Network-Based Methods for Prediction of Drug-Target Interactions

Labels as a feature: Network homophily for systematically annotating human GPCR drug-target interactions

Machine learning has revolutionized drug discovery by enabling the exploration of vast, uncharted chemical spaces essential for discovering novel patentable drugs. Despite the critical role of human G protein-coupled receptors in FDA-approved drugs, exhaustive in-distribution drug-target interaction testing across all pairs of human G protein-coupled receptors and known drugs is rare due to significant economic and technical challenges. This often leaves off-target effects unexplored, which poses a considerable risk to drug safety. In contrast to the traditional focus on out-of-distribution exploration (drug discovery), we introduce a neighborhood-to-prediction model termed Chemical Space Neural Networks that leverages network homophily and training-free graph neural networks with labels as features. We show that Chemical Space Neural Networks' ability to make accurate predictions strongly correlates with network homophily. Thus, labels as features strongly increase a machine learning model's capacity to enhance in-distribution prediction accuracy, which we show by integrating labeled data during inference. We validate these advancements in a high-throughput yeast biosensing system (3773 drug-target interactions, 539 compounds, 7 human G protein-coupled receptors) to discover novel drug-target interactions for FDA-approved drugs and to expand the general understanding of how to build reliable predictors to guide experimental verification.

Integrating network pharmacology and in vivo study to explore the anti-Alzheimer's potential of Bergenia ligulata and Nelumbo nucifera

Amyloid plaque buildup, tau protein tangles, oxidative stress, and neuronal death are the hallmarks of Alzheimer's disease (AD). Using network pharmacology, molecular docking, and in vivo experiments, this study investigated the neuroprotective potential of Bergenia ligulata (BL) and Nelumbo nucifera (NN) against aluminum chloride (AlCl₃)-induced AD. Network pharmacology focused on important biomarker proteins like acetylcholinesterase (AChE), BCL2, and caspase-3 to identify 74 bioactive targets linked to AD. The evaluation of ligand-protein interactions was done using molecular docking. Male Wistar rats were exposed to AlCl₃ to cause AD-like pathology in vivo, and a combination treatment of BL and NN at varying doses was provided. Apoptosis markers (BCL2, caspase-3), biochemical investigations (AChE activity, oxidative stress markers-GSH, SOD, catalase, and lipid peroxidation), behavioral evaluations (elevated plus maze, conditioned avoidance test), and histopathological analyses were investigated. The combination of BL and NN demonstrated substantial neuroprotection in a dose-dependent manner. Reduced AChE levels point out improved cholinergic activity. Oxidative stress indicators showed improvement, with lower levels of malondialdehyde and higher anti-oxidant levels of GSH, SOD, and catalase. Apoptotic markers showed an increase in BCL2 expression and a decrease in caspase-3, suggesting anti-apoptotic effects. Reduced neuronal degeneration in the cortex and hippocampal regions was confirmed by histopathology of the brain. The synergistic potential of BL and NN demonstrated potent neuroprotective effects by modulating AChE activity, reducing oxidative stress, increasing anti-oxidant levels, and inhibiting apoptosis. These findings highlighted the potential of BL and NN as a new therapeutic approach for the AD.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-025-04274-w.

Soy Isoflavones Protects Against Stroke by Inhibiting Keap1/NQO1/Nrf2/HO-1 Signaling Pathway: Network Pharmacology Analysis Combined with the Experimental Validation

Objectives: Ischemic stroke is a severe neurological disorder with high morbidity, mortality, and disability rates, posing a substantial burden on patients, families, and healthcare systems. Soy isoflavone (SI), a naturally occurring phytoestrogen, has demonstrated promising neuroprotective effects. This study aimed to evaluate the anti-stroke efficacy of SI and elucidate its underlying mechanisms through integrated phytochemical profiling, network pharmacology, and both in vitro and in vivo experimental validation. Methods: Active constituents of SI were extracted via reflux and identified using liquid chromatography-mass spectrometry (LC-MS). Network pharmacology was employed to predict therapeutic targets and signaling pathways. The neuroprotective effects of SI were first assessed in PC12 cells subjected to oxygen-glucose deprivation/reoxygenation (OGD/R) injury in vitro. For in vivo evaluation, transient cerebral ischemia-reperfusion injury was induced using the bilateral common carotid artery occlusion (BCCAO) model in adult male ICR rats (27.3 ± 1.8 g; 6-8 weeks old), obtained from the Shanghai Experimental Animal Center, Chinese Academy of Sciences. Forty-eight rats were randomly assigned into four groups (n = 12): sham, model (BCCAO), SI-treated (100 mg/kg, oral gavage for 5 days), and edaravone (EDA)-treated (10 mg/kg, i.p., positive control). All procedures were approved by the Institutional Animal Care and Use Committee of Changchun Normal University (Approval No. 2024003, 13 March 2024) and conducted in accordance with the NIH guidelines and ARRIVE 2.0 reporting standards. Results: In vitro, SI significantly enhanced PC12 cell viability from 57.23 ± 2.88% to 80.76 ± 4.43% following OGD/R. It also reduced intracellular Ca2+ by 58.42%, lactate dehydrogenase (LDH) release by 37.67%, caspase-3 activity by 55.05%, and reactive oxygen species (ROS) levels by 74.13% (p < 0.05). A flow cytometry analysis revealed that OGD/R increased the apoptosis rate from 5.34% (control) to 30.85% (model group), which was significantly attenuated by SI treatment, especially in the 560 µg/mL group (20.00%), followed by the 140 and 280 µg/mL groups. In vivo, SI improved neurological scores from 8.3 ± 1.09 to 6.8 ± 1.68, reduced cerebral infarction volume by 18.49%, and alleviated brain edema by 10.42% (p < 0.05). SI also decreased malondialdehyde (MDA) and LDH levels by 31.15% and 39.46%, respectively, while increasing the activity of antioxidant enzymes: superoxide dismutase (SOD) by 11.70%, catalase (CAT) by 26.09%, and glutathione peroxidase (GSH-px) by 27.55% (p < 0.01). Scratch assay results showed that SI restored the impaired migratory ability of the OGD/R-treated PC12 cells, further supporting its role in cellular repair. A Western blot analysis demonstrated the upregulation of nuclear factor erythroid 2-related factor 2 (Nrf2), heme oxygenase-1 (HO-1), and NAD(P)H:quinone oxidoreductase 1 (NQO1) and the downregulation of Kelch-like, ECH-associated protein 1 (Keap1) in the cerebral ischemia-reperfusion model. Conclusions: These findings indicate that soy isoflavone confers significant neuroprotective effects against cerebral ischemia-reperfusion injury by enhancing endogenous antioxidant defense mechanisms, reducing oxidative stress, inhibiting apoptosis, and promoting cell migration. The protective effects are likely mediated through the activation of the Nrf2/Keap1 signaling pathway, supporting the therapeutic potential of SI in ischemic stroke treatment.

DTIAM: a unified framework for predicting drug-target interactions, binding affinities and drug mechanisms

Accurate and robust prediction of drug-target interactions (DTIs) plays a vital role in drug discovery but remains challenging due to limited labeled data, cold start problems, and insufficient understanding of mechanisms of action (MoA). Distinguishing activation and inhibition mechanisms is particularly critical in clinical applications. Here, we propose DTIAM, a unified framework for predicting interactions, binding affinities, and activation/inhibition mechanisms between drugs and targets. DTIAM learns drug and target representations from large amounts of label-free data through self-supervised pre-training, which accurately extracts their substructure and contextual information, and thus benefits the downstream prediction based on these representations. DTIAM achieves substantial performance improvement over other state-of-the-art methods in all tasks, particularly in the cold start scenario. Moreover, independent validation demonstrates the strong generalization ability of DTIAM. All these results suggest that DTIAM can provide a practically useful tool for predicting novel DTIs and further distinguishing the MoA of candidate drugs.

Mechanism of Guishao Yigong decoction in treating colorectal cancer based on network pharmacology and experimental validation

OBJECTIVES

To explore the effective components of Guishao Yigong decoction (GYD) in the treatment of colorectal cancer and reveal its potential mechanism of action.

METHODS

Through network pharmacology, the main target and signaling pathway of GYD therapy for colorectal cancer (CRC) were found. Subsequently, the effect of GYD was verified by in vitro cell viability measurements, colony formation, and scratch healing tests. The effects of GYD on metabolic pathways in vivo were found through plasma metabolomics. Finally, flow cytometry and qPCR experiments were used to verify the cycle-blocking effect of GYD on CRC cells.

KEY FINDINGS

Based on the network pharmacological analysis and molecular docking technology, it was found that GYD could restrain the growth of CRC cells by affecting lipid metabolic pathways and mitogen-activated protein kinase (MAPK) signaling pathways. A series of cell experiments showed that GYD could inhibit the proliferation, migration and clonogenic ability of CRC cells. Furthermore, the plasma metabolomics results showed that GYD could affect the production of unsaturated fatty acids in mice. Flow cytometry and qPCR experiments further proved that GYD blocked the CRC cells in the G1 phase and modulated the expression of cell cycle-related targets, such as AKT, TP53, CDKN1A, and CDK2.

CONCLUSIONS

All the results indicated that GYD could regulate the related metabolism of unsaturated fatty acids. Thus, the cell cycle was blocked and the expressions of the key proteins such as AKT and TP53 were regulated, which achieved the purpose of intervention in colorectal cancer.

Unveiling the anti-inflammatory mechanism of exogenous hydrogen sulfide in Kawasaki disease based on network pharmacology and experimental validation

Kawasaki disease (KD) is a severe pediatric vasculitis leading to coronary artery complications. Hydrogen sulfide (H2S), a recognized endogenous gasotransmitter with anti-inflammatory properties, offers potential as a novel treatment for KD through its cardiovascular benefits. However, the specific effects and underlying mechanisms remain unclear. The objective of present study is to investigate the anti-inflammatory and therapeutic effects of exogenous H2S in KD using network pharmacology and experimental validation. By online database searches, a total of 405 pharmacological targets for H2S, 826 KD-related targets, and 107 potential therapeutic targets of H2S for KD were identified. Through PPI analysis and Cytoscape screening, 9 hub genes were filtered, namely TNF, IL6, JUN, AKT1, IL1B, TP53, NFKB1, MAPK1, and RELA. KEGG pathway enrichment indicated that the TLR4/MyD88/NF-κB signaling pathway may play a crucial role in the therapeutic effects of H2S on KD. Additionally, in vivo experiments confirmed that the treatment of sodium hydrosulfide (NaHS), an H2S donor, markedly improved body weight, reduced inflammatory pathology in the coronary arteries, and downregulated levels of inflammatory cytokines TNF-α, IL-1β, and IL-6. Furthermore, WB analysis confirmed that NaHS inhibited the expression of TLR4, MyD88, NF-κB, and p-NF-κB. In brief, it is the first to reveal that exogenous H2S attenuates the inflammatory response in KD via the TLR4/MyD88/NF-κB pathway, highlighting its potential as a novel therapeutic approach for KD. These findings lay a foundation for further development of H2S-based therapies for KD management.

Artificial Intelligence Models and Tools for the Assessment of Drug-Herb Interactions

Artificial intelligence (AI) has emerged as a powerful tool in medical sciences that is revolutionizing various fields of drug research. AI algorithms can analyze large-scale biological data and identify molecular targets and pathways advancing pharmacological knowledge. An especially promising area is the assessment of drug interactions. The AI analysis of large datasets, such as drugs' chemical structure, pharmacological properties, molecular pathways, and known interaction patterns, can provide mechanistic insights and identify potential associations by integrating all this complex information and returning potential risks associated with these interactions. In this context, an area where AI may prove valuable is in the assessment of the underlying mechanisms of drug interactions with natural products (i.e., herbs) that are used as dietary supplements. These products pose a challenging problem since they are complex mixtures of constituents with diverse and limited information regarding their pharmacological properties, especially their pharmacokinetic data. As the use of herbal products and supplements continues to grow, it becomes increasingly important to understand the potential interactions between them and conventional drugs and the associated adverse drug reactions. This review will discuss AI approaches and how they can be exploited in providing valuable mechanistic insights regarding the prediction of interactions between drugs and herbs, and their potential exploitation in experimental validation or clinical utilization.

Dual graph-embedded fusion network for predicting potential microbe-disease associations with sequence learning

Recent studies indicate that microorganisms are crucial for maintaining human health. Dysbiosis, or an imbalance in these microbial communities, is strongly linked to a variety of human diseases. Therefore, understanding the impact of microbes on disease is essential. The DuGEL model leverages the strengths of graph convolutional neural network (GCN) and graph attention network (GAT), ensuring that both local and global relationships within the microbe-disease association network are captured. The integration of the Long Short-Term Memory Network (LSTM) further enhances the model's ability to understand sequential dependencies in the feature representations. This comprehensive approach allows DuGEL to achieve a high level of accuracy in predicting potential microbe-disease associations, making it a valuable tool for biomedical research and the discovery of new therapeutic targets. By combining advanced graph-based and sequence-based learning techniques, DuGEL addresses the limitations of existing methods and provides a robust framework for the prediction of microbe-disease associations. To evaluate the performance of DuGEL, we conducted comprehensive comparative experiments and case studies based on two databases, HMDAD, and Disbiome to demonstrate that DuGEL can effectively predict potential microbe-disease associations.

Network Pharmacology-driven therapeutic interventions for Interstitial Lung Diseases using Traditional medicines: A Narrative Review

This review explores the progressive domain of network pharmacology and its potential to revolutionize therapeutic approaches for Interstitial Lung Diseases (ILDs), a collective term encompassing Interstitial Pneumonia, Pneumoconiosis, Connective Tissue Disease-related ILDs, and Sarcoidosis. The exploration focuses on the profound legacy of traditional medicines, particularly Ayurveda and Traditional Chinese Medicines (TCM), and their largely unexplored capacity in ILD treatment. These ancient healing systems, characterized by their holistic methodologies and multifaceted treatment modalities, offer a promising foundation for discovering innovative therapeutic strategies. Moreover, the review underscores the amalgamation of artificial intelligence (AI) and machine learning (ML) methodologies with bioinformatics, creating a computational synergy capable of deciphering the intricate biological networks associated with ILDs. Network pharmacology has tailored the hypothesis from the conventional "one target, one drug" towards a "network target, multi-component therapeutics" approach. The fusion of traditional literature and computational technology can unveil novel drugs, targets, and pathways, augmenting effective therapies and diminishing adverse effects related to current medications. In conclusion, this review provides a comprehensive exposition of how Network Pharmacology tools can leverage the insights of Ayurveda and TCM to craft efficacious therapeutic solutions for ILDs. It sets the stage for future investigations in this captivating interdisciplinary domain, validating the use of traditional medicines worldwide.

Exploring the therapeutic potential of Xiangsha Liujunzi Wan in Crohn's disease: from network pharmacology approach to experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

Xiangsha Liujunzi Wan (LJZW) is a traditional Chinese medicine (TCM) formula containing a variety of traditional Chinese herb components. Its principal components are often used in the treatment of gastrointestinal diseases and contribute to the treatment of Crohn's disease (CD).

AIM OF THE STUDY

To explore the therapeutic potential of LJZW in CD through network pharmacology, bioinformatics, molecular docking, and experimental verification.

METHODS

The principal bioactive components and corresponding targets of LJZW were ascertained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Potential targets for CD were identified in GeneCards, OMIM, DrugBank, DisGeNET, CTD, and Gene Expression Omnibus (GEO) databases. Intersection targets of LJZW and CD were identified using a Venn diagram and visualized using Cytoscape 3.8.0 to construct a protein-protein interaction (PPI) network. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed to assess the function of intersection targets. AutoDockTools and PyMOL were used for molecular docking to recognize the association between the core ingredients of LJZW and the core targets of CD. Subsequently, a series of experiments were conducted for validation.

RESULTS

The network pharmacology results indicated that there were 156 bioactive components and 268 corresponding targets for LJZW, 3023 primary relevant targets for CD, and 169 intersection targets for LJZW and CD. The PPI network was employed to identify five hub genes and six clusters. The GO functional analysis indicated that intersection targets are primarily correlated with oxidative stress and inflammatory responses. KEGG pathway analysis revealed that these targets were primarily associated with the phosphotylinosital 3 kinase (PI3K)-protein kinase B (AKT) and mitogen-activated protein kinase (MAPK) signaling pathways. The molecular docking results showed that the core ingredients of LJZW had good binding ability with the core targets of CD. A series of experiments demonstrated that LJZW could effectively attenuate TNBS-induced colitis symptoms, inhibit the inflammatory response, and protect intestinal barrier function by inhibiting the PI3K-AKT and MAPK signaling pathways, thus preventing and treating CD.

CONCLUSION

LJZW has the characteristics of multi-component, multi-target, and multi-pathway treatment, which helps to improve the treatment of CD, protect the intestinal barrier, and exert the effect of anti-inflammatory therapy by inhibiting PI3K-AKT and MAPK signaling pathways.

Next-generation pediatric care: nanotechnology-based and AI-driven solutions for cardiovascular, respiratory, and gastrointestinal disorders

BACKGROUND

Global pediatric healthcare reveals significant morbidity and mortality rates linked to respiratory, cardiac, and gastrointestinal disorders in children and newborns, mostly due to the complexity of therapeutic management in pediatrics and neonatology, owing to the lack of suitable dosage forms for these patients, often rendering them "therapeutic orphans". The development and application of pediatric drug formulations encounter numerous challenges, including physiological heterogeneity within age groups, limited profitability for the pharmaceutical industry, and ethical and clinical constraints. Many drugs are used unlicensed or off-label, posing a high risk of toxicity and reduced efficacy. Despite these circumstances, some regulatory changes are being performed, thus thrusting research innovation in this field.

DATA SOURCES

Up-to-date peer-reviewed journal articles, books, government and institutional reports, data repositories and databases were used as main data sources.

RESULTS

Among the main strategies proposed to address the current pediatric care situation, nanotechnology is specially promising for pediatric respiratory diseases since they offer a non-invasive, versatile, tunable, site-specific drug release. Tissue engineering is in the spotlight as strategy to address pediatric cardiac diseases, together with theragnostic systems. The integration of nanotechnology and theragnostic stands poised to refine and propel nanomedicine approaches, ushering in an era of innovative and personalized drug delivery for pediatric patients. Finally, the intersection of drug repurposing and artificial intelligence tools in pediatric healthcare holds great potential. This promises not only to enhance efficiency in drug development in general, but also in the pediatric field, hopefully boosting clinical trials for this population.

CONCLUSIONS

Despite the long road ahead, the deepening of nanotechnology, the evolution of tissue engineering, and the combination of traditional techniques with artificial intelligence are the most recently reported strategies in the specific field of pediatric therapeutics.

Protocol for identifying key genes using network-based approach as an alternative to differential expression analysis

In a variety of biological contexts, characterizing genes associated with disease etiology and mediating global transcriptomic change is a key initial step. Here, we present a protocol to identify such key genes using our tool "PathExt," a tool that implements a network-based approach. We describe steps for installing libraries, preparing input data and detailed procedures for running PathExt, and characterizing differential pathways and key genes based on ripple centrality scores. For complete details on the use and execution of this protocol, please refer to Agrawal et al.1,2.

Drug-target prediction through self supervised learning with dual task ensemble approach

Drug-Target interaction (DTI) prediction, a transformative approach in pharmaceutical research, seeks novel therapeutic applications for computational method based virtual screening, existing drugs to address untreated diseases and discovery of existing drugs side effects. The proposed model predict DTI through Heterogeneous biological network by combining drug, genes and disease related knowledge. For the purpose of embedding extraction Self-supervised learning (SSL) has been used which, trains models through pretext tasks, eliminating the need for manual annotations. The pretext tasks are related to either structural based information or similarity based information. To mitigate GNN vulnerability to non-robustness, ensemble learning can be incorporated into GNNs, harnessing multiple models to enhance robustness. This paper introduces a Graph neural network based architecture consisting of task based module and ensemble module for link prediction of DTI. The ensemble module of dual task combinations, both in cold start and warm start scenarios achieve very good performance as it provide 0.960 in cold start and 0.970 in warm start mean AUCROC score with less deviation.

Shenmai injection improves lipid metabolism in post-myocardial infarction heart failure based on network pharmacology and experimental validation

Background

Shenmai injection (SMI), a traditional Chinese medicine formulation derived from the herbal decoction Shenmai Yin, is widely used in treating cardiovascular disorders. This study extensively investigated the effects and mechanisms of action of SMI on lipid metabolism in post-myocardial infarction heart failure (pMIHF).

Methods

Network pharmacology was employed to predict the key targets and associated pathways involved in lipid metabolism for potential SMI treatments in post-myocardial infarction heart failure (pMIHF). Subsequently, a pMIHF mouse model and an ischemia/reperfusion (I/R) cell model were established to delve deeper into and validate the underlying mechanism of action.

Results

We performed network pharmacology analysis, which identified 48 active components in SMI and 201 common gene targets. Subsequent screening using the protein-protein interaction network identified 26 core targets, including interleukin (IL)-6, tumor necrosis factor (TNF)-α, peroxisome proliferator-activated receptor alpha (PPARα), and sirtuin 1 (SIRT1). Based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses, we predicted that SMI might act on lipid metabolism in pMIHF through the PPARα pathway, a hypothesis supported by the strong binding affinity between this receptor and the active components of SMI, as confirmed via molecular docking. In a left anterior descending artery-ligation mouse model, SMI significantly improved cardiac function, reduced serum free fatty acid levels, decreased inflammatory cell infiltration and myocardial fibrosis, and maintained myocardial mitochondrial morphology. In ischemia-reperfusion (I/R) cells, SMI reduced cell apoptosis, improved mitochondrial membrane potential, and decreased mRNA expression levels of IL-6 and TNF-α, while increasing protein levels of PPARα, SIRT1, and PPARα co-activator-1 alpha (PGC1α).

Conclusion

Collectively, our findings suggest that SMI enhances myocardial lipid metabolism and ameliorates pMIHF by upregulating the PPARα/SIRT1/PGC1α pathway.

Exploring the potential pharmacological mechanism of aripiprazole against hyperprolactinemia based on network pharmacology and molecular docking

The current primary therapeutic approach for schizophrenia is antipsychotic medication, and antipsychotic-induced hyperprolactinemia occurs in 40-80% of patients with schizophrenia. Aripiprazole, an atypical antipsychotic belonging to the quinolinone derivative class, can reduce the likelihood of developing hyperprolactinemia, but the pharmacological mechanisms of this reduction are unknown. This study aimed to explore the molecular mechanism of action of aripiprazole in treating hyperprolactinemia based on network pharmacology and molecular docking techniques. This study identified a total of 151 potential targets for aripiprazole from the DrugBank, TCMSP, BATMAN-TCM, TargetNet, and SwissTargetPrediction databases. Additionally, 71 hyperprolactinemia targets were obtained from the PharmGKB, DrugBank, TTD, GeneCards, OMIM, and DisGENET databases. Utilizing Venny 2.1.0 software, an intersection of 27 genes was identified between aripiprazole and hyperprolactinemia. To construct a common target protein-protein interaction (PPI) network, the common targets obtained from both sources were input into the STRING database. The resulting PPI network was then imported into Cytoscape 3.7.2 software, which identified eight core targets associated with aripiprazole's treatment of hyperprolactinemia. Subsequently, a PPI network was established for these targets. Enrichment analysis of the key targets was conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes in the DAVID database. Additionally, molecular docking verification of the interaction between aripiprazole and the core targets was performed using AutoDock Vina software. Aripiprazole's intervention in hyperprolactinemia primarily targets the following core proteins: Solute Carrier Family 6 Member 3 (SLC6A3), monoamine oxidase (MAO-B), Dopamine D2 receptor (DRD2), 5-hydroxytryptamine (serotonin) receptor 2A (HTR2A), 5-hydroxytryptamine (serotonin) receptor 2C (HTR2C), cytochrome P450 2D6 (CYP2D6), Dopamine D1 receptor (DRD1), Dopamine D4 receptor (DRD4). These targets are predominantly involved in biological processes such as the adenylate cyclase-activating adrenergic receptor signaling pathway, G-protein coupled receptor signaling pathway coupled to cyclic nucleotide second messenger, phospholipase C-activating G-protein coupled receptor signaling pathway, chemical synaptic transmission, and response to xenobiotic stimulus. Primary enrichment occurs in signaling pathways such as the neuroactive ligand-receptor interaction and serotonergic synapse pathways. Molecular docking results demonstrate a favorable affinity between aripiprazole and the core target proteins MAO-B, DRD2, SLC6A3, HTR2C, HTR2A, CYP2D6, DRD4, and DRD1. Network pharmacology predicted potential targets and signaling pathways for aripiprazole's intervention in hyperprolactinemia, offering theoretical support and a reference basis for optimizing clinical strategies and drug development involving aripiprazole.

Advancing Efficacy Prediction for EHR-based Emulated Trials in Repurposing Heart Failure Therapies

Introduction

The High mortality rates associated with heart failure (HF) have propelled the strategy of drug repurposing, which seeks new therapeutic uses for existing, approved drugs to enhance the management of HF symptoms effectively. An emerging trend focuses on utilizing real-world data, like EHR, to mimic randomized controlled trials (RCTs) for evaluating treatment outcomes through what are known as emulated trials (ET). Nonetheless, the intricacies inherent in EHR data-comprising detailed patient histories in databases, the omission of certain biomarkers or specific diagnostic tests, and partial records of symptoms-introduce notable discrepancies between EHR data and the stringent standards of RCTs. This gap poses a substantial challenge in conducting an ET to accurately predict treatment efficacy.

Objective

The objective of this research is to predict the efficacy of drugs repurposed for HF in randomized trials by leveraging EHR in ET.

Methods

We proposed an ET framework to predict drug efficacy, integrating target prediction based on biomedical databases with statistical analysis using EHR data. Specifically, we developed a novel target prediction model that learns low-dimensional representations of drug molecules, protein sequences, and diverse biomedical associations from a knowledge graph. Additionally, we crafted strategies to improve the prediction by considering the interactions between HF drugs and biological factors in the context of HF prognostic markers.

Results

Our validation of the drug-target prediction model against the BETA benchmark demonstrated superior performance, with an average AUCROC of 97.7%, PRAUC of 97.4%, F1 score of 93.1%, and a General Score of 96.1%, surpassing existing baseline algorithms. Further analysis of our ET framework on identifying 17 repurposed drugs-derived from 266 phase 3 HF RCTs-using data from 59,000 patients at the Mayo Clinic highlighted the framework's remarkable predictive accuracy. This analysis took into account various factors such as biological variables (e.g., gender, age, ethnicity), HF medications (e.g., ACE inhibitors, Beta-blockers, ARBs, Loop Diuretics), types of HF (HFpEF and HFrEF), confounders, and prognostic markers (e.g., NT-proBNP, bUn, creatinine, and hemoglobin). The ET framework significantly improved the accuracy compared to the baseline efficacy analysis that utilized EHR data. Notably, the best results were improved in AUC-ROC from 75.71% to 93.57% and in PRAUC from 78.66% to 90.34%, compared to the baseline models.

Conclusion

Our study presents an ET framework that significantly enhances drug efficacy emulation by integrating EHR-based analysis with target prediction. We demonstrated substantial success in predicting the efficacy of 17 HF drugs repurposed for phase 3 RCTs, showcasing the framework's potential in advancing HF treatment strategies.

Molecular mechanism of Dang-Shen-Yu-Xing decoction against Mycoplasma bovis pneumonia based on network pharmacology, molecular docking, molecular dynamics simulations and experimental verification

Mycoplasma bovis pneumonia is a highly contagious respiratory infection caused by Mycoplasma bovis. It is particularly prevalent in calves, posing a significant threat to animal health and leading to substantial economic losses. Dang-Shen-Yu-Xing decoction is often used to treat this condition in veterinary clinics. It exhibits robust anti-inflammatory effects and can alleviate pulmonary fibrosis. However, its mechanism of action remains unclear. Therefore, this study aimed to preliminarily explore the molecular mechanism of Dang-Shen-Yu-Xing decoction for treating mycoplasma pneumonia in calves through a combination of network pharmacology, molecular docking, molecular dynamics simulation methods, and experimental validation. The active components and related targets of Dang-Shen-Yu-Xing decoction were extracted from several public databases. Additionally, complex interactions between drugs and targets were explored through network topology, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Subsequently, the binding affinity of drug to disease-related targets was verified through molecular docking and molecular dynamics simulation. Finally, the pharmacodynamics were verified via animal experiments. The primary network topology analysis revealed two core targets and 10 key active components of Dang-Shen-Yu-Xing decoction against Mycoplasma bovis pneumonia. Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that the mechanism of Dang-Shen-Yu-Xing decoction for treating mycoplasma bovis pneumonia involved multiple signaling pathways, with the main pathways including PI3K-Akt and IL17 signaling pathways. Moreover, molecular docking predicted the binding affinity and conformation of the core targets of Dang-Shen-Yu-Xing decoction, IL6, and IL10, with the associated main active ingredients. The results showed a strong binding of the active ingredients to the hub target. Further, molecular docking dynamics simulation revealed three key active components of IL10 induced by Dang-Shen-Yu-Xing decoction against Mycoplasma bovis pneumonia. Finally, animal experiments confirmed Dang-Shen-Yu-Xing decoction pharmacodynamics, suggesting that it holds potential as an alternative therapy for treating mycoplasma bovis pneumonia.

Investigating the potential mechanism and therapeutic effects of SLXG for cholesterol gallstone treatment

BACKGROUND

Shugan Lidan Xiaoshi Granules (SLXG) is a traditional Chinese medicine (TCM) formulation frequently employed to prevent and treat cholesterol gallstones. SLXG is formulated based on the Chaihu Shugan Formula found in an ancient Chinese medical book, a traditional remedy in China for centuries, and has demonstrated successful treatment of numerous patients with gallbladder stones.

PURPOSE

This research sought to clarify the therapeutic impact and molecular mechanisms of SLXG and its active components in the treatment of cholesterol gallbladder stones.

METHODS

The study employed network pharmacology, UPLC-HRMS transcriptome sequencing, animal model experiments, molecular docking, and Surface Plasmon Resonance (SPR) to explore the molecular mechanisms of SLXG and its relationship with Traditional Chinese Medicines (TCMs) and potential targets. Furthermore, PPI network analysis, along with GO and KEGG enrichment analyses, were performed to explore the potential mechanisms through which SLXG and its active ingredient, naringenin, prevent and treat cholesterol gallstones. The mechanism of action was further elucidated using an animal model for gallbladder stone formation.

RESULTS

The study employed a network pharmacology and UPLC-HRMS to investigate the active compounds of SLXG for the treatment of cholesterol gallbladder stones, and subsequently constructed a network of therapeutic targets of SLXG. The results from gene enrichment analyses indicated that SLXG targets the metabolic pathway of bile secretion and the cholesterol metabolism pathway in addressing cholesterol gallbladder stones. The molecular docking results confirmed the interaction between the genes enriched in the pathways and the active ingredients in SLXG. Transcriptome sequencing results demonstrated that SLXG exerts its therapeutic effect on gallstones by regulating cholesterol and bile acid synthesis and metabolism. Furthermore, animal model experiments and SPR provided evidence that SLXG and its active ingredient, naringenin, exert therapeutic effects on cholesterol gallbladder stones by targeting the genes HMGCR, SOAT2, and UGT1A1, and influencing substances associated with cholesterol synthesis and metabolism.

CONCLUSIONS

Using systematic network pharmacology methods combined with in vivo validation experiments, we uncovered the fundamental pharmacological effects and potential mechanisms of SLXG and its active ingredient, naringenin, in the treatment of cholesterol gallstones. This research underscores the valuable role that traditional remedies can play in addressing medical challenges and suggests a promising direction for further exploration of natural treatments for the disease.

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

CHL-DTI: A Novel High-Low Order Information Convergence Framework for Effective Drug-Target Interaction Prediction

Recognizing drug-target interactions (DTI) stands as a pivotal element in the expansive field of drug discovery. Traditional biological wet experiments, although valuable, are time-consuming and costly as methods. Recently, computational methods grounded in network learning have demonstrated great advantages by effective topological feature extraction and attracted extensive research attention. However, most existing network-based learning methods only consider the low-order binary correlation between individual drug and target, neglecting the potential higher-order correlation information derived from multiple drugs and targets. High-order information, as an essential component, exhibits complementarity with low-order information. Hence, the incorporation of higher-order associations between drugs and targets, while adequately integrating them with the existing lower-order information, could potentially yield substantial breakthroughs in predicting drug-target interactions. We propose a novel dual channels network-based learning model CHL-DTI that converges high-order information from hypergraphs and low-order information from ordinary graph for drug-target interaction prediction. The convergence of high-low order information in CHL-DTI is manifested in two key aspects. First, during the feature extraction stage, the model integrates both high-level semantic information and low-level topological information by combining hypergraphs and ordinary graph. Second, CHL-DTI fully fuse the innovative introduced drug-protein pairs (DPP) hypergraph network structure with ordinary topological network structure information. Extensive experimentation conducted on three public datasets showcases the superior performance of CHL-DTI in DTI prediction tasks when compared to SOTA methods. The source code of CHL-DTI is available at https://github.com/UPCLyy/CHL-DTI .

Transplanting network pharmacology technology into food science research: A comprehensive review on uncovering food-sourced functional factors and their health benefits

Network pharmacology is an emerging interdisciplinary research method. The application of network pharmacology to reveal the nutritional effects and mechanisms of active ingredients in food is of great significance in promoting the development of functional food, facilitating personalized nutrition, and exploring the mechanisms of food health effects. This article systematically reviews the application of network pharmacology in the field of food science using a literature review method. The application progress of network pharmacology in food science is discussed, and the mechanisms of functional factors in food on the basis of network pharmacology are explored. Additionally, the limitations and challenges of network pharmacology are discussed, and future directions and application prospects are proposed. Network pharmacology serves as an important tool to reveal the mechanisms of action and health benefits of functional factors in food. It helps to conduct in-depth research on the biological activities of individual ingredients, composite foods, and compounds in food, and assessment of the potential health effects of food components. Moreover, it can help to control and enhance their functionality through relevant information during the production and processing of samples to guarantee food safety. The application of network pharmacology in exploring the mechanisms of functional factors in food is further analyzed and summarized. Combining machine learning, artificial intelligence, clinical experiments, and in vitro validation, the achievement transformation of functional factor in food driven by network pharmacology is of great significance for the future development of network pharmacology research.

The Art of Finding the Right Drug Target: Emerging Methods and Strategies

Drug targets are specific molecules in biological tissues and body fluids that interact with drugs. Drug target discovery is a key component of drug discovery and is essential for the development of new drugs in areas such as cancer therapy and precision medicine. Traditional in vitro or in vivo target discovery methods are time-consuming and labor-intensive, limiting the pace of drug discovery. With the development of modern discovery methods, the discovery and application of various emerging technologies have greatly improved the efficiency of drug discovery, shortened the cycle time, and reduced the cost. This review provides a comprehensive overview of various emerging drug target discovery strategies, including computer-assisted approaches, drug affinity response target stability, multiomics analysis, gene editing, and nonsense-mediated mRNA degradation, and discusses the effectiveness and limitations of the various approaches, as well as their application in real cases. Through the review of the aforementioned contents, a general overview of the development of novel drug targets and disease treatment strategies will be provided, and a theoretical basis will be provided for those who are engaged in pharmaceutical science research. SIGNIFICANCE STATEMENT: Target-based drug discovery has been the main approach to drug discovery in the pharmaceutical industry for the past three decades. Traditional drug target discovery methods based on in vivo or in vitro validation are time-consuming and costly, greatly limiting the development of new drugs. Therefore, the development and selection of new methods in the drug target discovery process is crucial.

Exploring potential therapeutic combinations for castration-sensitive prostate cancer using supercomputers: a proof of concept study

To address the challenge of finding new combination therapies against castration-sensitive prostate cancer, we introduce Vini, a computational tool that predicts the efficacy of drug combinations at the intracellular level by integrating data from the KEGG, DrugBank, Pubchem, Protein Data Bank, Uniprot, NCI-60 and COSMIC databases. Vini is a computational tool that predicts the efficacy of drugs and their combinations at the intracellular level. It addresses the problem comprehensively by considering all known target genes, proteins and small molecules and their mutual interactions involved in the onset and development of cancer. The results obtained point to new, previously unexplored combination therapies that could theoretically be promising candidates for the treatment of castration-sensitive prostate cancer and could prevent the inevitable progression of the cancer to the incurable castration-resistant stage. Furthermore, after analyzing the obtained triple combinations of drugs and their targets, the most common targets became clear: ALK, BCL-2, mTOR, DNA and androgen axis. These results may help to define future therapies against castration-sensitive prostate cancer. The use of the Vini computer model to explore therapeutic combinations represents an innovative approach in the search for effective treatments for castration-sensitive prostate cancer, which, if clinically validated, could potentially lead to new breakthrough therapies.

Naringenin modulates the NO‑cGMP‑PKG signaling pathway by binding to AKT to enhance osteogenic differentiation in hPDLSCs

Naringenin (NAR) is a prominent flavanone that has been recognized for its capacity to promote the osteogenic differentiation of human periodontal ligament stem cells (hPDLSCs). The present study aimed to explore how NAR promotes the osteogenic differentiation of hPDLSCs and to assess its efficacy in repairing alveolar bone defects. For this purpose, a protein‑protein interaction network of NAR action was established by mRNA sequencing and network pharmacological analysis. Gene and protein expression levels were evaluated by reverse transcription‑quantitative and western blotting. Alizarin red and alkaline phosphatase staining were also employed to observe the osteogenic capacity of hPDLSCs, and immunofluorescence was used to examine the co‑localization of NAR molecular probes and AKT in cells. The repair of mandibular defects was assessed by micro‑computed tomography (micro‑CT), Masson staining and immunofluorescence. Additionally, computer simulation docking software was utilized to determine the binding affinity of NAR to the target protein, AKT. The results demonstrated that activation of the nitric oxide (NO)‑cyclic guanosine monophosphate (cGMP)‑protein kinase G (PKG) signaling pathway could promote the osteogenic differentiation of hPDLSCs. Inhibition of AKT, endothelial nitric oxide synthase and soluble guanylate cyclase individually attenuated the ability of NAR to promote the osteogenic differentiation of hPDLSCs. Micro‑CT and Masson staining revealed that the NAR gavage group exhibited more new bone formation at the defect site. Immunofluorescence assays confirmed the upregulated expression of Runt‑related transcription factor 2 and osteopontin in the NAR gavage group. In conclusion, the results of the present study suggested that NAR promotes the osteogenic differentiation of hPDLSCs by activating the NO‑cGMP‑PKG signaling pathway through its binding to AKT.

The Gene Network Correlation Analysis of Obesity to Type 1 Diabetes and Cardiovascular Disorders: An Interactome-Based Bioinformatics Approach

The current study focuses on the importance of Protein-Protein Interactions (PPIs) in biological processes and the potential of targeting PPIs as a new treatment strategy for diseases. Specifically, the study explores the cross-links of PPIs network associated with obesity, type 1 diabetes mellitus (T1DM), and cardiac disease (CD), which is an unexplored area of research. The research aimed to understand the role of highly connected proteins in the network and their potential as drug targets. The methodology for this research involves retrieving genes from the NCBI online gene database, intersecting genes among three diseases (type 1 diabetes, obesity, and cardiovascular) using Interactivenn, determining suitable drug molecules using NetworkAnalyst, and performing various bioinformatics analyses such as Generic Protein-Protein Interactions, topological properties analysis, function enrichment analysis in terms of GO, and Kyoto Encyclopedia of Genes and Genomes (KEGG), gene co-expression network, and protein drug as well as protein chemical interaction network. The study focuses on human subjects. The results of this study identified 12 genes [VEGFA (Vascular Endothelial Growth Factor A), IL6 (Interleukin 6), MTHFR (Methylenetetrahydrofolate reductase), NPPB (Natriuretic Peptide B), RAC1 (Rac Family Small GTPase 1), LMNA (Lamin A/C), UGT1A1 (UDP-glucuronosyltransferase family 1 membrane A1), RETN (Resistin), GCG (Glucagon), NPPA (Natriuretic Peptide A), RYR2 (Ryanodine receptor 2), and PRKAG2 (Protein Kinase AMP-Activated Non-Catalytic Subunit Gamma 2)] that were shared across the three diseases and could be used as key proteins for protein-drug/chemical interaction. Additionally, the study provides an in-depth understanding of the complex molecular and biological relationships between the three diseases and the cellular mechanisms that lead to their development. Potentially significant implications for the therapy and management of various disorders are highlighted by the findings of this study by improving treatment efficacy, simplifying treatment regimens, cost-effectiveness, better understanding of the underlying mechanism of these diseases, early diagnosis, and introducing personalized medicine. In conclusion, the current study provides new insights into the cross-links of PPIs network associated with obesity, T1DM, and CD, and highlights the potential of targeting PPIs as a new treatment strategy for these prevalent diseases.

Exploring the therapeutic potential of garlic in alcoholic liver disease: a network pharmacology and experimental validation study

OBJECTIVE

Employing network pharmacology and molecular docking, the study predicts the active compounds in garlic and elucidates their mechanism in inhibiting the development of alcoholic liver disease (ALD). ALD is a global chronic liver disease with potential for hepatocellular carcinoma progression.

METHODS

The main active ingredients and targets of garlic were identified through screening the TCMSP, TCM-ID, and ETCM databases. ALD disease targets were sourced from DisGeNET, GeneCards, and DiGSeE databases, and intervention targets for garlic were determined through intersections. Protein interaction networks were constructed using the STRING platform, and GO and KEGG pathway enrichment analyses were performed with R software. The garlic component-disease-target network was established using Cytoscape software. Validation of active ingredients against core targets was conducted through molecular docking simulations using AutoDock Vina software. Expression validation of core targets was carried out using human sequencing data of ALD obtained from the GEO database.

RESULTS

Integration of garlic drug targets with ALD disease targets identified 83 target genes. Validation through an alcohol-induced ALD mouse model supported certain network pharmacology findings, suggesting that garlic may impede disease progression by mitigating the inflammatory response and promoting ethanol metabolism.

CONCLUSION

This study provides insights into the potential therapeutic mechanisms of garlic in inhibiting ALD development. The identified active ingredients offer promising avenues for further investigation and development of treatments for ALD, emphasizing the importance of botanical remedies in liver disease management.

Dihydroartemisinin ameliorates experimental autoimmune myasthenia gravis by regulating CD4+ T cells and modulating gut microbiota

BACKGROUND

Dihydroartemisinin (DHA), a derivative and active metabolite of artemisinin, possesses various immunomodulatory properties. However, its role in myasthenia gravis (MG) has not been clearly explored. Here, we investigated the role of DHA in experimental autoimmune myasthenia gravis (EAMG) and its potential mechanisms.

METHODS

The AChR97-116 peptide-induced EAMG model was established in Lewis rats and treated with DHA. Flow cytometry was used to assess the release of Th cell subsets and Treg cells, and 16S rRNA gene amplicon sequence analysis was applied to explore the relationship between the changes in the intestinal flora after DHA treatment. In addition, network pharmacology and molecular docking were utilized to explore the potential mechanism of DHA against EAMG, which was further validated in the rat model by immunohistochemical and RT-qPCR for further validation.

RESULTS

In this study, we demonstrate that oral administration of DHA ameliorated clinical symptoms in rat models of EAMG, decreased the expression level of Th1 and Th17 cells, and increased the expression level of Treg cells. In addition, 16S rRNA gene amplicon sequence analysis showed that DHA restored gut microbiota dysbiosis in EAMG rats by decreasing Ruminococcus abundance and increasing the abundance of Clostridium, Bifidobacterium, and Allobaculum. Using network pharmacology, 103 potential targets of DHA related to MG were identified, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that PI3K-AKT signaling pathway was related to the treatment of DHA on EAMG. Meanwhile, molecular docking verified that DHA has good binding affinity to AKT1, CASP3, EGFR, and IGF1. Immunohistochemical staining showed that DHA treatment significantly inhibited the phosphorylated expression of AKT and PI3K in the spleen tissues of EAMG rats. In EAMG rats, RT-qPCR results also showed that DHA reduced the mRNA expression levels of PI3K and AKT1.

CONCLUSIONS

DHA ameliorated EAMG by inhibiting the PI3K-AKT signaling pathway, regulating CD4+ T cells and modulating gut microbiota, providing a novel therapeutic approach for the treatment of MG.

Plant antibacterials: The challenges and opportunities

Nature possesses an inexhaustible reservoir of agents that could serve as alternatives to combat the growing threat of antimicrobial resistance (AMR). While some of the most effective drugs for treating bacterial infections originate from natural sources, they have predominantly been derived from fungal and bacterial species. However, a substantial body of literature is available on the promising antibacterial properties of plant-derived compounds. In this comprehensive review, we address the major challenges associated with the discovery and development of plant-derived antimicrobial compounds, which have acted as obstacles preventing their clinical use. These challenges encompass limited sourcing, the risk of agent rediscovery, suboptimal drug metabolism, and pharmacokinetics (DMPK) properties, as well as a lack of knowledge regarding molecular targets and mechanisms of action, among other pertinent issues. Our review underscores the significance of these challenges and their implications in the quest for the discovery and development of effective plant-derived antimicrobial agents. Through a critical examination of the current state of research, we give valuable insights that will advance our understanding of these classes of compounds, offering potential solutions to the global crisis of AMR. © 2017 Elsevier Inc. All rights reserved.

Enzymatic Metabolic Switches of Astrocyte Response to Lipotoxicity as Potential Therapeutic Targets for Nervous System Diseases

Astrocytes play a pivotal role in maintaining brain homeostasis. Recent research has highlighted the significance of palmitic acid (PA) in triggering pro-inflammatory pathways contributing to neurotoxicity. Furthermore, Genomic-scale metabolic models and control theory have revealed that metabolic switches (MSs) are metabolic pathway regulators by potentially exacerbating neurotoxicity, thereby offering promising therapeutic targets. Herein, we characterized these enzymatic MSs in silico as potential therapeutic targets, employing protein-protein and drug-protein interaction networks alongside structural characterization techniques. Our findings indicate that five MSs (P00558, P04406, Q08426, P09110, and O76062) were functionally linked to nervous system drug targets and may be indirectly regulated by specific neurological drugs, some of which exhibit polypharmacological potential (e.g., Trifluperidol, Trifluoperazine, Disulfiram, and Haloperidol). Furthermore, four MSs (P00558, P04406, Q08426, and P09110) feature ligand-binding or allosteric cavities with druggable potential. Our results advocate for a focused exploration of P00558 (phosphoglycerate kinase 1), P04406 (glyceraldehyde-3-phosphate dehydrogenase), Q08426 (peroxisomal bifunctional enzyme, enoyl-CoA hydratase, and 3-hydroxyacyl CoA dehydrogenase), P09110 (peroxisomal 3-ketoacyl-CoA thiolase), and O76062 (Delta(14)-sterol reductase) as promising targets for the development or repurposing of pharmacological compounds, which could have the potential to modulate lipotoxic-altered metabolic pathways, offering new avenues for the treatment of related human diseases such as neurological diseases.

Drug repurposing for cancer therapy

Cancer, a complex and multifactorial disease, presents a significant challenge to global health. Despite significant advances in surgical, radiotherapeutic and immunological approaches, which have improved cancer treatment outcomes, drug therapy continues to serve as a key therapeutic strategy. However, the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects, and thus there remains a critical need to develop novel cancer therapeutics. One promising strategy that has received widespread attention in recent years is drug repurposing: the identification of new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective, proven to be safe, and can significantly expedite the drug development process due to their already established safety profiles. In light of this, the present review offers a comprehensive overview of the various methods employed in drug repurposing, specifically focusing on the repurposing of drugs to treat cancer. We describe the antitumor properties of candidate drugs, and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment. In addition, we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery. We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen. To conclude, we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application.

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

Exploring the effect and mechanism of cucurbitacin B on cholestatic liver injury based on network pharmacology and experimental verification

ETHNOPHARMACOLOGICAL RELEVANCE

Cholestatic liver injury (CLI) is a pathologic process with the impairment of liver and bile secretion and excretion, resulting in an excessive accumulation of bile acids within the liver, which leads to damage to both bile ducts and hepatocytes. This process is often accompanied by inflammation. Cucumis melo L is a folk traditional herb for the treatment of cholestasis. Cucurbitacin B (CuB), an important active ingredient in Cucumis melo L, has significant anti-inflamamatory effects and plays an important role in diseases such as neuroinflammation, skin inflammation, and chronic hepatitis. Though numerous studies have confirmed the significant therapeutic effect of CuB on liver diseases, the impact of CuB on CLI remains uncertain. Consequently, the objective of this investigation is to elucidate the therapeutic properties and potential molecular mechanisms underlying the effects of CuB on CLI.

AIM OF THE STUDY

The aim of this paper was to investigate the potential protective mechanism of CuB against CLI.

METHODS

First, the corresponding targets of CuB were obtained through the SwissTargetPrediction and SuperPre online platforms. Second, the DisGeNET database, GeneCards database, and OMIM database were utilized to screen therapeutic targets for CLI. Then, protein-protein interaction (PPI) was determined using the STRING 11.5 data platform. Next, the OmicShare platform was employed for the purpose of visualizing the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The molecular docking technique was then utilized to evaluate the binding affinity existing between potential targets and CuB. Subsequently, the impacts of CuB on the LO2 cell injury model induced by Lithocholic acid (LCA) and the CLI model induced by 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) were determined by evaluating inflammation in both in vivo and in vitro settings. The potential molecular mechanism was explored by real-time quantitative polymerase chain reaction (RT-qPCR) and western blot (WB) techniques.

RESULTS

A total of 122 CuB targets were collected and high affinity targets were identified through the PPI network, namely TLR4, STAT3, HIF1A, and NFKB1. GO and KEGG analyses indicated that the treatment of CLI with CuB chiefly involved the inflammatory pathway. In vitro study results showed that CuB alleviated LCA-induced LO2 cell damage. Meanwhile, CuB reduced elevated AST and ALT levels and the release of inflammatory factors in LO2 cells induced by LCA. In vivo study results showed that CuB could alleviate DDC-induced pathological changes in mouse liver, inhibit the activity of serum transaminase, and suppress the liver and systemic inflammatory reaction of mice. Mechanically, CuB downregulated the IL-6, STAT3, and HIF-1α expression and inhibited STAT3 phosphorylation.

CONCLUSION

By combining network pharmacology with in vivo and in vitro experiments, the results of this study suggested that CuB prevented the inflammatory response by inhibiting the IL-6/STAT3/HIF-1α signaling pathway, thereby demonstrating potential protective and therapeutic effects on CLI. These results establish a scientific foundation for the exploration and utilization of natural medicines for CLI.

Exploring the molecular mechanism of Xuebifang in the treatment of diabetic peripheral neuropathy based on bioinformatics and network pharmacology

Background

Xuebifang (XBF), a potent Chinese herbal formula, has been employed in managing diabetic peripheral neuropathy (DPN). Nevertheless, the precise mechanism of its action remains enigmatic.

Purpose

The primary objective of this investigation is to employ a bioinformatics-driven approach combined with network pharmacology to comprehensively explore the therapeutic mechanism of XBF in the context of DPN.

Study design and Methods

The active chemicals and their respective targets of XBF were sourced from the TCMSP and BATMAN databases. Differentially expressed genes (DEGs) related to DPN were obtained from the GEO database. The targets associated with DPN were compiled from the OMIM, GeneCards, and DrugBank databases. The analysis of GO, KEGG pathway enrichment, as well as immuno-infiltration analysis, was conducted using the R language. The investigation focused on the distribution of therapeutic targets of XBF within human organs or cells. Subsequently, molecular docking was employed to evaluate the interactions between potential targets and active compounds of XBF concerning the treatment of DPN.

Results

The study successfully identified a total of 122 active compounds and 272 targets associated with XBF. 5 core targets of XBF for DPN were discovered by building PPI network. According to GO and KEGG pathway enrichment analysis, the mechanisms of XBF for DPN could be related to inflammation, immune regulation, and pivotal signalling pathways such as the TNF, TLR, CLR, and NOD-like receptor signalling pathways. These findings were further supported by immune infiltration analysis and localization of immune organs and cells. Moreover, the molecular docking simulations demonstrated a strong binding affinity between the active chemicals and the carefully selected targets.

Conclusion

In summary, this study proposes a novel treatment model for XBF in DPN, and it also offers a new perspective for exploring the principles of traditional Chinese medicine (TCM) in the clinical management of DPN.

Network-Based Methods and Their Applications in Drug Discovery

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

mtADENet: A novel interpretable method integrating multiple types of network-based inference approaches for prediction of adverse drug events

Identification of adverse drug events (ADEs) is crucial to reduce human health risks and accelerate drug safety assessment. ADEs are mainly caused by unintended interactions with primary or additional targets (off-targets). In this study, we proposed a novel interpretable method named mtADENet, which integrates multiple types of network-based inference approaches for ADE prediction. Different from phenotype-based methods, mtADENet introduced computational target profiles predicted by network-based methods to bridge the gap between chemical structures and ADEs, and hence can not only predict ADEs for drugs and novel compounds within or outside the drug-ADE association network, but also provide insights for the elucidation of molecular mechanisms of the ADEs caused by drugs. We constructed a series of network-based prediction models for 23 ADE categories. These models achieved high AUC values ranging from 0.865 to 0.942 in 10-fold cross validation. The best model further showed high performance on four external validation sets, which outperformed two previous network-based methods. To show the practical value of mtADENet, we performed case studies on developmental neurotoxicity and cardio-oncology, and over 50 % of predicted ADEs and targets for drugs and novel compounds were validated by literature. Moreover, mtADENet is freely available at our web server named NetInfer (http://lmmd.ecust.edu.cn/netinfer/). In summary, mtADENet would be a powerful tool for ADE prediction and drug safety assessment in drug discovery and development.

In silico Approaches for Exploring the Pharmacological Activities of Benzimidazole Derivatives: A Comprehensive Review

BACKGROUND

This article reviews computational research on benzimidazole derivatives. Cytotoxicity for all compounds against cancer cell lines was measured and the results revealed that many compounds exhibited high inhibitions. This research examines the varied pharmacological properties like anticancer, antibacterial, antioxidant, anti-inflammatory and anticonvulsant activities of benzimidazole derivatives. The suggested method summarises In silico research for each activity. This review examines benzimidazole derivative structure-activity relationships and pharmacological effects. In silico investigations can anticipate structural alterations and their effects on these derivative's pharmacological characteristics and efficacy through many computational methods. Molecular docking, molecular dynamics simulations and virtual screening help anticipate pharmacological effects and optimize chemical design. These trials will improve lead optimization, target selection, and ADMET property prediction in drug development. In silico benzimidazole derivative studies will be assessed for gaps and future research. Prospective studies might include empirical verification, pharmacodynamic analysis, and computational methodology improvement.

OBJECTIVES

This review discusses benzimidazole derivative In silico research to understand their specific pharmacological effects. This will help scientists design new drugs and guide future research.

METHODS

Latest, authentic and published reports on various benzimidazole derivatives and their activities are being thoroughly studied and analyzed.

RESULT

The overview of benzimidazole derivatives is more comprehensive, highlighting their structural diversity, synthetic strategies, mechanisms of action, and the computational tools used to study them.

CONCLUSION

In silico studies help to understand the structure-activity relationship (SAR) of benzimidazole derivatives. Through meticulous alterations of substituents, ring modifications, and linker groups, this study identified the structural factors influencing the pharmacological activity of benzimidazole derivatives. These findings enable the rational design and optimization of more potent and selective compounds.

Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview

Drug-target interactions (DTIs) are an important part of the drug development process. When the drug (a chemical molecule) binds to a target (proteins or nucleic acids), it modulates the biological behavior/function of the target, returning it to its normal state. Predicting DTIs plays a vital role in the drug discovery (DD) process as it has the potential to enhance efficiency and reduce costs. However, DTI prediction poses significant challenges and expenses due to the time-consuming and costly nature of experimental assays. As a result, researchers have increased their efforts to identify the association between medications and targets in the hopes of speeding up drug development and shortening the time to market. This paper provides a detailed discussion of the initial stage in drug discovery, namely drug-target interactions. It focuses on exploring the application of machine learning methods within this step. Additionally, we aim to conduct a comprehensive review of relevant papers and databases utilized in this field. Drug target interaction prediction covers a wide range of applications: drug discovery, prediction of adverse effects and drug repositioning. The prediction of drugtarget interactions can be categorized into three main computational methods: docking simulation approaches, ligand-based methods, and machine-learning techniques.

Mining Chemogenomic Spaces for Prediction of Drug-Target Interactions

The pipeline of drug discovery consists of a number of processes; drug-target interaction determination is one of the salient steps among them. Computational prediction of drug-target interactions can facilitate in reducing the search space of experimental wet lab-based verifications steps, thus considerably reducing time and other resources dedicated to the drug discovery pipeline. While machine learning-based methods are more widespread for drug-target interaction prediction, network-centric methods are also evolving. In this chapter, we focus on the process of the drug-target interaction prediction from the perspective of using machine learning algorithms and the various stages involved for developing an accurate predictor.

Multifunctional nanoparticle-mediated combining therapy for human diseases

Combining existing drug therapy is essential in developing new therapeutic agents in disease prevention and treatment. In preclinical investigations, combined effect of certain known drugs has been well established in treating extensive human diseases. Attributed to synergistic effects by targeting various disease pathways and advantages, such as reduced administration dose, decreased toxicity, and alleviated drug resistance, combinatorial treatment is now being pursued by delivering therapeutic agents to combat major clinical illnesses, such as cancer, atherosclerosis, pulmonary hypertension, myocarditis, rheumatoid arthritis, inflammatory bowel disease, metabolic disorders and neurodegenerative diseases. Combinatorial therapy involves combining or co-delivering two or more drugs for treating a specific disease. Nanoparticle (NP)-mediated drug delivery systems, i.e., liposomal NPs, polymeric NPs and nanocrystals, are of great interest in combinatorial therapy for a wide range of disorders due to targeted drug delivery, extended drug release, and higher drug stability to avoid rapid clearance at infected areas. This review summarizes various targets of diseases, preclinical or clinically approved drug combinations and the development of multifunctional NPs for combining therapy and emphasizes combinatorial therapeutic strategies based on drug delivery for treating severe clinical diseases. Ultimately, we discuss the challenging of developing NP-codelivery and translation and provide potential approaches to address the limitations. This review offers a comprehensive overview for recent cutting-edge and challenging in developing NP-mediated combination therapy for human diseases.

Diarylpentanoids, the privileged scaffolds in antimalarial and anti-infectives drug discovery: A review

Asia is a hotspot for infectious diseases, including malaria, dengue fever, tuberculosis, and the pandemic COVID-19. Emerging infectious diseases have taken a heavy toll on public health and the economy and have been recognized as a major cause of morbidity and mortality, particularly in Southeast Asia. Infectious disease control is a major challenge, but many surveillance systems and control strategies have been developed and implemented. These include vector control, combination therapies, vaccine development, and the development of new anti-infectives. Numerous newly discovered agents with pharmacological anti-infective potential are being actively and extensively studied for their bioactivity, toxicity, selectivity, and mode of action, but many molecules lose their efficacy over time due to resistance developments. These facts justify the great importance of the search for new, effective, and safe anti-infectives. Diarylpentanoids, a curcumin derivative, have been developed as an alternative with better bioavailability and metabolism as a therapeutic agent. In this review, the mechanisms of action and potential targets of antimalarial drugs as well as the classes of antimalarial drugs are presented. The bioactivity of diarylpentanoids as a potential scaffold for a new class of anti-infectives and their structure-activity relationships are also discussed in detail.

ResBiGAAT: Residual Bi-GRU with attention for protein-ligand binding affinity prediction

Protein-ligand interaction plays a crucial role in drug discovery, facilitating efficient drug development and enabling drug repurposing. Several computational algorithms, such as Graph Neural Networks and Convolutional Neural Networks, have been proposed to predict the binding affinity using the three-dimensional structure of ligands and proteins. However, there are limitations due to the need for experimental characterization of the three-dimensional structure of protein sequences, which is still lacking for some proteins. Moreover, these models often suffer from unnecessary complexity, resulting in extraneous computations. This study presents ResBiGAAT, a novel deep learning model that combines a deep Residual Bidirectional Gated Recurrent Unit with two-sided self-attention mechanisms. ResBiGAAT leverages protein and ligand sequence-level features and their physicochemical properties to efficiently predict protein-ligand binding affinity. Through rigorous evaluation using 5-fold cross-validation, we demonstrate the performance of our proposed approach. The model exhibits competitive performance on an external dataset, highlighting its generalizability. Our publicly available web interface, located at resbigaat.streamlit.app, allows users to conveniently input protein and ligand sequences to estimate binding affinity.

ZeroBind: a protein-specific zero-shot predictor with subgraph matching for drug-target interactions

Existing drug-target interaction (DTI) prediction methods generally fail to generalize well to novel (unseen) proteins and drugs. In this study, we propose a protein-specific meta-learning framework ZeroBind with subgraph matching for predicting protein-drug interactions from their structures. During the meta-training process, ZeroBind formulates training a protein-specific model, which is also considered a learning task, and each task uses graph neural networks (GNNs) to learn the protein graph embedding and the molecular graph embedding. Inspired by the fact that molecules bind to a binding pocket in proteins instead of the whole protein, ZeroBind introduces a weakly supervised subgraph information bottleneck (SIB) module to recognize the maximally informative and compressive subgraphs in protein graphs as potential binding pockets. In addition, ZeroBind trains the models of individual proteins as multiple tasks, whose importance is automatically learned with a task adaptive self-attention module to make final predictions. The results show that ZeroBind achieves superior performance on DTI prediction over existing methods, especially for those unseen proteins and drugs, and performs well after fine-tuning for those proteins or drugs with a few known binding partners.

EDC-Predictor: A Novel Strategy for Prediction of Endocrine-Disrupting Chemicals by Integrating Pharmacological and Toxicological Profiles

Identification of endocrine-disrupting chemicals (EDCs) is crucial in the reduction of human health risks. However, it is hard to do so because of the complex mechanisms of the EDCs. In this study, we propose a novel strategy named EDC-Predictor to integrate pharmacological and toxicological profiles for the prediction of EDCs. Different from conventional methods that only focus on a few nuclear receptors (NRs), EDC-Predictor considers more targets. It uses computational target profiles from network-based and machine learning-based methods to characterize compounds, including both EDCs and non-EDCs. The best model constructed by these target profiles outperformed those models by molecular fingerprints. In a case study to predict NR-related EDCs, EDC-Predictor showed a wider applicability domain and higher accuracy than four previous tools. Another case study further demonstrated that EDC-Predictor could predict EDCs targeting other proteins rather than NRs. Finally, a free web server was developed to make EDC prediction easier (http://lmmd.ecust.edu.cn/edcpred/). In summary, EDC-Predictor would be a powerful tool in EDC prediction and drug safety assessment.

Explore the active ingredients and potential mechanisms of JianPi QingRe HuaYu Methods in the treatment of gastric inflammation-cancer transformation by network pharmacology and experimental validation

BACKGROUND

JianPi QingRe HuaYu Methods (JQH) have been long used to treat chronic atrophic gastritis (CAG) and precancerous lesions of gastric cancer (PLGC). However, whether JQH can inhibit the transformation of gastritis to gastric cancer (GC) remains unclear.

METHODS

Herein, we first retrieved the active ingredients and targets of JQH from the TCMSP database and the targets related to the gastric inflammation-cancer transformation from public databases. Differentially expressed genes (DEGs) related to gastric inflammation-cancer transformation were identified from the Gene Expression Omnibus (GEO) database. Then, we obtained the potential therapeutic targets of JQH in treating gastric inflammation-cancer transformation by intersecting drugs and disease targets. The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) analyses of the potential therapeutic targets were conducted using R software. Next, we conducted molecular docking and in vitro experiments to validate our results.

RESULTS

We obtained 214 potential therapeutic targets of JQH by intersecting drugs and disease targets. We found that the potential mechanisms of JQH in treating gastric inflammation-cancer transformation might be related to JAK-STAT, Wnt, p53 and VEGF signaling pathways. The molecular docking indicated that quercetin, as the main active ingredient of JQH, might inhibit gastric inflammation-cancer transformation by binding with specific receptors. Our experimental results showed that quercetin inhibited cells proliferation (P < 0.001), promoted cell apoptosis (P < 0.001), reduced the secretion of pro-inflammatory cytokines (P < 0.001) and promoted the secretion of anti-inflammatory cytokines (P < 0.001) in MNNG-induced GES-1 cells. Furthermore, quercetin inhibited cells proliferation (P < 0.001) and reduced mRNA and protein level of markers of PLGC (P < 0.001) in CDCA-induced GES-1 cells.

CONCLUSION

These results provide the material basis and regulatory mechanisms of JQH in treating gastric inflammation-cancer transformation.

Integrated bioinformatics analysis and network pharmacology to explore the potential mechanism of Patrinia heterophylla Bunge against acute promyelocytic leukemia

INTRODUCTION

Current treatment with arsenic trioxide and all-trans retinoic acid has greatly improved the therapeutic efficacy and prognosis of acute promyelocytic leukemia (APL), but may cause numerous adverse effects. Patrinia heterophylla Bunge (PHEB), commonly known as "Mu-Tou-Hui" in China, is effective in treating leukemia. However, no studies have reported the use of PHEB for APL treatment. In this study, we aimed to investigate the potential anticancer mechanism of PHEB against APL.

METHODS

Public databases were used to search for bioactive compounds in PHEB, their potential targets, differentially expressed genes associated with APL, and therapeutic targets for APL. The core targets and signaling pathways of PHEB against APL were identified by the protein-protein interaction network, Kaplan-Meier curves, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment, and compound-target-pathway network analysis. Molecular docking was performed to predict the binding activity between the most active compounds and the key targets.

RESULTS

Quercetin and 2 other active components of PHEB may exert anti-APL effects through proteoglycans in cancer, estrogen signaling, and acute myeloid leukemia pathways. We also identified 6 core targets of the bioactive compounds of PHEB, including protein tyrosine phosphatase receptor type C, proto-oncogene tyrosine-protein kinase Src, mitogen-activated protein kinase phosphatase 3 (MAPK3), matrix metalloproteinase-9, vascular endothelial growth factor receptor-2, and myeloperoxidase, most of which were validated to improve the 5-year survival of patients. Molecular docking results showed that the active compound bound well to key targets.

CONCLUSION

The results not only predict the active ingredients and potential molecular mechanisms of PHEB against APL, but also help to guide further investigation into the anti-APL application of PHEB.

AMMVF-DTI: A Novel Model Predicting Drug-Target Interactions Based on Attention Mechanism and Multi-View Fusion

Accurate identification of potential drug-target interactions (DTIs) is a crucial task in drug development and repositioning. Despite the remarkable progress achieved in recent years, improving the performance of DTI prediction still presents significant challenges. In this study, we propose a novel end-to-end deep learning model called AMMVF-DTI (attention mechanism and multi-view fusion), which leverages a multi-head self-attention mechanism to explore varying degrees of interaction between drugs and target proteins. More importantly, AMMVF-DTI extracts interactive features between drugs and proteins from both node-level and graph-level embeddings, enabling a more effective modeling of DTIs. This advantage is generally lacking in existing DTI prediction models. Consequently, when compared to many of the start-of-the-art methods, AMMVF-DTI demonstrated excellent performance on the human, C. elegans, and DrugBank baseline datasets, which can be attributed to its ability to incorporate interactive information and mine features from both local and global structures. The results from additional ablation experiments also confirmed the importance of each module in our AMMVF-DTI model. Finally, a case study is presented utilizing our model for COVID-19-related DTI prediction. We believe the AMMVF-DTI model can not only achieve reasonable accuracy in DTI prediction, but also provide insights into the understanding of potential interactions between drugs and targets.

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.

Identification of bioactive compounds and potential mechanisms of scutellariae radix-coptidis rhizoma in the treatment of atherosclerosis by integrating network pharmacology and experimental validation

OBJECTIVE

This study aims at investigating the potential targets and functional mechanisms of Scutellariae Radix-Coptidis Rhizoma (QLYD) against atherosclerosis (AS) through network pharmacology, molecular docking, bioinformatic analysis and experimental validation.

METHODS

The compositions of QLYD were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and literature, where the main active components of QLYD and corresponding targets were identified. The potential therapeutic targets of AS were excavated using the OMIM database, DrugBank database, DisGeNET database, CTD database and GEO datasets. The protein-protein interaction (PPI) network of common targets was constructed and visualized by Cytoscape 3.7.2 software. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis were performed to analyze the function of core targets in the PPI network. Molecular docking was carried out using AutoDockTools, AutoDock Vina, and PyMOL software to verify the correlation between the main components of QLYD and the core targets. Mouse AS model was established and the results of network pharmacology were verified by in vivo experiments.

RESULTS

Totally 49 active components and 225 corresponding targets of QLYD were obtained, where 68 common targets were identified by intersecting with AS-related targets. Five hub genes including IL6, VEGFA, AKT1, TNF, and IL1B were screened from the PPI network. GO functional analysis reported that these targets had associations mainly with cellular response to oxidative stress, regulation of inflammatory response, epithelial cell apoptotic process, and blood coagulation. KEGG pathway analysis demonstrated that these targets were correlated to AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, IL-17 signaling pathway, MAPK signaling pathway, and NF-kappa B signaling pathway. Results of molecular docking indicated good binding affinity of QLYD to FOS, AKT1, and TNF. Animal experiments showed that QLYD could inhibit inflammation, improve blood lipid levels and reduce plaque area in AS mice to prevent and treat AS.

CONCLUSION

QLYD may exert anti-inflammatory and anti-oxidative stress effects through multi-component, multi-target and multi-pathway to treat AS.

MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug-target interaction

BACKGROUND

Prediction of drug-target interaction (DTI) is an essential step for drug discovery and drug reposition. Traditional methods are mostly time-consuming and labor-intensive, and deep learning-based methods address these limitations and are applied to engineering. Most of the current deep learning methods employ representation learning of unimodal information such as SMILES sequences, molecular graphs, or molecular images of drugs. In addition, most methods focus on feature extraction from drug and target alone without fusion learning from drug-target interacting parties, which may lead to insufficient feature representation.

MOTIVATION

In order to capture more comprehensive drug features, we utilize both molecular image and chemical features of drugs. The image of the drug mainly has the structural information and spatial features of the drug, while the chemical information includes its functions and properties, which can complement each other, making drug representation more effective and complete. Meanwhile, to enhance the interactive feature learning of drug and target, we introduce a bidirectional multi-head attention mechanism to improve the performance of DTI.

RESULTS

To enhance feature learning between drugs and targets, we propose a novel model based on deep learning for DTI task called MCL-DTI which uses multimodal information of drug and learn the representation of drug-target interaction for drug-target prediction. In order to further explore a more comprehensive representation of drug features, this paper first exploits two multimodal information of drugs, molecular image and chemical text, to represent the drug. We also introduce to use bi-rectional multi-head corss attention (MCA) method to learn the interrelationships between drugs and targets. Thus, we build two decoders, which include an multi-head self attention (MSA) block and an MCA block, for cross-information learning. We use a decoder for the drug and target separately to obtain the interaction feature maps. Finally, we feed these feature maps generated by decoders into a fusion block for feature extraction and output the prediction results.

CONCLUSIONS

MCL-DTI achieves the best results in all the three datasets: Human, C. elegans and Davis, including the balanced datasets and an unbalanced dataset. The results on the drug-drug interaction (DDI) task show that MCL-DTI has a strong generalization capability and can be easily applied to other tasks.