Network-Based Methods for Prediction of Drug-Target Interactions

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.

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.

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.

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.

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.

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.

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.

Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances

INTRODUCTION

Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks.

AREAS COVERED

This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets.

EXPERT OPINION

Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.

Artificial Intelligence-based Efficacy Prediction of Phase 3 Clinical Trial for Repurposing Heart Failure Therapies

Introduction

Drug repurposing involves finding new therapeutic uses for already approved drugs, which can save costs as their pharmacokinetics and pharmacodynamics are already known. Predicting efficacy based on clinical endpoints is valuable for designing phase 3 trials and making Go/No-Go decisions, given the potential for confounding effects in phase 2.

Objectives

This study aims to predict the efficacy of the repurposed Heart Failure (HF) drugs for the Phase 3 Clinical Trial.

Methods

Our study presents a comprehensive framework for predicting drug efficacy in phase 3 trials, which combines drug-target prediction using biomedical knowledgebases with statistical analysis of real-world data. We developed a novel drug-target prediction model that uses low-dimensional representations of drug chemical structures and gene sequences, and biomedical knowledgebase. Furthermore, we conducted statistical analyses of electronic health records to assess the effectiveness of repurposed drugs in relation to clinical measurements (e.g., NT-proBNP).

Results

We identified 24 repurposed drugs (9 with a positive effect and 15 with a non-positive) for heart failure from 266 phase 3 clinical trials. We used 25 genes related to heart failure for drug-target prediction, as well as electronic health records (EHR) from the Mayo Clinic for screening, which contained over 58,000 heart failure patients treated with various drugs and categorized by heart failure subtypes. Our proposed drug-target predictive model performed exceptionally well in all seven tests in the BETA benchmark compared to the six cutting-edge baseline methods (i.e., best performed in 266 out of 404 tasks). For the overall prediction of the 24 drugs, our model achieved an AUCROC of 82.59% and PRAUC (average precision) of 73.39%.

Conclusion

The study demonstrated exceptional results in predicting the efficacy of repurposed drugs for phase 3 clinical trials, highlighting the potential of this method to facilitate computational drug repurposing.

Expanding potential targets of herbal chemicals by node2vec based on herb-drug interactions

BACKGROUND

The identification of chemical-target interaction is key to pharmaceutical research and development, but the unclear materials basis and complex mechanisms of traditional medicine (TM) make it difficult, especially for low-content chemicals which are hard to test in experiments. In this research, we aim to apply the node2vec algorithm in the context of drug-herb interactions for expanding potential targets and taking advantage of molecular docking and experiments for verification.

METHODS

Regarding the widely reported risks between cardiovascular drugs and herbs, Salvia miltiorrhiza (Danshen, DS) and Ligusticum chuanxiong (Chuanxiong, CX), which are widely used in the treatment of cardiovascular disease (CVD), and approved drugs for CVD form the new dataset as an example. Three data groups DS-drug, CX-drug, and DS-CX-drug were applied to serve as the context of drug-herb interactions for link prediction. Three types of datasets were set under three groups, containing information from chemical-target connection (CTC), chemical-chemical connection (CCC) and protein-protein interaction (PPI) in increasing steps. Five algorithms, including node2vec, were applied as comparisons. Molecular docking and pharmacological experiments were used for verification.

RESULTS

Node2vec represented the best performance with average AUROC and AP values of 0.91 on the datasets "CTC, CCC, PPI". Targets of 32 herbal chemicals were identified within 43 predicted edges of herbal chemicals and drug targets. Among them, 11 potential chemical-drug target interactions showed better binding affinity by molecular docking. Further pharmacological experiments indicated caffeic acid increased the thermal stability of the protein GGT1 and ligustilide and low-content chemical neocryptotanshinone induced mRNA change of FGF2 and MTNR1A, respectively.

CONCLUSIONS

The analytical framework and methods established in the study provide an important reference for researchers in discovering herb-drug interactions, alerting clinical risks, and understanding complex mechanisms of TM.

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.

Investigation of Anti-Alzheimer's Mechanisms of Sarsasapogenin Derivatives by Network-Based Combining Structure-Based Methods

Alzheimer's disease (AD), a neurodegenerative disease with no cure, affects millions of people worldwide and has become one of the biggest healthcare challenges. Some investigated compounds play anti-AD roles at the cellular or the animal level, but their molecular mechanisms remain unclear. In this study, we designed a strategy combining network-based and structure-based methods together to identify targets for anti-AD sarsasapogenin derivatives (AAs). First, we collected drug-target interactions (DTIs) data from public databases, constructed a global DTI network, and generated drug-substructure associations. After network construction, network-based models were built for DTI prediction. The best bSDTNBI-FCFP_4 model was further used to predict DTIs for AAs. Second, a structure-based molecular docking method was employed for rescreening the prediction results to obtain more credible target proteins. Finally, in vitro experiments were conducted for validation of the predicted targets, and Nrf2 showed significant evidence as the target of anti-AD compound AA13. Moreover, we analyzed the potential mechanisms of AA13 for the treatment of AD. Generally, our combined strategy could be applied to other novel drugs or compounds and become a useful tool in identification of new targets and elucidation of disease mechanisms. Our model was deployed on our NetInfer web server (http://lmmd.ecust.edu.cn/netinfer/).

Pharmacological Mechanism of Aucklandiae Radix against Gastric Ulcer Based on Network Pharmacology and In Vivo Experiment

Background and Objectives: Aucklandiae Radix is a well-known medicinal herb that is often used to treat gastric ulcer, but its molecular mechanism of anti-ulcer action is poorly understood. This research aimed to reveal the potential active components, core targets, and mechanisms of Aucklandiae Radix in treating gastric ulcer by combining network pharmacology and animal experimentation. Materials and Methods: First, a network pharmacology strategy was used to predict the main components, candidate targets, and potential signaling pathways. Molecular docking was then used to confirm the binding affinity between the main components and primary targets. Finally, rats were treated with indomethacin 30 mg/kg to establish a gastric ulcer model. Aucklandiae Radix extract (0.15, 0.3, and 0.6 g/kg) was pre-treated in rats by oral gavage for 14 days, and the protective effect and candidate targets of network pharmacology were validated through morphological observation, pathological staining, and biochemical index detection. Results: A total of eight potential active components and 331 predicted targets were screened from Aucklandiae Radix, 37 of which were common targets with gastric ulcer. According to the component–target network and protein-protein interaction (PPI) network, stigmasterol, mairin, sitosterol, and dehydrocostus lactone were identified as the key components, and RAC-alpha serine/threonine-protein kinase (AKT1), prostaglandin-endoperoxide synthase 2 (PTGS2), interleukin 1 beta (IL1B), caspase-3 (CASP3), and CASP8 were selected as the core targets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment results revealed the pharmacological mechanism of Aucklandiae Radix against gastric ulcer related to many biological processes and pathways, including antibacterial, anti-inflammatory, prostaglandin receptor response, and apoptosis. Molecular docking verification showed that the key components and core targets had good binding affinities. In the in vivo experiments, Aucklandiae Radix notably relieved the gastric ulcer by reducing the levels of tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and myeloperoxidase (MPO) while improving the gastric histopathological features. Conclusion: The overall findings suggest that Aucklandiae Radix treats gastric ulcer with a multi-component, multi-target, and multi-mechanism model.

DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network

MOTIVATION

In vitro experiment-based drug-target interaction (DTI) exploration demands more human, financial and data resources. In silico approaches have been recommended for predicting DTIs to reduce time and cost. During the drug development process, one can analyze the therapeutic effect of the drug for a particular disease by identifying how the drug binds to the target for treating that disease. Hence, DTI plays a major role in drug discovery. Many computational methods have been developed for DTI prediction. However, the existing methods have limitations in terms of capturing the interactions via multiple semantics between drug and target nodes in a heterogeneous biological network (HBN).

METHODS

In this paper, we propose a DTiGNN framework for identifying unknown drug-target pairs. The DTiGNN first calculates the similarity between the drug and target from multiple perspectives. Then, the features of drugs and targets from each perspective are learned separately by using a novel method termed an information entropy-based random walk. Next, all of the learned features from different perspectives are integrated into a single drug and target similarity network by using a multi-view convolutional neural network. Using the integrated similarity networks, drug interactions, drug-disease associations, protein interactions and protein-disease association, the HBN is constructed. Next, a novel embedding algorithm called a meta-graph guided graph neural network is used to learn the embedding of drugs and targets. Then, a convolutional neural network is employed to infer new DTIs after balancing the sample using oversampling techniques.

RESULTS

The DTiGNN is applied to various datasets, and the result shows better performance in terms of the area under receiver operating characteristic curve (AUC) and area under precision-recall curve (AUPR), with scores of 0.98 and 0.99, respectively. There are 23,739 newly predicted DTI pairs in total.

Target identification by structure-based computational approaches: Recent advances and perspectives

The use of computational techniques in the early stages of drug discovery has recently experienced a boost, especially in the target identification step. Finding the biological partner(s) for new or existing synthetic and/or natural compounds by "wet" approaches may be challenging; therefore, preliminary in silico screening is even more recommended. After a brief overview of some of the most known target identification techniques, recent advances in structure-based computational approaches for target identification are reported in this digest, focusing on Inverse Virtual Screening and its recent applications. Moreover, future perspectives concerning the use of such methodologies, coupled or not with other approaches, are analyzed.

Computational Approaches to Enzyme Inhibition by Marine Natural Products in the Search for New Drugs

The exploration of biologically relevant chemical space for the discovery of small bioactive molecules present in marine organisms has led not only to important advances in certain therapeutic areas, but also to a better understanding of many life processes. The still largely untapped reservoir of countless metabolites that play biological roles in marine invertebrates and microorganisms opens new avenues and poses new challenges for research. Computational technologies provide the means to (i) organize chemical and biological information in easily searchable and hyperlinked databases and knowledgebases; (ii) carry out cheminformatic analyses on natural products; (iii) mine microbial genomes for known and cryptic biosynthetic pathways; (iv) explore global networks that connect active compounds to their targets (often including enzymes); (v) solve structures of ligands, targets, and their respective complexes using X-ray crystallography and NMR techniques, thus enabling virtual screening and structure-based drug design; and (vi) build molecular models to simulate ligand binding and understand mechanisms of action in atomic detail. Marine natural products are viewed today not only as potential drugs, but also as an invaluable source of chemical inspiration for the development of novel chemotypes to be used in chemical biology and medicinal chemistry research.

Using chemical and biological data to predict drug toxicity

Various sources of information can be used to better understand and predict compound activity and safety-related endpoints, including biological data such as gene expression and cell morphology. In this review, we first introduce types of chemical, in vitro and in vivo information that can be used to describe compounds and adverse effects. We then explore how compound descriptors based on chemical structure or biological perturbation response can be used to predict safety-related endpoints, and how especially biological data can help us to better understand adverse effects mechanistically. Overall, the described applications demonstrate how large-scale biological information presents new opportunities to anticipate and understand the biological effects of compounds, and how this can support predictive toxicology and drug discovery projects.

NMTF-DTI: A Nonnegative Matrix Tri-factorization Approach With Multiple Kernel Fusion for Drug-Target Interaction Prediction

Prediction of drug-target interactions (DTIs) plays a significant role in drug development and drug discovery. Although this task requires a large investment in terms of time and cost, especially when it is performed experimentally, the results are not necessarily significant. Computational DTI prediction is a shortcut to reduce the risks of experimental methods. In this study, we propose an effective approach of nonnegative matrix tri-factorization, referred to as NMTF-DTI, to predict the interaction scores between drugs and targets. NMTF-DTI utilizes multiple kernels (similarity measures) for drugs and targets and Laplacian regularization to boost the prediction performance. The performance of NMTF-DTI is evaluated via cross-validation and is compared with existing DTI prediction methods in terms of the area under the receiver operating characteristic (ROC) curve (AUC) and the area under the precision and recall curve (AUPR). We evaluate our method on four gold standard datasets, comparing to other state-of-the-art methods. Cross-validation and a separate, manually created dataset are used to set parameters. The results show that NMTF-DTI outperforms other competing methods. Moreover, the results of a case study also confirm the superiority of NMTF-DTI.

Systems Pharmacology and Pharmacokinetics Strategy to Decode Bioactive Ingredients and Molecular Mechanisms from Zingiber officinale as Phyto-therapeutics against Neurological Diseases

BACKGROUND

The bioactive constituents from Zingiber officinale (Z. officinale) have shown a positive effect on neurodegenerative diseases like Alzheimer's disease (AD), which manifests as progressive memory loss and cognitive impairment.

OBJECTIVE

This study investigates the binding ability and the pharmaco-therapeutic potential of Z. officinale with AD disease targets by molecular docking and molecular dynamic (MD) simulation approaches.

METHODS

By coupling enormous available phytochemical data and advanced computational technologies, the possible molecular mechanism of action of these bioactive compounds was deciphered by evaluating phytochemicals, target fishing, and network biological analysis.

RESULTS

As a result, 175 bioactive compounds and 264 human target proteins were identified. The gene ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analysis and molecular docking were used to predict the basis of vital bioactive compounds and biomolecular mechanisms involved in the treatment of AD. Amongst selected bioactive compounds, 10- Gingerdione and 1-dehydro-[8]-gingerdione exhibited significant anti-neurological properties against AD targeting amyloid precursor protein with docking energy of -6.0 and -5.6, respectively.

CONCLUSION

This study suggests that 10-Gingerdione and 1-dehydro-[8]-gingerdione strongly modulates the anti-neurological activity and are associated with pathological features like amyloid-β plaques and hyperphosphorylated tau protein are found to be critically regulated by these two target proteins. This comprehensive analysis provides a clue for further investigation of these natural compounds' inhibitory activity in drug discovery for AD treatment.

The curse and blessing of abundance-the evolution of drug interaction databases and their impact on drug network analysis

BACKGROUND

Widespread bioinformatics applications such as drug repositioning or drug-drug interaction prediction rely on the recent advances in machine learning, complex network science, and comprehensive drug datasets comprising the latest research results in molecular biology, biochemistry, or pharmacology. The problem is that there is much uncertainty in these drug datasets-we know the drug-drug or drug-target interactions reported in the research papers, but we cannot know if the not reported interactions are absent or yet to be discovered. This uncertainty hampers the accuracy of such bioinformatics applications.

RESULTS

We use complex network statistics tools and simulations of randomly inserted previously unaccounted interactions in drug-drug and drug-target interaction networks-built with data from DrugBank versions released over the plast decade-to investigate whether the abundance of new research data (included in the latest dataset versions) mitigates the uncertainty issue. Our results show that the drug-drug interaction networks built with the latest dataset versions become very dense and, therefore, almost impossible to analyze with conventional complex network methods. On the other hand, for the latest drug database versions, drug-target networks still include much uncertainty; however, the robustness of complex network analysis methods slightly improves.

CONCLUSIONS

Our big data analysis results pinpoint future research directions to improve the quality and practicality of drug databases for bioinformatics applications: benchmarking for drug-target interaction prediction and drug-drug interaction severity standardization.

Machine Learning in Drug Metabolism Study

Metabolic reactions in the body transform the administered drug into metabolites. These metabolites exhibit diverse biological activities. Drug metabolism is the major underlying cause of drug overdose-related toxicity, adversative drug effects and the drug's reduced efficacy. Though metabolic reactions deactivate a drug, drug metabolites are often considered pivotal agents for off-target effects or toxicity. On the other side, in combination drug therapy, one drug may influence another drug's metabolism and clearance and is thus considered one of the primary causes of drug-drug interactions. Today with the advancement of machine learning, the metabolic fate of a drug candidate can be comprehensively studied throughout the drug development procedure. Naïve Bayes, Logistic Regression, k-Nearest Neighbours, Decision Trees, different Boosting and Ensemble methods, Support Vector Machines and Artificial Neural Network boosted Deep Learning are some machine learning algorithms which are being extensively used in such studies. Such tools are covering several attributes of drug metabolism, with an emphasis on the prediction of drug-drug interactions, drug-target-interactions, clinical drug responses, metabolite predictions, sites of metabolism, etc. These reports are crucial for evaluating metabolic stability and predicting prospective drug-drug interactions, and can help pharmaceutical companies accelerate the drug development process in a less resource-demanding manner than what in vitro studies offer. It could also help medical practitioners to use combinatorial drug therapy in a more resourceful manner. Also, with the help of the enormous growth of deep learning, traditional fields of computational drug development like molecular interaction fields, molecular docking, quantitative structure-to-activity relationship (QSAR) studies and quantum mechanical simulations are producing results which were unimaginable couple of years back. This review provides a glimpse of a few contextually relevant machine learning algorithms and then focuses on their outcomes in different studies.

Acteoside and ursolic acid synergistically protects H2O2-induced neurotrosis by regulation of AKT/mTOR signalling: from network pharmacology to experimental validation

CONTEXT

Ursolic acid (UA) and acteoside (ATS) are important active components that have been used to treat Alzheimer's disease (AD) because of their neuroprotective effects, but the exact mechanism is still unclear.

OBJECTIVE

Network pharmacology was used to explore the mechanism of UA + ATS in treating AD, and cell experiments were used to verify the mechanism.

MATERIALS AND METHODS

UA + ATS targets and AD-related genes were retrieved from TCMSP, STITCH, SwissTargetPrediction, GeneCards, DisGeNET and GEO. Key targets were obtained by constructing protein interaction network through STRING. The neuroprotective effects of UA + ATS were verified in H₂O₂-treated PC12 cells. The subsequent experiments were divided into Normal, Model (H₂O₂ pre-treatment for 4 h), Control (H₂O₂+ solvent pre-treatment), UA (5 μM), ATS (40 μM), UA (5 μM) + ATS (40 μM). Then apoptosis, mitochondrial membrane potential, caspase-3 activity, ATG5, Beclin-1 protein expression and Akt, mTOR phosphorylation levels were detected.

RESULTS

The key targets of UA + ATS-AD network were mainly enriched in Akt/mTOR pathway. Cell experiments showed that UA (ED₅₀: 5 μM) + ATS (ED₅₀: 40 μM) could protect H₂O₂-induced (IC₅₀: 250 μM) nerve damage by enhancing cells viability, combating apoptosis, restoring MMP, reducing the activation of caspase-3, lessening the phosphorylation of Akt and mTOR, and increasing the expression of ATG5 and Beclin-1.

CONCLUSIONS

ATS and UA regulates multiple targets, bioprocesses and signal pathways against AD pathogenesis. ATS and UA synergistically protects H₂O₂-induced neurotrosis by regulation of AKT/mTOR signalling.

To explore the active constituents of Sedum aizoon L in the treatment of coronary heart disease based on network pharmacology and molecular docking methodology

Background

There is a lack of effective drugs for the treatment of coronary heart disease (CHD). Sedum aizoon L (SL) has multiple effects, and there is no report on CHD in SL at present. The aim of this study is to explore the mechanisms of action of SL in the treatment of CHD based on network pharmacology and molecular docking technology.

Methods

The targets and active ingredients of SL were screened using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, and CHD-related targets were obtained by searching GeneCards and DisGeNet databases. The intersection of LS active ingredient targets and CHD targets was used to construct a "drug-ingredient-disease-target" network using the Cytoscape software. The STRING database was used to construct a protein-protein interaction (PPI) network, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. Key targets and core active ingredients were selected and molecular docking was performed using the AutoDock software.

Results

According to the predicted results, a total of 134 corresponding target genes for LS, 12 active components, 1,704 CHD-related targets, and 52 intersecting targets were obtained. GO function and KEGG pathway analysis showed that the key targets were involved with signal transducer and activator of transcription 3 (STAT3), tumor protein p53 (TP53), and vascular endothelial growth factor A (VEGFA). The molecular docking results showed that the key targets bound to the important active ingredients in a stable conformation. The core active ingredients of LS in the treatment of CHD were determined to be ursolic acid, myricetin, and beta-sitosterol.

Conclusions

SL may act on targets such as STAT3, TP53, and VEGFA through tumor necrosis factor (TNF) signaling pathway, interleukin 17A (IL-17A) signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and other related pathways, thereby playing a role in preventing and treating CHD.

Discovering drug-target interaction knowledge from biomedical literature

MOTIVATION

The interaction between drugs and targets (DTI) in human body plays a crucial role in biomedical science and applications. As millions of papers come out every year in the biomedical domain, automatically discovering DTI knowledge from biomedical literature, which are usually triplets about drugs, targets and their interaction, becomes an urgent demand in the industry. Existing methods of discovering biological knowledge are mainly extractive approaches that often require detailed annotations (e.g. all mentions of biological entities, relations between every two entity mentions, etc.). However, it is difficult and costly to obtain sufficient annotations due to the requirement of expert knowledge from biomedical domains.

RESULTS

To overcome these difficulties, we explore an end-to-end solution for this task by using generative approaches. We regard the DTI triplets as a sequence and use a Transformer-based model to directly generate them without using the detailed annotations of entities and relations. Further, we propose a semi-supervised method, which leverages the aforementioned end-to-end model to filter unlabeled literature and label them. Experimental results show that our method significantly outperforms extractive baselines on DTI discovery. We also create a dataset, KD-DTI, to advance this task and release it to the community.

AVAILABILITY AND IMPLEMENTATION

Our code and data are available at https://github.com/bert-nmt/BERT-DTI.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational approaches for network-based integrative multi-omics analysis

Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.

A novel method for drug-target interaction prediction based on graph transformers model

BACKGROUND

Drug-target interactions (DTIs) prediction becomes more and more important for accelerating drug research and drug repositioning. Drug-target interaction network is a typical model for DTIs prediction. As many different types of relationships exist between drug and target, drug-target interaction network can be used for modeling drug-target interaction relationship. Recent works on drug-target interaction network are mostly concentrate on drug node or target node and neglecting the relationships between drug-target.

RESULTS

We propose a novel prediction method for modeling the relationship between drug and target independently. Firstly, we use different level relationships of drugs and targets to construct feature of drug-target interaction. Then, we use line graph to model drug-target interaction. After that, we introduce graph transformer network to predict drug-target interaction.

CONCLUSIONS

This method introduces a line graph to model the relationship between drug and target. After transforming drug-target interactions from links to nodes, a graph transformer network is used to accomplish the task of predicting drug-target interactions.

Methodology of network pharmacology for research on Chinese herbal medicine against COVID-19: A review

Traditional Chinese medicine, as a complementary and alternative medicine, has been practiced for thousands of years in China and possesses remarkable clinical efficacy. Thus, systematic analysis and examination of the mechanistic links between Chinese herbal medicine (CHM) and the complex human body can benefit contemporary understandings by carrying out qualitative and quantitative analysis. With increasing attention, the approach of network pharmacology has begun to unveil the mystery of CHM by constructing the heterogeneous network relationship of "herb-compound-target-pathway," which corresponds to the holistic mechanisms of CHM. By integrating computational techniques into network pharmacology, the efficiency and accuracy of active compound screening and target fishing have been improved at an unprecedented pace. This review dissects the core innovations to the network pharmacology approach that were developed in the years since 2015 and highlights how this tool has been applied to understanding the coronavirus disease 2019 and refining the clinical use of CHM to combat it.

GCHN-DTI: Predicting drug-target interactions by graph convolution on heterogeneous networks

Determining the interaction of drug and target plays a key role in the process of drug development and discovery. The calculation methods can predict new interactions and speed up the process of drug development. In recent studies, the network-based approaches have been proposed to predict drug-target interactions. However, these methods cannot fully utilize the node information from heterogeneous networks. Therefore, we propose a method based on heterogeneous graph convolutional neural network for drug-target interaction prediction, GCHN-DTI (Predicting drug-target interactions by graph convolution on heterogeneous net-works), to predict potential DTIs. GCHN-DTI integrates network information from drug-target interactions, drug-drug interactions, drug-similarities, target-target interactions, and target-similarities. Then, the graph convolution operation is used in the heterogeneous network to obtain the node embedding of the drugs and the targets. Furthermore, we incorporate an attention mechanism between graph convolutional layers to combine node embedding from each layer. Finally, the drug-target interaction score is predicted based on the node embedding of the drugs and the targets. Our model uses fewer network types and achieves higher prediction performance. In addition, the prediction performance of the model will be significantly improved on the dataset with a higher proportion of positive samples. The experimental evaluations show that GCHN-DTI outperforms several state-of-the-art prediction methods.

A pharmacometric approach to evaluate drugs for potential repurposing as COVID-19 therapeutics

INTRODUCTION

Developing and evaluating novel compounds for treatment or prophylaxis of emerging infectious diseases is costly and time-consuming. Repurposing of already available marketed compounds is an appealing option as they already have an established safety profile. This approach could substantially reduce cost and time required to make effective treatments available to fight the COVID-19 pandemic. However, this approach is challenging since many drug candidates show efficacy in in vitro experiments, but fail to deliver effect when evaluated in clinical trials. Better approaches to evaluate in vitro data are needed, in order to prioritize drugs for repurposing.

AREAS COVERED

This article evaluates potential drugs that might be of interest for repurposing in the treatment of patients with COVID-19 disease. A pharmacometric simulation-based approach was developed to evaluate in vitro activity data in combination with expected clinical drug exposure, in order to evaluate the likelihood of achieving effective concentrations in patients.

EXPERT OPINION

The presented pharmacometric approach bridges in vitro activity data to clinically expected drug exposures, and could therefore be a useful compliment to other methods in order to prioritize repurposed drugs for evaluation in prospective randomized controlled clinical trials.

Network Pharmacology and Molecular Docking Study of Yupingfeng Powder in the Treatment of Allergic Diseases

Objective

To explore the potential mechanisms of Yupingfeng Powder (YPFP) in the treatment of allergic diseases by using network pharmacology and molecular docking technology.

Methods

The active components and targets of YPFP were screened by the TCMSP database. The targets associated with atopic dermatitis, asthma, allergic rhinitis, and food allergy were obtained from GeneCards and OMIM databases, respectively. The intersection of the above disease-related targets was identified as allergy-related targets. Then, allergy-related targets and YPFP-related targets were crossed to obtain the potential targets of YPFP for allergy treatment. A protein-protein-interaction (PPI) network and a drug-target-disease topology network were constructed to screen hub targets and key ingredients. Next, GO and KEGG pathway enrichment analyses were performed separately on the potential targets and hub targets to identify the biological processes and signaling pathways involved. Finally, molecular docking was conducted to verify the binding affinity between key ingredients and hub targets.

Results

In this study, 45 active ingredients were identified from YPFP, and 48 allergy-related targets were predicted by network pharmacology. IL6, TNF, IL1B, PTGS2, CXCL8, JUN, CCL2, IL10, IFNG, and IL4 were screened as hub targets by the PPI network. However, quercetin, kaempferol, wogonin, formononetin, and 7-O-methylisomucronulatol were identified as key ingredients by the drug-target-disease topological network. GO and KEGG pathway enrichment analysis indicated that the therapeutic effect of YPFP on allergy involved multiple biological processes and signaling pathways, including positive regulation of fever generation, positive regulation of neuroinflammatory response, vascular endothelial growth factor production, negative regulation of cytokine production involved in immune response, positive regulation of mononuclear cell migration, type 2 immune response, and negative regulation of lipid storage. Molecular docking verified that all the key ingredients had good binding affinity with hub targets.

Conclusion

This study revealed the key ingredients, hub targets, and potential mechanisms of YPFP antiallergy, and these data can provide some theoretical basis for subsequent allergy treatment and drug development.

Drug-Target Network Study Reveals the Core Target-Protein Interactions of Various COVID-19 Treatments

The coronavirus disease 2019 (COVID-19) pandemic has caused a dramatic loss of human life and devastated the worldwide economy. Numerous efforts have been made to mitigate COVID-19 symptoms and reduce the death rate. We conducted literature mining of more than 250 thousand published works and curated the 174 most widely used COVID-19 medications. Overlaid with the human protein-protein interaction (PPI) network, we used Steiner tree analysis to extract a core subnetwork that grew from the pharmacological targets of ten credible drugs ascertained by the CTD database. The resultant core subnetwork consisted of 34 interconnected genes, which were associated with 36 drugs. Immune cell membrane receptors, the downstream cellular signaling cascade, and severe COVID-19 symptom risk were significantly enriched for the core subnetwork genes. The lung mast cell was most enriched for the target genes among 1355 human tissue-cell types. Human bronchoalveolar lavage fluid COVID-19 single-cell RNA-Seq data highlighted the fact that T cells and macrophages have the most overlapping genes from the core subnetwork. Overall, we constructed an actionable human target-protein module that mainly involved anti-inflammatory/antiviral entry functions and highly overlapped with COVID-19-severity-related genes. Our findings could serve as a knowledge base for guiding drug discovery or drug repurposing to confront the fast-evolving SARS-CoV-2 virus and other severe infectious diseases.

From Traditional Ethnopharmacology to Modern Natural Drug Discovery: A Methodology Discussion and Specific Examples

Ethnopharmacology, through the description of the beneficial effects of plants, has provided an early framework for the therapeutic use of natural compounds. Natural products, either in their native form or after crude extraction of their active ingredients, have long been used by different populations and explored as invaluable sources for drug design. The transition from traditional ethnopharmacology to drug discovery has followed a straightforward path, assisted by the evolution of isolation and characterization methods, the increase in computational power, and the development of specific chemoinformatic methods. The deriving extensive exploitation of the natural product chemical space has led to the discovery of novel compounds with pharmaceutical properties, although this was not followed by an analogous increase in novel drugs. In this work, we discuss the evolution of ideas and methods, from traditional ethnopharmacology to in silico drug discovery, applied to natural products. We point out that, in the past, the starting point was the plant itself, identified by sustained ethnopharmacological research, with the active compound deriving after extensive analysis and testing. In contrast, in recent years, the active substance has been pinpointed by computational methods (in silico docking and molecular dynamics, network pharmacology), followed by the identification of the plant(s) containing the active ingredient, identified by existing or putative ethnopharmacological information. We further stress the potential pitfalls of recent in silico methods and discuss the absolute need for in vitro and in vivo validation as an absolute requirement. Finally, we present our contribution to natural products’ drug discovery by discussing specific examples, applying the whole continuum of this rapidly evolving field. In detail, we report the isolation of novel antiviral compounds, based on natural products active against influenza and SARS-CoV-2 and novel substances active on a specific GPCR, OXER1.

BETA: a comprehensive benchmark for computational drug-target prediction

Internal validation is the most popular evaluation strategy used for drug-target predictive models. The simple random shuffling in the cross-validation, however, is not always ideal to handle large, diverse and copious datasets as it could potentially introduce bias. Hence, these predictive models cannot be comprehensively evaluated to provide insight into their general performance on a variety of use-cases (e.g. permutations of different levels of connectiveness and categories in drug and target space, as well as validations based on different data sources). In this work, we introduce a benchmark, BETA, that aims to address this gap by (i) providing an extensive multipartite network consisting of 0.97 million biomedical concepts and 8.5 million associations, in addition to 62 million drug-drug and protein-protein similarities and (ii) presenting evaluation strategies that reflect seven cases (i.e. general, screening with different connectivity, target and drug screening based on categories, searching for specific drugs and targets and drug repurposing for specific diseases), a total of seven Tests (consisting of 344 Tasks in total) across multiple sampling and validation strategies. Six state-of-the-art methods covering two broad input data types (chemical structure- and gene sequence-based and network-based) were tested across all the developed Tasks. The best-worst performing cases have been analyzed to demonstrate the ability of the proposed benchmark to identify limitations of the tested methods for running over the benchmark tasks. The results highlight BETA as a benchmark in the selection of computational strategies for drug repurposing and target discovery.

In silico Methods for Identification of Potential Therapeutic Targets

At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.

Graphical abstract

Identification of Bioactive Compounds and Potential Mechanisms of Kuntai Capsule in the Treatment of Polycystic Ovary Syndrome by Integrating Network Pharmacology and Bioinformatics

Objective

This study elucidates the potential therapeutic targets and molecular mechanisms of KTC in the treatment of PCOS.

Materials and Methods

Using the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP), the active ingredients and potential targets of KTC were obtained. The Gene Expression Omnibus (GEO) database was used to find differentially expressed genes (DEGs) related to PCOS. Search the CTD, DisGeNet, genecards, NCBI, OMIM, and PharmGKB databases for therapeutic targets related to PCOS. The intersection of potential targets, DEGs, and therapeutic targets was submitted to perform bioinformatics analysis by R language. Finally, the analyses' core targets and their corresponding active ingredients were molecularly docked.

Results

88 potential therapeutic targets of KTC for PCOS were discovered by intersecting the potential targets, DEGs, and therapeutic targets. According to bioinformatics analysis, the mechanisms of KTC treatment for PCOS could be linked to IL-17 signaling route, p53 signaling pathway, HIF-1 signaling pathway, etc. The minimal binding energies of the 5 core targets and their corresponding ingredients were all less than -6.5. Further research found that quercetin may replace KTC in the treatment of PCOS. Discussion and Conclusions. We explored the active ingredients and molecular mechanisms of KTC in the treatment of PCOS and found that quercetin may be the core ingredient of KTC in the treatment of PCOS.

Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives

Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.