The SARS-CoV-2 outbreak highlights the persistent vulnerability of humanity to epidemics and emerging microbial threats, emphasizing the lack of time to develop disease-specific treatments. Therefore, it appears beneficial to utilize existing resources and therapies. Computational drug repositioning is an effective strategy that redirects authorized drugs to new therapeutic purposes. This strategy holds significant promise for newly emerging diseases, as drug discovery is a lengthy and expensive process. Through this study, we present an ensemble method based on the convolutional neural network integrated with genetic algorithm and deep forest classifier for virus-drug association prediction (CGDVDA). We generated feature vectors by combining drug chemical structure and virus genomic sequence-based similarities, and extracted prominent deep features by applying the convolutional neural network. The convoluted features are optimized using the genetic algorithm and classified using the ensemble deep forest classifier to predict novel virus-drug associations. The proposed method predicts drugs for COVID-19 and other viral diseases in the dataset. The model could achieve ROC-AUC scores of 0.9159 on fivefold cross-validation. We compared the performance of the model with state-of-the-art approaches and classifiers. The experimental results and case studies illustrate the efficacy of CGDVDA in predicting drugs against viral infectious diseases.

Parkinson's disease (PD) is a complex neurodegenerative disorder lacking effective disease-modifying treatments. In this study, we integrated large-scale protein-protein interaction networks with a multi-modal graph neural network (GNN) to identify and prioritize multi-target drug repurposing candidates for PD. Network analysis and advanced clustering methods delineated functional modules, and a novel Functional Centrality Index was employed to pinpoint key nodes within the PD interactome. The GNN model, incorporating molecular descriptors, network topology, and uncertainty quantification, predicted candidate drugs that simultaneously target critical proteins implicated in lysosomal dysfunction, mitochondrial impairment, synaptic disruption, and neuroinflammation. Among the top hits were compounds such as dithiazanine, ceftolozane, DL-α-tocopherol, bromisoval, imidurea, medronic acid, and modufolin. These findings provide mechanistic insights into PD pathology and demonstrate that a polypharmacology approach can reveal repurposing opportunities for existing drugs. Our results highlight the potential of network-based deep learning frameworks to accelerate the discovery of multi-target therapies for PD and other multifactorial neurodegenerative diseases.

When host cells are infected with coronaviruses, the first viral protein produced is non-structural protein 1 (Nsp1). This protein inhibits host protein synthesis and induces host mRNA degradation to enhance viral proliferation. Despite its critical role, the mechanism by which Nsp1 mediates cellular mRNA degradation remains unclear. In this study, we use cell-free translation to address how host mRNA stability is regulated by Nsp1. We reveal that SARS-CoV-2 Nsp1 binding to the ribosome is enough to trigger mRNA degradation independently of ribosome collisions or active translation. MERS-CoV Nsp1 inhibits translation without triggering degradation, highlighting mechanistic differences between the two Nsp1 counterparts. Nsp1 and viral mRNAs appear to co-evolve, rendering viral mRNAs immune to Nsp1-mediated degradation in SARS-CoV-2, MERS-CoV, and Bat-Hp viruses. By providing insights into the mode of action of Nsp1, our study helps to understand the biology of Nsp1 better and find strategies for therapeutic targeting against coronaviral infections.

Flaviviridae family comprises some of the most vulnerable viruses known for causing widespread outbreaks, high mortality rates, and severe long-term health complications in humans. Viruses like Dengue (DENV), Zika (ZIKV) and Hepatitis C (HCV) are endemic across the globe, especially in tropical and subtropical regions, infecting multiple tissues and leading to significant health crises. Investigating virus-host interactions across tissues can reveal tissue-specific drug targets and aid antiviral drug repurposing. In this study, we employed a multi-step computational approach to construct a comprehensive virus-human interactome by integrating virus-host protein-protein interactions (PPIs) with tissue-specific gene expression profiles to study key protein targets associated with Flaviviridae infections. Mapping drug-target predictions revealed druggable proteins - CCNA2 in peripheral blood mononuclear cells (PBMC) and EIF2S2, CDK7 and CARS in the liver, with Tamoxifen, Sirolimus, Entrectinib and L-cysteine as potential repurposable drugs, respectively. Further protein-ligand docking and molecular dynamics (MD) simulations were conducted to assess the stability of the complexes. These findings highlight common druggable human targets exploited by DENV, ZIKV and HCV, providing a foundation for broad-spectrum antiviral therapies. By focusing on shared host pathways and targets in viral replication, we propose promising drug candidates, supporting the development of unified therapeutic strategies against Flaviviridae viruses.

Objectives: Jasminoidin (JA) and ursodeoxycholic acid (UA) have been shown to exert synergistic effects on cerebral ischemia (CI) therapy, but the underlying mechanisms remain to be elucidated. Objective: To elucidate the synergistic mechanisms involved in the combined use of JA and UA (JU) for CI therapy using a driver-induced modular screening (DiMS) strategy. Methods: Network proximity and topology-based approaches were used to identify synergistic modules and driver genes from an anti-ischemic microarray dataset (ArrayExpress, E-TABM-662). A middle cerebral artery occlusion/reperfusion (MCAO/R) model was established in 30 Sprague Dawley rats, divided into sham, vehicle, JA (25 mg/mL), UA (7 mg/mL), and JU (JA:UA = 1:1) groups. After 90 minutes of ischemia, infarct volume and neurological deficit scores were evaluated. Western blotting was performed 24 h after administration to validate key protein changes. Results: Six, eleven, and four drug-responsive On_modules were identified for JA, UA, and JU, respectively. Three synergistic modules (Sy-modules, JU-Mod-7, 8, and 10) and 12 driver genes (e.g., NRF1, FN1, CUL3) were identified, mainly involving the PI3K-Akt and MAPK pathways and regulation of the actin cytoskeleton. JA and UA synergistically reduced infarct volume and neurological deficit score (2.5, p < 0.05) in MCAO/R rats. In vivo studies demonstrated that JU suppressed the expression of CUL3, FN1, and ITGA4, while it increased that of NRF1. Conclusions: JU acts synergistically on CI-reperfusion injury by regulating FN1, CUL3, ITGA4, and NRF1 and inducing the PI3K-Akt, MAPK, and actin cytoskeleton pathways. DiMS provides a new approach to uncover mechanisms of combination therapies.

Drug repurposing, also known as drug repositioning or reprofiling, involves identifying new therapeutic uses for existing drugs beyond their original indications. Historical examples include sildenafil citrate transitioning to an erectile dysfunction treatment and thalidomide shifting from a sedative to an immunomodulatory agent. Advocates tout its potential to address unmet medical needs by expediting development, reducing costs, and using drugs with established safety profiles. However, concerns exist regarding specificity for new indications, safety, and regulatory exploitation. Ethical considerations include equitable access, informed consent when using drugs off-label, and transparency. Recent advancements include artificial intelligence (AI) applications, network pharmacology, and omics technologies. Clinical trials explore repurposed drugs' efficacy, with regulatory agencies facilitating approval. Challenges include intellectual property protection, drug target specificity, trial design complexities, and funding limitations. Ethical challenges encompass patient autonomy, potential conflicts of interest due to financial incentives for industries, and resource allocation. Future directions involve precision medicine, AI, and global collaboration. In conclusion, drug repurposing offers a promising pathway for therapeutic innovation but requires careful consideration of its complexities and ethical implications to maximize benefits and minimize risks.

Identifying treatments that can alter the natural history of amyotrophic lateral sclerosis (ALS) is challenging. For years, drug discovery in ALS has relied upon traditional approaches with limited success. Drug repurposing, where clinically approved drugs are reevaluated for other indications, offers an alternative strategy that overcomes some of the challenges associated with de novo drug discovery.

In this review, the authors discuss the challenge of drug discovery in ALS and examine the potential of drug repurposing for the identification of new effective treatments. The authors consider a range of approaches, from screening in experimental models to computational approaches, and outline some general principles for preclinical and clinical research to help bridge the translational gap. Literature was reviewed from original publications, press releases and clinical trials.

Despite remaining challenges, drug repurposing offers the opportunity to improve therapeutic options for ALS patients. Nevertheless, stringent preclinical research will be necessary to identify the most promising compounds together with innovative experimental medicine studies to bridge the translational gap. The authors further highlight the importance of combining expertise across academia, industry and wider stakeholders, which will be key in the successful delivery of repurposed therapies to the clinic.

Repurposing of existing drugs for new indications has attracted substantial attention owing to its potential to accelerate drug development and reduce costs. Hundreds of computational resources such as databases and predictive platforms have been developed that can be applied for drug repurposing, making it challenging to select the right resource for a specific drug repurposing project. With the aim of helping to address this challenge, here we overview computational approaches to drug repurposing based on a comprehensive survey of available in silico resources using a purpose-built drug repurposing ontology that classifies the resources into hierarchical categories and provides application-specific information. We also present an expert evaluation of selected resources and three drug repurposing case studies implemented within the Horizon Europe REMEDi4ALL project to demonstrate the practical use of the resources. This comprehensive Review with expert evaluations and case studies provides guidelines and recommendations on the best use of various in silico resources for drug repurposing and establishes a basis for a sustainable and extendable drug repurposing web catalogue.

To address the rapid growth of scientific publications and data in biomedical research, knowledge graphs (KGs) have become a critical tool for integrating large volumes of heterogeneous data to enable efficient information retrieval and automated knowledge discovery (AKD). However, transforming unstructured scientific literature into KGs remains a significant challenge, with previous methods unable to achieve human-level accuracy. In this study, we utilized an information extraction pipeline that won first place in the LitCoin NLP Challenge (2022) to construct a large-scale KG named iKraph using all PubMed abstracts. The extracted information matches human expert annotations and significantly exceeds the content of manually curated public databases. To enhance the KG's comprehensiveness, we integrated relation data from 40 public databases and relation information inferred from high-throughput genomics data. This KG facilitates rigorous performance evaluation of AKD, which was infeasible in previous studies. We designed an interpretable, probabilistic-based inference method to identify indirect causal relations and applied it to real-time COVID-19 drug repurposing from March 2020 to May 2023. Our method identified 600-1400 candidate drugs per month, with one-third of those discovered in the first two months later supported by clinical trials or PubMed publications. These outcomes are very challenging to attain through alternative approaches that lack a thorough understanding of the existing literature. A cloud-based platform (https://biokde.insilicom.com) was developed for academic users to access this rich structured data and associated tools.

Drug repositioning has emerged as a promising strategy for identifying new therapeutic applications for existing drugs. In this study, we present DRGBCN, a novel computational method that integrates heterogeneous information through a deep bilinear attention network to infer potential drugs for specific diseases. DRGBCN involves constructing a comprehensive drug-disease network by incorporating multiple similarity networks for drugs and diseases. Firstly, we introduce a layer attention mechanism to effectively learn the embeddings of graph convolutional layers from these networks. Subsequently, a bilinear attention network is constructed to capture pairwise local interactions between drugs and diseases. This combined approach enhances the accuracy and reliability of predictions. Finally, a multi-layer perceptron module is employed to evaluate potential drugs. Through extensive experiments on three publicly available datasets, DRGBCN demonstrates better performance over baseline methods in 10-fold cross-validation, achieving an average area under the receiver operating characteristic curve (AUROC) of 0.9399. Furthermore, case studies on bladder cancer and acute lymphoblastic leukemia confirm the practical application of DRGBCN in real-world drug repositioning scenarios. Importantly, our experimental results from the drug-disease network analysis reveal the successful clustering of similar drugs within the same community, providing valuable insights into drug-disease interactions. In conclusion, DRGBCN holds significant promise for uncovering new therapeutic applications of existing drugs, thereby contributing to the advancement of precision medicine.

Drug discovery, a multifaceted process from compound identification to regulatory approval, historically plagued by inefficiencies and time lags due to limited data utilization, now faces urgent demands for accelerated lead compound identification. Innovations in biological data and computational chemistry have spurred a shift from trial-and-error methods to holistic approaches to medicinal chemistry. Computational techniques, particularly artificial intelligence (AI), notably machine learning (ML) and deep learning (DL), have revolutionized drug development, enhancing data analysis and predictive modeling. Natural products (NPs) have long served as rich sources of biologically active compounds, with many successful drugs originating from them. Advances in information science expanded NP-related databases, enabling deeper exploration with AI. Integrating AI into NP drug discovery promises accelerated discoveries, leveraging AI's analytical prowess, including generative AI for data synthesis. This perspective illuminates AI's current landscape in NP drug discovery, addressing strengths, limitations, and future trajectories to advance this vital research domain.

Artificial Intelligence (AI) and Machine Learning (ML) are transforming drug discovery by overcoming traditional challenges like high costs, time-consuming, and frequent failures. AI-driven approaches streamline key phases, including target identification, lead optimization, de novo drug design, and drug repurposing. Frameworks such as deep neural networks (DNNs), convolutional neural networks (CNNs), and deep reinforcement learning (DRL) models have shown promise in identifying drug targets, optimizing delivery systems, and accelerating drug repurposing. Generative adversarial networks (GANs) and variational autoencoders (VAEs) aid de novo drug design by creating novel drug-like compounds with desired properties. Case studies, such as DDR1 kinase inhibitors designed using generative models and CDK20 inhibitors developed via structure-based methods, highlight AI's ability to produce highly specific therapeutics. Models like SNF-CVAE and DeepDR further advance drug repurposing by uncovering new therapeutic applications for existing drugs. Advanced ML algorithms enhance precision in predicting drug efficacy, toxicity, and ADME-Tox properties, reducing development costs and improving drug-target interactions. AI also supports polypharmacology by optimizing multi-target drug interactions and enhances combination therapy through predictions of drug synergies and antagonisms. In nanomedicine, AI models like CURATE.AI and the Hartung algorithm optimize personalized treatments by predicting toxicological risks and real-time dosing adjustments with high accuracy. Despite its potential, challenges like data quality, model interpretability, and ethical concerns must be addressed. High-quality datasets, transparent models, and unbiased algorithms are essential for reliable AI applications. As AI continues to evolve, it is poised to revolutionize drug discovery and personalized medicine, advancing therapeutic development and patient care.

Cancer-therapy related cardiac dysfunction (CTRCD) remains a significant cause of morbidity and mortality in cancer survivors. In this study, we aimed to identify differential plasma proteins and metabolites associated with left ventricular dysfunction (LVD) in cancer patients.

We analyzed data from 50 patients referred to the Cleveland Clinic Cardio-Oncology Center for echocardiograph assessment, integrating electronic health records, proteomic, and metabolomic profiles. LVD was defined as an ejection fraction ≤ 55% based on echocardiographic evaluation. Classification-based machine learning models were used to predict LVD using plasma metabolites and proteins as input features.

We identified 13 plasma proteins (P < 0.05) and 14 plasma metabolites (P < 0.05) associated with LVD. Key proteins included markers of inflammation (ST2, TNFRSF14, OPN, and AXL) and chemotaxis (RARRES2, MMP-2, MEPE, and OPN). Notably, sex-specific associations were observed, such as uridine (P = 0.003) in males. Furthermore, metabolomic features significantly associated with LVD included 1-Methyl-4-imidazoleacetic acid (P = 0.015), COL1A1 (P = 0.009), and MMP-2 (P = 0.016), and pointing to metabolic shifts and heightened inflammation in patients with LVD.

Our findings suggest that circulating metabolites may non-invasively detect clinical and molecular differences in patients with LVD, providing insights into underlying disease pathways and potential therapeutic targets.

The global spread of monkeypox, caused by the double-stranded DNA monkeypox virus (MPXV), has underscored the urgent need for effective antiviral treatments. In this study, we aim to identify a potent inhibitor for MPXV DNA polymerase (DNAP), a critical enzyme in the virus replication process. Using a computational drug repurposing approach, we performed a virtual screening of 1615 FDA-approved drugs based on drug-likeness and molecular docking against DNAP. Among these, 1430 compounds met Lipinski's rule of five for drug-likeness, with Doxycycline emerging as the most promising competitive inhibitor, binding strongly to the DNAP active site with a binding affinity of - 9.3 kcal/mol. This interaction involved significant hydrogen bonds, electrostatic interactions, and hydrophobic contacts, with Doxycycline demonstrating a stronger affinity than established antivirals for smallpox, including Cidofovir, Brincidofovir, and Tecovirimat. Stability and flexibility analyses through a 200 ns molecular dynamics simulation and normal mode analysis confirmed the robustness of Doxycycline binding to DNAP. Overall, our results suggest Doxycycline as a promising candidate for monkeypox treatment, though additional experimental and clinical studies are needed to confirm its therapeutic potential and clinical utility.

The online version contains supplementary material available at 10.1007/s40203-025-00307-7.

The global impact of SARS-CoV-2 highlights the need for treatments beyond vaccination, given the limited availability of effective medications. While Pfizer introduced Paxlovid, an FDA-approved antiviral targeting the SARS-CoV-2 main protease (Mpro), this study focuses on designing new antivirals against another protease, papain-like protease (PLpro), which is crucial for viral replication and immune suppression. NCATS/NIH performed a high-throughput screen of ∼15,000 molecules from an internal molecular library, identifying initial hits with a 0.5% success rate. To improve the hit rate and identify potent inhibitors, machine learning-based virtual screens were applied to ∼150,000 compounds, yielding 125 top predicted hits. Biochemical evaluation revealed 25 promising compounds, with a 20% hit-rate and IC50 values from 1.75 μM to <36 μM across 13 chemotypes. Further analog screening of those chemotypes, as part of the structure-activity relationships, led to 20 additional hits. Additionally, the hit-to-lead optimization of chemotype 7 produced 10 more analogs. These PLpro inhibitors provide promising templates for antiviral development against COVID-19.

Internet of Medical Things (IoMT) is a group of medical devices that connect the healthcare information technology to minimize the redundant hospital visit and healthcare system troubles. IoMT connect the patients to the doctor and transmit the medical data over the network. The spread of corona virus has put the people at high risk. Due to increasing number of cases and its stress on health professionals, IoMT technology is used in many healthcare centers. But, the security level and data classification accuracy was not improved by existing methods during the data communication. In order to solve these issues, Cayley-Purser Cryptographic Secured Communication based Jackknife Correlative Data Classification (CPCSC-JCDC) method is designed. The key objective of CPCSC-JCDC method is to collect the patient information through IoMT devices and send to the doctor in more secured manner. Initially in CPCSC-JCDC method, the patient data is collected. After the data collection process, the data gets encrypted with help of public key of the patient by using cayley-purser cryptosystem. After the encryption process, the data is sent to the doctor. The doctor receives and decrypts the patient data by using their private key. After decryption process, the doctor analyses the patient data and classifies the data as emergency case or normal case by using jackknife correlation function. This helps to minimize the patient readmission rate and increase the patient satisfaction level. Experimental evaluation is carried out by Novel Corona Virus 2019 dataset using different metrics like data classification accuracy, data classification time and security level. The evaluation result shows that CPCSC-JCDC method improves the security level as well as accuracy and minimizes the time consumption during data communication than existing works.

Xiao-er-kang-du (XEKD) capsule is a Chinese herbal formula used for treatment of upper respiratory tract infection caused by various viruses in pediatric patients in China. XEKD is used clinically for the treatment of influenza-like symptoms, including fever, chills, cough, stuffy and runny nose, headache, and sore throat, with remarkable efficacy. However, the pharmacologic mechanism of XEKD against influenza B virus (IBV) infection is unclear.

The main purpose of the present work is to explore the curative effect as well as possible mechanisms of XEKD against influenza B virus (IBV) (Victoria strain).

Both in vivo and in vitro experiments were performed to confirm the antiviral properties of XEKD. High-performance liquid chromatography was used to analyze the active components and assess the stability of XEKD. In addition, the mechanism of action of XEKD against IBV (Victoria) was investigated by western blot, immunofluorescence, and immunohistochemical analyses, in addition to confocal fluorescence microscopy.

The findings revealed that XEKD demonstrated antiviral effects against IBV infection in both in vivo and in vitro via the mTOR/ULK1/Beclin1/VPS34 pathway and promote cellular autophagy to mitigate IBV-induced lung tissue damage. The results of this work are expected to lead to a deeper understanding of the mechanism underlying the effect of the XEKD capsule against IBV infections.

IBV infection was found to inhibit autophagy, which exacerbated inflammatory damage. XEKD regulates autophagy through the mTOR/ULK1/Beclin1/VPS34 pathway and exerts antiviral effects, thereby laying a foundation for further development of XEKD as a potential therapeutic against IBV infection.

Herbal formulae have been used in China for thousands of years but have unclear clinical positioning and unknown characteristic indications make it difficult to determine their specific application in various diseases, which seriously hamper their clinical value. Identifying the precise clinical positioning and clinical advantages of similar formulae for related diseases is a critical issue.

To develop a methodology based on modular pharmacology to determine the clinical advantages and precise clinical position of similar formulae.

In this study, we proposed a modular-based network proximity approach to explore drug repositioning and clinical advantages of three formulae, Shirebi tablets (SRB), Yuxuebi capsules (YXB), and Wangbifukang granules (WBFK), for rheumatic disease. First, we constructed a rheumatology target network, and modules were obtained using the cluster tool molecular complex detection (MCODE). Based on the modular interaction map established by a quantitative approach for inter-module coordination and its transition (IMCC), using a targeting rate (TR) matrix to identify targeted modules of three formulae. Moreover, the network proximity Z-score and Jaccard similarity coefficient were used to identify potential optimal symptomatic indications and related diseases using three formulae. At the same time, the driver genes for SRB and gouty arthritis were identified by flow centrality and shortest distance, and the epresentative driver genes were validated by in vivo experiments.

32 modules were obtained using the MCODE method. 4, 4, and 14 characteristic targeted modules of SRB, YXB, and WBFK, respectively, were identified using a targeting rate (TR) matrix. Module 2, 16, and 19 were targeted by both SRB and WBFK. The common effects of SRB and WBFK focused on inflammatory response and innate immune response, YXB was found to be involved in the collagen catabolic process, transmembrane receptor protein serine/threonine kinase signaling pathway. Moreover, potential optimal symptomatic indications and representative related diseases were identified for three formulae: SRB was significantly associated with GA (Z = -20.26); YXB was significantly associated with AS (Z = -4.532), MI (Z = -29.11), RhFv (Z = -6.945), OA (Z = -39.97), and GA (Z = -13.03); and WBFK was significantly associated with MI (Z = -205.5), SLE (Z = -37.65), RhFv (Z = -42.45), and GA (Z = -17.24). Finally, 8 driver genes for SRB and gouty arthritis were identified,the representative driver genes TRAF6 and NFE2L1 were validated by in vivo experiments.

The modular-based network proximity approach proposed in this study may provide a new perspective for the precise drug repositioning and clinical advantages of similar formulae in disease treatment.

Drug discovery is a complicated process through which new therapeutics are identified to prevent and treat specific diseases. Identification of drug-target interactions (DTIs) stands as a pivotal aspect within the realm of drug discovery and development. The traditional process of drug discovery, especially identification of DTIs, is marked by its high costs of experimental assays and low success rates. Computational methods have emerged as indispensable tools, especially those employing artificial intelligence (AI) methods, which could streamline the process, thereby reducing costs and time consumption and potentially increasing success rates. In this review, we focus on the application of AI techniques in DTI prediction. Specifically, we commence with a comprehensive overview of drug discovery and development, along with systematic prediction and validation of DTIs. We proceed to highlight the prominent databases and toolkits used in developing AI methods for DTI prediction, as well as with methodologies for evaluating their efficacy. We further extend the exploration into three primary types of state-of-the-art AI methods used in DTI prediction, including classical machine learning, deep learning and network-based methods. Finally, we summarize the key findings and outline the current challenges and future directions that AI methods face in scientific drug discovery and development.

Immune checkpoint inhibitors (ICIs) are drugs that inhibit immune checkpoint (ICP) molecules to restore the antitumor activity of immune cells and eliminate tumor cells. Due to the limitations and certain side effects of current ICIs, such as programmed death protein-1, programmed cell death-ligand 1, and cytotoxic T lymphocyte-associated antigen 4 (CTLA4) antibodies, there is an urgent need to find new drugs with ICP inhibitory effects. In this study, a network-based computational framework called multi-network algorithm-driven drug repositioning targeting ICP (Mnet-DRI) is developed to accurately repurpose novel ICIs from ≈3000 Food and Drug Administration-approved or investigational drugs. By applying Mnet-DRI to PD-L1, maprotiline (MAP), an antidepressant drug is repurposed, as a potential PD-L1 modifier for colorectal and lung cancers. Experimental validation revealed that MAP reduced PD-L1 expression by targeting E3 ubiquitin ligase speckle-type zinc finger structural protein (SPOP), and the combination of MAP and anti-CTLA4 in vivo significantly enhanced the antitumor effect, providing a new alternative for the clinical treatment of colorectal and lung cancer.

High microglial heterogeneities hinder the development of microglia-targeted treatment for Alzheimer's disease (AD).

We integrated 0.7 million single-nuclei RNA-sequencing transcriptomes from human brains using a variational autoencoder. We predicted AD-relevant microglial subtype-specific transition networks for disease-associated microglia (DAM), tau microglia, and neuroinflammation-like microglia (NIM). We prioritized drugs by specifically targeting microglia-specific transition networks and validated drugs using two independent real-world patient databases.

We identified putative AD molecular drivers (e.g., SYK, CTSB, and INPP5D) in transition networks of DAM and NIM. Via specifically targeting NIM, we identified that usage of ketorolac was associated with reduced AD incidence in both MarketScan (hazard ratio [HR] = 0.89) and INSIGHT (HR = 0.83) Clinical Research Network databases, mechanistically supported by ketorolac-treated transcriptomic data from AD patient induced pluripotent stem cell-derived microglia.

This study offers insights into the pathobiology of AD-relevant microglial subtypes and identifies ketorolac as a potential anti-inflammatory treatment for AD.

An integrative analysis of ≈ 0.7 million single-nuclei RNA-sequencing transcriptomes from human brains identified Alzheimer's disease (AD)-relevant microglia subtypes. Network-based analysis identified putative molecular drivers (e.g., SYK, CTSB, INPP5D) of transition networks between disease-associated microglia (DAM) and neuroinflammation-like microglia (NIM). Via network-based prediction and population-based validation, we identified that usage of ketorolac (a US Food and Drug Administration-approved anti-inflammatory medicine) was associated with reduced AD incidence in two independent patient databases. Mechanistic observation showed that ketorolac treatment downregulated the Type-I interferon signaling in patient induced pluripotent stem cell-derived microglia, mechanistically supporting its protective effects in real-world patient databases.

Background/Objectives: Breast cancer in women is the most commonly diagnosed and most malignant tumor. Although luminal A breast cancer (LumA) has a relatively better prognosis, it still has a persistent pattern of recurrence. Ganoderma lucidum (Curtis) P. Karst. is a kind of traditional Chinese medicine and has antitumor effects. In this study, we aimed to identify the genes relevant to prognosis, find novel targets, and investigate the function of the bioactive protein from G. lucidum, called FIP-glu, in improving prognosis. Methods: Gene expression data and clinical information of LumA breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Using bioinformatics methods, a predictive risk model was constructed to predict the prognosis for each patient. The cell counting kit-8 (CCK8) and clone formation assays were used to validate gene function. The ability of FIP-glu to regulate RNA levels of risk genes was validated. Results: Six risk genes (slit-roundabout GTPase-activating protein 2 (SRGAP2), solute carrier family 35 member 2 (SLC35A2), sequence similarity 114 member A1 (FAM114A1), tumor protein P53-inducible protein 11 (TP53I11), transmembrane protein 63C (TMEM63C), and polymeric immunoglobulin receptor (PIGR)) were identified, and a prognostic model was constructed. The prognosis was worse in the high-risk group and better in the low-risk group. The receiver operating characteristic (ROC) curve confirmed the model's accuracy. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that the differentially expressed genes (DEGs) between the high- and low-risk groups were significantly enriched in the immune responses. TMEM63C could promote tumor viability, growth, and proliferation in vitro. FIP-glu significantly regulated these risk genes, and attenuated the promoting effect of TMEM63C in breast cancer cells. Conclusions: SRGAP2, SLC35A2, FAM114A1, TP53I11, TMEM63C, and PIGR were identified as the potential risk genes for predicting the prognosis of patients. TMEM63C could be a potential novel therapeutic target. Moreover, FIP-glu was a promising drug for improving the prognosis of LumA breast cancer.

Herpes simplex virus (HSV) infection has worldwide public health concerns and lifelong medical impacts. The standard therapy, acyclovir, has limited efficacy in preventing HSV subclinical virus shedding, and drug resistance occurs in immunocompromised patients, highlighting the need for novel therapeutics. HSV infection manifests in the skin epidermal layer, but current drug discovery utilizes Vero cells and fibroblasts monolayer cultures, capturing neither in vivo relevance nor tissue environment. To bridge the gap, we established 3D bioprinted human skin equivalents that recapitulate skin architecture in a 96-well plate format amenable for antiviral screening and preclinical testing. Screening a library of 738 compounds with broad targets and mechanisms of action, we identified potent antivirals, including most of the known anti-HSV compounds, validating the translational relevance of our assay. Acyclovir was dramatically less potent for inhibiting HSV in keratinocytes compared to donor-matched fibroblasts. In contrast, antivirals against HSV helicase/primase or host replication pathways displayed similar potency across cell types and donor sources in 2D and 3D models. The reduced potency of acyclovir in keratinocytes, the primary cell type encountered by HSV reactivation, helps explain the limited benefit acyclovir and its congeners play in reducing sexual transmission. Finally, we demonstrated that our 3D bioprinted skin platform can integrate patient-derived cells, facilitating the incorporation of variable genetic backgrounds early into drug testing. Thus, these data indicate that the 3D bioprinted human skin equivalent assay platform provides a more physiologically relevant approach to identifying potential antivirals for HSV-directed drug development.

Endometrial cancer (EC) is the 6th most common cancer in women around the world. Alone in the United States (US), 66,200 new cases and 13,030 deaths are expected to occur in 2023 which needs the rapid development of potential therapies against EC. Here, a network-based drug-repurposing strategy is developed which led to the identification of 16 FDA-approved drugs potentially repurposable for EC as potential immune checkpoint inhibitors (ICIs). A network of EC-associated immune checkpoint proteins (ICPs)-induced protein interactions (P-ICP) was constructed. As a result of network analysis of P-ICP, top key target genes closely interacting with ICPs were shortlisted followed by network proximity analysis in drug-target interaction (DTI) network and pathway cross-examination which identified 115 distinct pathways of approved drugs as potential immune checkpoint inhibitors. The presented approach predicted 16 drugs to target EC-associated ICPs-induced pathways, three of which have already been used for EC and six of them possess immunomodulatory properties providing evidence of the validity of the strategy. Classification of the predicted pathways indicated that 15 drugs can be divided into two distinct pathway groups, containing 17 immune pathways and 98 metabolic pathways. In addition, drug-drug correlation analysis provided insight into finding useful drug combinations. This fair and verified analysis creates new opportunities for the quick repurposing of FDA-approved medications in clinical trials.

The advent of artificial intelligence (AI) has catalyzed a profound transformation in the pharmaceutical industry, ushering in a paradigm shift across various domains, including drug discovery, formulation development, manufacturing, quality control, and post-market surveillance. This comprehensive review examines the multifaceted impact of AI-driven technologies on all stages of the pharmaceutical life cycle. It discusses the application of machine learning algorithms, data analytics, and predictive modeling to accelerate drug discovery processes, optimize formulation development, enhance manufacturing efficiency, ensure stringent quality control measures, and revolutionize post-market surveillance methodologies. By describing the advancements, challenges, and future prospects of harnessing AI in the pharmaceutical landscape, this review offers valuable insights into the evolving dynamics of drug development and regulatory practices in the era of AI-driven innovation.

Amidst the ongoing global challenge of the SARS-CoV-2 pandemic, the quest for effective antiviral medications remains paramount. This comprehensive review delves into the dynamic landscape of FDA-approved medications repurposed for COVID-19, categorized as antiviral and non-antiviral agents. Our focus extends beyond conventional narratives, encompassing vaccination targets, repurposing efficacy, clinical studies, innovative treatment modalities, and future outlooks. Unveiling the genomic intricacies of SARS-CoV-2 variants, including the WHO-designated Omicron variant, we explore diverse antiviral categories such as fusion inhibitors, protease inhibitors, transcription inhibitors, neuraminidase inhibitors, nucleoside reverse transcriptase, and non-antiviral interventions like importin α/β1-mediated nuclear import inhibitors, neutralizing antibodies, and convalescent plasma. Notably, Molnupiravir emerges as a pivotal player, now licensed in the UK. This review offers a fresh perspective on the historical evolution of COVID-19 therapeutics, from repurposing endeavors to the latest developments in oral anti-SARS-CoV-2 treatments, ushering in a new era of hope in the battle against the pandemic.

The Co-administration of multiple drugs can enhance the efficacy of disease treatment by reducing drug resistance and side effects. However, it also raises the risk of adverse drug interactions, presenting a challenging problem in healthcare. Various approaches have been developed to predict drug-drug interactions (DDIs) by leveraging both knowledge graphs and drug attribute information. While these methods have shown promise, they often fail to effectively capture correlations between biomedical information in the knowledge graph and drug properties. This work introduces a novel end-to-end DDI predictor framework based on generative adversarial networks. This framework utilizes a message-passing neural network to capture molecular structure information while employing the knowledge-aware graph attention network to capture the representation of drugs in the knowledge graph through considering the importance of different multihop neighbor nodes and relationships. The dual generative adversarial networks employ two generators and two discriminators in knowledge graph embedding and molecular topology embedding for adversarial training to capture the interrelations and complementary knowledge between molecular structure information and semantic information from the knowledge graph. comprehensive experiments have demonstrated that the proposed method outperforms state-of-the-art algorithms in binary classification, with improvements of 1.0% in accuracy, 0.45% in area under the receiver operating characteristic curve (AUC), 0.24% in area under the precision-recall curve (AUPR), and 0.98% in F1 score. Furthermore, for multiclass classification tasks, improvements were observed across various evaluation metrics, including 0.9% in accuracy, 0.25% in macro precision, 0.2% in macro recall, and 0.28% in macro F1. Additionally, ablation studies were conducted to showcase the effectiveness and robustness of our method in DDI prediction tasks.

Although various strategies have been used to prevent and treat SARS-CoV-2, the spread and evolution of SARS-CoV-2 is still progressing rapidly. The emerging variants Omicron and its sublineage have a greater ability to spread and escape nearly all current monoclonal antibodies treatments, highlighting an urgent need to develop therapeutics targeting current and emerging Omicron variants or recombinants with breadth and potency. Here, some small molecule drugs are rapidly identified that could covalently binding to receptor binding domain (RBD) protein of Omicron through the combined application of artificial intelligence (AI) and activity-based protein profiling (ABPP) technology. The surface plasmon resonance (SPR) and pseudo-virus neutralization experiments further reveal that an FDA-approved drug gallic acid has robust neutralization potency against Omicron pseudo-virus with the IC50 values of 23.56 × 10-6 m. Taken together, a platform combining AI intelligence, biochemical, SPR, molecular docking, and pseudo-virus-based screening for rapid identification and evaluation of potential anti-SARS-CoV-2 small molecule drugs is established and the effectiveness of the platform is validated.

The emergence of the human monkeypox virus (MPXV) and the lack of effective medications have necessitated the exploration of various strategies to combat its infection. This study employs a network-based approach to drug discovery, utilizing the BLASTn and phylogenetic analysis to compare the MPXV genome with those of 18 related orthopoxviruses, revealing over 75 % genomic similarity. Through a literature review, 160 human-host proteins linked to MPXV and its relatives were identified, leading to the construction of a human-host protein interactome. Analysis of this interactome highlighted 39 central hub proteins, which were then examined for potential drug targets. The process successfully revealed 15 targets already approved for use with medications. Additionally, the functional enrichment analysis provided insights into potential pathways and disorders connected with these targets. Four medications, namely Baricitinib, Infliximab, Adalimumab, and Etanercept, have been identified as potential candidates for repurposing to combat MPXV. In addition, the pharmacophore-based screening identified a molecule that is comparable to Baricitinib and has the potential to be effective against MPXV. The findings of the study suggest that ZINC22060520 is a promising medication for treating MPXV infection and proposes these medications as potential options for additional experimental and clinical assessment in the battle against MPXV.

This study introduces a deep learning framework based on SMILES representations of chemical structures to predict drug-drug interactions (DDIs). The model extracts Morgan fingerprints and key molecular descriptors, transforming them into raw graphical features for input into a modified ResNet18 architecture. The deep residual network, enhanced with regularization techniques, efficiently addresses training issues such as gradient vanishing and exploding, resulting in superior predictive performance. Experimental results show that StructNet-DDI achieved an AUC of 99.7%, an accuracy of 94.4%, and an AUPR of 99.9%, demonstrating the model's effectiveness and reliability. These findings highlight that StructNet-DDI can effectively extract crucial features from molecular structures, offering a simple yet robust tool for DDI prediction.

Given that there is currently no clinically approved drug or vaccine for parainfluenza 3 (PIV3), we applied a drug repurposing method based on disease similarity and chemical similarity to screen 2,585 clinically approved chemical drugs using PIV3 potential drugs BCX-2798 and zanamivir as our controls. Twelve candidate drugs were obtained after being screened with good disease similarity and chemical similarity (S > 0.50, T > 0.56). When docking them with the PIV3 target protein, hemagglutinin-neuraminidase (HN), only oseltamivir was docked with a better score than BCX-2798, which indicates that oseltamivir has an inhibitory effect on PIV3. After the distance (Z d c ) between the drug target of 14 drugs and the PIV3 disease target was measured by the network proximity method based on the PIV3 disease module, it was found that theZ d c values of amikacin, oseltamivir, ribavirin, and streptomycin were less than those of the control. Thus, oseltamivir is the best potential drug because it met all the above screening requirements. Additionally, to explore whether oseltamivir binds to HN stably, molecular dynamics simulation of the binding of oseltamivir to HN was carried out, and the results showed that the RMSD value of the complex tended to be stable within 100 ns, and the binding free energy of the complex was low (-10.60 kcal/mol). It was proved that oseltamivir screened by our drug repurposing method had the potential feasibility of treating PIV3.

Contemporary drug discovery paradigms rely heavily on binding assays about the bio-physicochemical processes. However, this dominant approach suffers from overlooked higher-order interactions arising from the intricacies of molecular mechanisms, such as those involving cis-regulatory elements. It introduces potential impairments and restrains the potential development of computational methods. To address this limitation, I developed a deep learning model that leverages an end-to-end approach, relying exclusively on therapeutic information about drugs. By transforming textual representations of drug and virus genetic information into high-dimensional latent representations, this method evades the challenges arising from insufficient information about binding specificities. Its strengths lie in its ability to implicitly consider complexities such as epistasis and chemical-genetic interactions, and to handle the pervasive challenge of data scarcity. Through various modeling skills and data augmentation techniques, the proposed model demonstrates outstanding performance in out-of-sample validations, even in scenarios with unknown complex interactions. Furthermore, the study highlights the importance of chemical diversity for model training. While the method showcases the feasibility of deep learning in data-scarce scenarios, it reveals a promising alternative for drug discovery in situations where knowledge of underlying mechanisms is limited.

Integrating artificial intelligence (AI) with electrochemical biosensors is revolutionizing medical treatments by enhancing patient data collection and enabling the development of advanced wearable sensors for health, fitness, and environmental monitoring. Electrochemical biosensors, which detect biomarkers through electrochemical processes, are significantly more effective. The integration of artificial intelligence is adept at identifying, categorizing, characterizing, and projecting intricate data patterns. As the Internet of Things (IoT), big data, and big health technologies move from theory to practice, AI-powered biosensors offer significant opportunities for real-time disease detection and personalized healthcare. Still, they also pose challenges such as data privacy, sensor stability, and algorithmic bias. This paper highlights the critical advances in material innovation, biorecognition elements, signal transduction, data processing, and intelligent decision systems necessary for developing next-generation wearable and implantable devices. Despite existing limitations, the integration of AI into biosensor systems shows immense promise for creating future medical devices that can provide early detection and improved patient outcomes, marking a transformative step forward in healthcare technology.

Cardiovascular and cerebrovascular diseases are the leading causes of death worldwide and interact closely with each other. Danhong Injection (DHI) is a widely used preparation for the co-treatment of brain and heart diseases (CTBH). However, the underlying molecular endotype mechanisms of DHI in the CTBH remain unclear.

To elucidate the underlying endotype mechanisms of DHI in the CTBH.

In this study, we proposed a modular-based disease and drug-integrated analysis (MDDIA) strategy for elucidating the systematic CTBH mechanisms of DHI using high-throughput transcriptome-wide sequencing datasets of DHI in the treatment of patients with stable angina pectoris (SAP) and cerebral infarction (CI). First, we identified drug-targeted modules of DHI and disease modules of SAP and CI based on the gene co-expression networks of DHI therapy and the protein-protein interaction networks of diseases. Moreover, module proximity-based topological analyses were applied to screen CTBH co-module pairs and driver genes of DHI. At the same time, the representative driver genes were validated via in vitro experiments on hypoxia/reoxygenation-related cardiomyocytes and neuronal cell lines of H9C2 and HT22.

Seven drug-targeted modules of DHI and three disease modules of SAP and CI were identified by co-expression networks. Five modes of modular relationships between the drug and disease modules were distinguished by module proximity-based topological analyses. Moreover, 13 targeted module pairs and 17 driver genes associated with DHI in the CTBH were also screened. Finally, the representative driver genes AKT1, EDN1, and RHO were validated by in vitro experiments.

This study, based on clinical sequencing data and modular topological analyses, integrated diseases and drug targets. The CTBH mechanism of DHI may involve the altered expression of certain driver genes (SRC, STAT3, EDN1, CYP1A1, RHO, RELA) through various enriched pathways, including the Wnt signaling pathway.

The global health pandemic known as COVID-19, which stems from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a significant concern worldwide. Several treatment methods exist for COVID-19; however, there is an urgent demand for previously established drugs and vaccines to effectively combat the disease. Since, discovering new drugs poses a significant challenge, making the repurposing of existing drugs can potentially reduce time and costs compared to developing entirely new drugs from scratch. The objective of this study is to identify hub genes and associated repurposed drugs targeting them. We analyzed differentially expressed genes (DEGs) by analyzing RNA-seq transcriptomic datasets and integrated with genes associated with COVID-19 present in different databases. We detected 173 Covid-19 associated genes for the construction of a protein-protein interaction (PPI) network which resulted in the identification of the top 10 hub genes/proteins (STAT1, IRF7, MX1, IRF9, ISG15, OAS3, OAS2, OAS1, IRF3, and IRF1). Hub genes were subjected to GO functional and KEGG pathway enrichment analyses, which indicated some key roles and signaling pathways that were strongly related to SARS-CoV-2 infections. We conducted drug repurposing analysis using CMap, TTD, and DrugBank databases with these 10 hub genes, leading to the identification of Piceatannol, CKD-712, and PMID26394986-Compound-10 as top-ranked candidate drugs. Finally, drug-gene interactions analysis through molecular docking and validated via molecular dynamic simulation for 80 ns suggests PMID26394986-Compound-10 as the only potential drug. Our research demonstrates how in silico analysis might produce repurposing candidates to help respond faster to new disease outbreaks.Communicated by Ramaswamy H. Sarma.

Hypertension has become a major contributor to the morbidity and mortality of cardiovascular diseases worldwide. Despite the evidence of the anti-hypertensive effect of gastrodia-uncaria granules (GUG) in hypertensive patients, little is known about its potential therapeutic targets as well as the underlying mechanism. GUG components were sourced from TCMSP and HERB, with bioactive ingredients screened. Hypertension-related targets were gathered from DisGeNET, OMIM, GeneCards, CTD, and GEO. The STRING database constructed a protein-protein interaction network, visualized by Cytoscape 3.7.1. Core targets were analyzed via GO and KEGG using R package ClusterProfiler. Molecular docking with AutodockVina 1.2.2 revealed favorable binding affinities. In vivo studies on hypertensive mice and rats validated network pharmacology findings. GUG yielded 228 active ingredients and 1190 targets, intersecting with 373 hypertension-related genes. PPI network analysis identified five core genes: AKT1, TNF-α, GAPDH, IL-6, and ALB. Top enriched GO terms and KEGG pathways associated with the anti-hypertensive properties of GUG were documented. Molecular docking indicated stable binding of core components to targets. In vivo study showed that GUG could improve vascular relaxation, alleviate vascular remodeling, and lower blood pressure in hypertensive animal models possibly through inhibiting inflammatory factors such as AKT1, mTOR, and CCND1. Integrated network pharmacology and in vivo experiment showed that GUG may exert anti-hypertensive effects by inhibiting inflammation response, which provides some clues for understanding the effect and mechanisms of GUG in the treatment of hypertension.