determine the toxicity level of a known drug, and eliminate drugs with high toxicity at the beginning of the drug development phase, thereby avoiding expensive laboratory and animal testing. The Food and Drug Administration (FDA) and other regulatory bodies are increasingly supporting the use of in silico to in vitro/in vivo validation methods and assessments of drug safety and efficacy. Full article

This review provides a comprehensive analysis of the transformative impact of artificial intelligence (AI) and machine learning (ML) on modern drug design, specifically focusing on how these advanced computational techniques address the inherent limitations of traditional small-molecule drug design methodologies. It begins by outlining the historical challenges of the drug discovery pipeline, including protracted timelines, exorbitant costs, and high clinical failure rates. Subsequently, it examines the core principles of structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS), establishing the critical bottlenecks that have historically impeded efficient drug development. The central sections elucidate how cutting-edge ML and deep learning (DL) paradigms, such as generative models and reinforcement learning, are revolutionizing chemical space exploration, enhancing binding affinity prediction, improving protein flexibility modeling, and automating critical design tasks. Illustrative real-world case studies demonstrating quantifiable accelerations in discovery timelines and improved success probabilities are presented. Finally, the review critically examines prevailing challenges, including data quality, model interpretability, ethical considerations, and evolving regulatory landscapes, while offering forward-looking critical perspectives on the future trajectory of AI-driven pharmaceutical innovation. Full article

Epilepsy remains a widespread neurological disorder, with approximately 30% of patients showing resistance to current antiepileptic therapies. To address this unmet need, a series of 2-(isoindolin-2-yl) esters derived from natural amino acids were designed and evaluated for their potential interaction with the GABA_A receptor. Sixteen derivatives were subjected to in silico assessments, including physicochemical and ADMET profiling, virtual screening–ensemble docking, and enhanced sampling molecular dynamics simulations (metadynamics calculations). Among these, compounds derived from the aromatic amino acids, phenylalanine, tyrosine, tryptophan, and histidine, exhibited superior predicted affinity, attributed to π–π stacking interactions at the benzodiazepine binding site of the GABA_A receptor. Based on computational performance, the tyrosine and tryptophan derivatives were synthesized and further assessed in vivo using the pentylenetetrazole-induced seizure model in zebrafish (Danio rerio). The tryptophan derivative produced comparable behavioral seizure reduction to the reference drug diazepam at the tested concentrations. The results implies that aromatic amino acid-derived isoindoline esters are promising anticonvulsant candidates and support the hypothesis that π–π interactions may play a critical role in modulating GABA_A receptor binding affinity. Full article

Background/Objectives: Substance use disorders, particularly opioid addiction, continue to pose a major global health and toxicological challenge. Morphine dependence represents a significant problem in both clinical practice and preclinical research, particularly in modeling the pharmacodynamics of withdrawal. Rodent models remain indispensable for investigating the neurotoxicological effects of chronic opioid exposure and withdrawal. However, conventional behavioral assessments rely on manual observation, limiting objectivity, reproducibility, and scalability—critical constraints in modern drug toxicity evaluation. This study introduces MWB_Analyzer, an automated and high-throughput system designed to quantitatively and objectively assess morphine withdrawal behaviors in rats. The goal is to enhance toxicological assessments of CNS-active substances through robust, scalable behavioral phenotyping. Methods: MWB_Analyzer integrates optimized multi-angle video capture, real-time signal processing, and machine learning-driven behavioral classification. An improved YOLO-based architecture was developed for the accurate detection and categorization of withdrawal-associated behaviors in video frames, while a parallel pipeline processed audio signals. The system incorporates behavior-specific duration thresholds to isolate pharmacologically and toxicologically relevant behavioral events. Experimental animals were assigned to high-dose, low-dose, and control groups. Withdrawal was induced and monitored under standardized toxicological protocols. Results: MWB_Analyzer achieved over 95% reduction in redundant frame processing, markedly improving computational efficiency. It demonstrated high classification accuracy: >94% for video-based behaviors (93% on edge devices) and >92% for audio-based events. The use of behavioral thresholds enabled sensitive differentiation between dosage groups, revealing clear dose–response relationships and supporting its application in neuropharmacological and neurotoxicological profiling. Conclusions: MWB_Analyzer offers a robust, reproducible, and objective platform for the automated evaluation of opioid withdrawal syndromes in rodent models. It enhances throughput, precision, and standardization in addiction research. Importantly, this tool supports toxicological investigations of CNS drug effects, preclinical pharmacokinetic and pharmacodynamic evaluations, drug safety profiling, and regulatory assessment of novel opioid and CNS-active therapeutics. 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

This study presents the synthesis, physicochemical characterization, and biological evaluation of a novel ternary nickel(II) complex with isoleucine and 1,10-phenanthroline ligands, [Ni(Phen)(Ile)₂]∙6H₂O, designed as a potential antitumor agent. Single-crystal X-ray diffraction revealed a monoclinic structure (C2-space group) with an octahedral Ni(II) coordination involving Phen and Ile ligands. A Hirshfeld surface analysis highlighted intermolecular interactions stabilizing the crystal lattice, with hydrogen bonds (H···H and O···H/H···O) dominating (99.1% of contacts). Density functional theory (DFT) calculations, including solvation effects (in water and methanol), demonstrated strong agreement with the experimental geometric parameters and revealed higher affinity to the water solvent. The electronic properties of the complex, such as HOMO−LUMO gaps (3.20–4.26 eV) and electrophilicity (4.54–5.88 eV), indicated a charge-transfer potential suitable for biological applications through interactions with biomolecules. Raman and infrared spectroscopic studies showed vibrational modes associated with Ni–N/O bonds and ligand-specific deformations, with solvation-induced shifts observed. A study using ultraviolet–visible–near-infrared absorption spectroscopy demonstrated that the complex remains stable in solution. In vitro cytotoxicity assays against MCF-7 (breast adenocarcinoma) and HCT-116 (colorectal carcinoma) cells showed dose-dependent activity, achieving 47.6% and 65.3% viability reduction at 100 μM (48 h), respectively, with lower toxicity to non-tumor lung fibroblasts (GM07492A, 39.8%). Supporting the experimental data, we performed computational modeling to examine the pharmacokinetic profile, with particular focus on the absorption, distribution, metabolism, and excretion properties and drug-likeness potential. Full article

The global burden of respiratory viral infections is notable, which is attributed to their higher transmissibility compared to other viral diseases. Respiratory viruses are seen to have evolved resistance to available treatment options. Although vaccines and antiviral drugs control some respiratory viruses, this control is limited due to unexpected events, such as mutations and the development of antiviral resistance. The technology of proteolysis-targeting chimeras (PROTACs) has been emerging as a novel technology in viral therapeutics. These are small molecules that can selectively degrade target proteins via the ubiquitin–proteasome pathway. PROTACs as a therapy were initially developed against cancer, but they have recently shown promising results in their antiviral mechanisms by targeting viral and/or host proteins involved in the pathogenesis of viral infections. In this review, we elaborate on the antiviral potential of PROTACs as therapeutic agents and their potential as vaccine components against important respiratory viral pathogens, including influenza viruses, coronaviruses (SARS-CoV-2), and respiratory syncytial virus. Advanced applications of PROTAC antiviral strategies, such as hemagglutinin and neuraminidase degraders for influenza and spike proteins of SARS-CoV-2, are detailed in this review. Additionally, the role of PROTACs in targeting cellular mechanisms within the host, thereby preventing viral pathogenesis and eliciting an antiviral effect, is discussed. The potential of PROTACs as vaccines, utilizing proteasome-based virus attenuation to achieve a robust protective immune response, while ensuring safety and enhancing efficient production, is also presented. With the promises exhibited by PROTACs, this technology faces significant challenges, including the emergence of novel viral strains, tissue-specific expression of E3 ligases, and pharmacokinetic constraints. With advanced computational design in molecular platforms, PROTAC-based antiviral development offers an alternative, transformative path in tackling respiratory viruses. Full article

Facial reconstruction presents complex challenges due to the intricate nature of craniofacial anatomy and the necessity for individualized treatment. Conventional reconstructive methods—such as autologous bone grafts and prefabricated alloplastic implants—pose limitations, including donor site morbidity, implant rejection, and suboptimal aesthetic results. The emergence of 3D printing technology has introduced patient-specific implants (PSIs) that enhance anatomical fit, functional restoration, and biocompatibility. This review outlines the evolution of 3D-printed implants, key materials, computer-assisted design (CAD), and their applications across trauma, oncology, congenital conditions, and aesthetics. It also addresses current challenges and explores future directions, such as bioprinting, smart implants, and drug-eluting coatings. Full article