Deep learning in target prediction and drug repositioning: Recent advances and challenges

Heterogeneous Graph Contrastive Learning with Graph Diffusion for Drug Repositioning

Drug repositioning, which identifies novel therapeutic applications for existing drugs, offers a cost-effective alternative to traditional drug development. However, effectively capturing the complex relationships between drugs and diseases remains challenging. We present HGCL-DR, a novel heterogeneous graph contrastive learning framework for drug repositioning that effectively integrates global and local feature representations through three key components. First, we introduce an improved heterogeneous graph contrastive learning approach to model drug-disease relationships. Second, for local feature extraction, we employ a bidirectional graph convolutional network with a subgraph generation strategy in the bipartite drug-disease association graph, while utilizing a graph diffusion process to capture long-range dependencies in drug-drug and disease-disease relation graphs. Third, for global feature extraction, we leverage contrastive learning in the heterogeneous graph to enhance embedding consistency across different feature spaces. Extensive experiments on four benchmark data sets using 10-fold cross-validation demonstrate that HGCL-DR consistently outperforms state-of-the-art baselines in both AUPR, AUROC, and F1-score metrics. Ablation studies confirm the significance of each proposed component, while case studies on Alzheimer's disease and breast neoplasms validate HGCL-DR's practical utility in identifying novel drug candidates. These results establish HGCL-DR as an effective approach for computational drug repositioning.

Boosting Drug-Disease Association Prediction for Drug Repositioning via Dual-Feature Extraction and Cross-Dual-Domain Decoding

The extraction of biomedical data has significant academic and practical value in contemporary biomedical sciences. In recent years, drug repositioning, a cost-effective strategy for drug development by discovering new indications for approved drugs, has gained increasing attention. However, many existing drug repositioning methods focus on mining information from adjacent nodes in biomedical networks without considering the potential inter-relationships between the feature spaces of drugs and diseases. This can lead to inaccurate encoding, resulting in biased mined drug-disease association information. To address this limitation, we propose a new model called Dual-Feature Drug Repurposing Neural Network (DFDRNN). DFDRNN allows the mining of two features (similarity and association) from the drug-disease biomedical networks to encode drugs and diseases. A self-attention mechanism is utilized to extract neighbor feature information. It incorporates two dual-feature extraction modules: the single-domain dual-feature extraction (SDDFE) module for extracting features within a single domain (drugs or diseases) and the cross-domain dual-feature extraction (CDDFE) module for extracting features across domains. By utilizing these modules, we ensure more appropriate encoding of drugs and diseases. A cross-dual-domain decoder is also designed to predict drug-disease associations in both domains. Our proposed DFDRNN model outperforms six state-of-the-art methods on four benchmark data sets, achieving an average AUROC of 0.946 and an average AUPR of 0.597. Case studies on three diseases show that the proposed DFDRNN model can be applied in real-world scenarios, demonstrating its significant potential in drug repositioning.

Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides

Novel antifungal drugs that evade resistance are urgently needed for Candida infections. Antifungal peptides (AFPs) are potential candidates due to their specific mechanism of action, which makes them less prone to developing drug resistance. An AFP de novo design method, Deep Learning-Quantitative Structure‒Activity Relationship Empirical Screening (DL-QSARES), is developed by integrating deep learning and quantitative structure‒activity relationship  empirical screening. After generating candidate AFPs (c_AFPs) through the recombination of dominant amino acids and dipeptide compositions, natural language processing models are utilized and quantitative structure‒activity relationship (QSAR) approaches based on physicochemical properties to screen for promising c_AFPs. Forty-nine promising c_AFPs are screened, and their minimum inhibitory concentrations (MICs) against C. albicans are determined to be 3.9-125 µg mL-1, of which four leading c_AFPs (AFP-8, -10, -11, and -13) has MICs of <10 µg mL-1 against the four tested pathogenic fungi, and AFP-13 has excellent therapeutic efficacy in the animal model.

Improving drug repositioning accuracy using non-negative matrix tri-factorization

Drug repositioning is a transformative approach in drug discovery, offering a pathway to repurpose existing drugs for new therapeutic uses. In this study, we introduce the IDDNMTF model designed to predict drug repositioning opportunities with greater precision. The IDDNMTF model integrates multiple datasets, allowing for a more comprehensive analysis of drug-disease associations. We evaluated the IDDNMTF model using various combinations of datasets and found that its performance, as measured by AUC, AUPR, and F1 scores, improved with the inclusion of more data. This trend underscores the importance of data diversity in strengthening predictive capabilities. Comparatively, the IDDNMTF model demonstrated superior performance against the NMF model, solidifying its potential in drug repositioning. In summary, the IDDNMTF model offers a promising tool for identifying new therapeutic uses for existing drugs. Its predictive accuracy and interpretability are poised to accelerate the transition from bench to bedside, contributing to personalized medicine and the development of targeted treatments.

Deep learning: A game changer in drug design and development

The lengthy and costly drug discovery process is transformed by deep learning, a subfield of artificial intelligence. Deep learning technologies expedite the procedure, increasing treatment success rates and speeding life-saving procedures. Deep learning stands out in target identification and lead selection. Deep learning greatly accelerates initial stage by analyzing large datasets of biological data to identify possible therapeutic targets and rank targeted drug molecules with desired features. Predicting possible adverse effects is another significant challenge. Deep learning offers prompt and efficient assistance with toxicology prediction in a very short time, deep learning algorithms can forecast a new drug's possible harm. This enables to concentrate on safer alternatives and steer clear of late-stage failures brought on by unanticipated toxicity. Deep learning unlocks the possibility of drug repurposing; by examining currently available medications, it is possible to find whole new therapeutic uses. This method speeds up development of diseases that were previously incurable. De novo drug discovery is made possible by deep learning when combined with sophisticated computational modeling, it can create completely new medications from the ground. Deep learning can recommend and direct towards new drug candidates with high binding affinities and intended therapeutic effects by examining molecular structures of disease targets. This provides focused and personalized medication. Lastly, drug characteristics can be optimized with aid of deep learning. Researchers can create medications with higher bioavailability and fewer toxicity by forecasting drug pharmacokinetics. In conclusion, deep learning promises to accelerate drug development, reduce costs, and ultimately save lives.

An Optimised Mobilenet V2 Attention Parallel Network for Predicting Drug-Drug Interactions Through Combining Local and Global Features

Drug-drug interactions (DDIs) are an important concern in the clinical practice and drug development process as these may lead to serious adverse effects on patient safety. Thorough DDI prediction is important for effective medication management and reduced risk factors. This work presents a new technique, namely MV2SAPCNNO: MobileNetV2 with simplicial attention network-based parallel convolutional neural network and narwhal optimiser, for improving the precision of DDI prediction. The proposed method starts with data preprocessing, including normalisation and noise reduction, to enhance the quality of the data. Then, MobileNetV2 with simplicial attention network (MV2SAN) is used to extract both local and global features from the dataset. These features are processed using a parallel convolutional neural network (PCNN), optimised by the narwhal optimiser (NO) to improve parameter tuning, minimise error and reduce computational complexity. The performance of the model is evaluated using accuracy, precision, recall and F-score. Experimental results prove that MV2SAPCN-NO achieves better performance over the current models of DDI prediction in accuracy and enhanced classification metrics. The narwhal optimiser enhances the model's convergence efficiency and decreases computational time with an excellent predictive performance. An efficient and accurate DDI prediction model was proposed called MV2SAPCNNO. This model actually outperformed traditional models, and such findings were exhibited to contribute towards secure medication administration, drug development processes and protection of patients in clinical practice.

In vitro study of essential oils encapsulated in chitosan microparticles against Histoplasma capsulatum and their pathogenicity in Caenorhabditis elegans

Histoplasmosis, caused by Histoplasma capsulatum, poses risks for immunocompromised individuals. With limited therapeutic options, this study explores microparticles as antimicrobial delivery systems for Cymbopogon flexuosus and Pelargonium graveolens essential oils against H. capsulatum. The broth microdilution assay showed MICs of 32 to 128 µg/mL in filamentous phase and 8 to 64 µg/mL in yeast phase. Combining microparticles with antifungal drugs demonstrated synergistic effects in both filamentous and yeast-like forms with amphotericin B or itraconazole. Chitosan microparticles reduced H. capsulatum biofilm biomass and metabolic activity by about 60% at 512 µg/mL. In vivo evaluation with Caenorhabditis elegans showed H. capsulatum caused over 90% mortality. These findings highlight the potential use of chitosan microparticles as a delivery system for essential oils against H. capsulatum, especially in combination with other compounds.

Exploration of innovative drug repurposing strategies for combating human protozoan diseases: Advances, challenges, and opportunities

Protozoan infections (e.g., malaria, trypanosomiasis, and toxoplasmosis) pose a considerable global burden on public health and socioeconomic problems, leading to high rates of morbidity and mortality. Due to the limited arsenal of effective drugs for these diseases, which are associated with devastating side effects and escalating drug resistance, there is an urgent need for innovative antiprotozoal drugs. The emergence of drug repurposing offers a low-cost approach to discovering new therapies for protozoan diseases. In this review, we summarize recent advances in drug repurposing for various human protozoan diseases and explore cost-effective strategies to identify viable new treatments. We highlight the cross-applicability of repurposed drugs across diverse diseases and harness common chemical motifs to provide new insights into drug design, facilitating the discovery of new antiprotozoal drugs. Challenges and opportunities in the field are discussed, delineating novel directions for ongoing and future research.

A spatial hierarchical network learning framework for drug repositioning allowing interpretation from macro to micro scale

Biomedical network learning offers fresh prospects for expediting drug repositioning. However, traditional network architectures struggle to quantify the relationship between micro-scale drug spatial structures and corresponding macro-scale biomedical networks, limiting their ability to capture key pharmacological properties and complex biomedical information crucial for drug screening and therapeutic discovery. Moreover, challenges such as difficulty in capturing long-range dependencies hinder current network-based approaches. To address these limitations, we introduce the Spatial Hierarchical Network, modeling molecular 3D structures and biological associations into a unified network. We propose an end-to-end framework, SpHN-VDA, integrating spatial hierarchical information through triple attention mechanisms to enhance machine understanding of molecular functionality and improve the accuracy of virus-drug association identification. SpHN-VDA outperforms leading models across three datasets, particularly excelling in out-of-distribution and cold-start scenarios. It also exhibits enhanced robustness against data perturbation, ranging from 20% to 40%. It accurately identifies critical motifs for binding sites, even without protein residue annotations. Leveraging reliability of SpHN-VDA, we have identified 25 potential candidate drugs through gene expression analysis and CMap. Molecular docking experiments with the SARS-CoV-2 spike protein further corroborate the predictions. This research highlights the broad potential of SpHN-VDA to enhance drug repositioning and identify effective treatments for various diseases.

MREDTA: A BERT and transformer-based molecular representation encoder for predicting drug-target binding affinity

Drug-target binding affinity (DTA) prediction is vital for drug repositioning. The accuracy and generalizability of DTA models remain a major challenge. Here, we develop a model composed of BERT-Trans Block, Multi-Trans Block, and DTI Learning modules, referred to as Molecular Representation Encoder-based DTA prediction (MREDTA). MREDTA has three advantages: (1) extraction of both local and global molecular features simultaneously through skip connections; (2) improved sensitivity to molecular structures through the Multi-Trans Block; (3) enhanced generalizability through the introduction of BERT. Compared with 12 advanced models, benchmark testing of KIBA and Davis datasets demonstrated optimal performance of MREDTA. In case study, we applied MREDTA to 2034 FDA-approved drugs for treating non-small-cell lung cancer (NSCLC), all of which act on mutant EGFRT790M protein. The corresponding molecular docking results demonstrated the robustness of MREDTA.

Computational methods and key considerations for in silico design of proteolysis targeting chimera (PROTACs)

Proteolysis-targeting chimeras (PROTACs), as heterobifunctional molecules, have garnered significant attention for their ability to target previously undruggable proteins. Due to the challenges in obtaining crystal structures of PROTAC molecules in the ternary complex, a plethora of computational tools have been developed to aid in PROTAC design. These computational tools can be broadly classified into artificial intelligence (AI)-based or non-AI-based methods. This review aims to provide a comprehensive overview of the latest computational methods for the PROTAC design process, covering both AI and non-AI approaches, from protein selection to ternary complex modeling and prediction. Key considerations for in silico PROTAC design are discussed, along with additional considerations for deploying AI-based models. These considerations are intended to guide subsequent model development in the PROTAC design process. Finally, future directions and recommendations are provided.

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.

The role of artificial intelligence in the development of anticancer therapeutics from natural polyphenols: Current advances and future prospects

Natural polyphenols, abundant in the human diet, are derived from a wide variety of sources. Numerous preclinical studies have demonstrated their significant anticancer properties against various malignancies, making them valuable resources for drug development. However, traditional experimental methods for developing anticancer therapies from natural polyphenols are time-consuming and labor-intensive. Recently, artificial intelligence has shown promising advancements in drug discovery. Integrating AI technologies into the development process for natural polyphenols can substantially reduce development time and enhance efficiency. In this study, we review the crucial roles of natural polyphenols in anticancer treatment and explore the potential of AI technologies to aid in drug development. Specifically, we discuss the application of AI in key stages such as drug structure prediction, virtual drug screening, prediction of biological activity, and drug-target protein interaction, highlighting the potential to revolutionize the development of natural polyphenol-based anticancer therapies.

MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs

BACKGROUND

Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge.

RESULTS

We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA.

CONCLUSIONS

During the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction.

Deep Learning-Driven Exploration of Pyrroloquinoline Quinone Neuroprotective Activity in Alzheimer's Disease

Alzheimer's disease (AD) is a pressing concern in neurodegenerative research. To address the challenges in AD drug development, especially those targeting Aβ, this study uses deep learning and a pharmacological approach to elucidate the potential of pyrroloquinoline quinone (PQQ) as a neuroprotective agent for AD. Using deep learning for a comprehensive molecular dataset, blood-brain barrier (BBB) permeability is predicted and the anti-inflammatory and antioxidative properties of compounds are evaluated. PQQ, identified in the Mediterranean-DASH intervention for a diet that delays neurodegeneration, shows notable BBB permeability and low toxicity. In vivo tests conducted on an Aβ₁₋₄₂-induced AD mouse model verify the effectiveness of PQQ in reducing cognitive deficits. PQQ modulates genes vital for synapse and anti-neuronal death, reduces reactive oxygen species production, and influences the SIRT1 and CREB pathways, suggesting key molecular mechanisms underlying its neuroprotective effects. This study can serve as a basis for future studies on integrating deep learning with pharmacological research and drug discovery.

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.

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach

Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100 ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

Orchestrating Precision within the Tumor Microenvironment by Biomimetic Nanoprodrugs for Effective Tumor Therapy

Malignant tumors are still one of the most deadly diseases that threaten human life and health. However, developing new drugs is challenging due to lengthy trials, funding constraints, and regulatory approval procedures. Consequently, researchers have devoted themselves to transforming some clinically approved old drugs into antitumor drugs with certain active ingredients, which have become an attractive alternative. Disulfiram (DSF), an antialcohol medication, can rapidly metabolize in the physiological environment into diethyldithiocarbamate (DTC) which can readily react with Cu2+ ions in situ to form the highly toxic bis(N,N-diethyldithiocarbamate)-copper(II) (CuET) complex. In this study, DSF is loaded into mesoporous dopamine nanocarriers and surface-chelated with tannin and Cu2+ to construct M-MDTC nanoprodrugs under the camouflage of K7 tumor cell membranes. After intravenous injection, M-MDTC nanoprodrugs successfully reach the tumor sites with the help of mediated cell membranes. Under slightly acidic pH and photothermal stimulation conditions, DSF and Cu2+ are simultaneously released, forming a highly toxic CuET to kill tumor cells in situ. The generated CuET can also induce immunogenic cell death of tumor cells, increase the proportion of CD86+ CD80+ cells, and promote dendritic cell maturation. In vitro and in vivo studies of M-MDTC nanoprodrugs have shown excellent tumor-cell-killing ability and solid tumor suppression. This approach enables in situ amplification of chemotherapy in the tumor microenvironment, achieving an effective antitumor treatment.

Exploring NCATS in-house biomedical data for evidence-based drug repurposing

Drug repurposing is a strategy for identifying new uses of approved or investigational drugs that are outside the scope of the original medical indication. Even though many repurposed drugs have been found serendipitously in the past, the increasing availability of large volumes of biomedical data has enabled more systemic, data-driven approaches for drug candidate identification. At National Center of Advancing Translational Sciences (NCATS), we invent new methods to generate new data and information publicly available to spur innovation and scientific discovery. In this study, we aimed to explore and demonstrate biomedical data generated and collected via two NCATS research programs, the Toxicology in the 21st Century program (Tox21) and the Biomedical Data Translator (Translator) for the application of drug repurposing. These two programs provide complementary types of biomedical data from uncovering underlying biological mechanisms with bioassay screening data from Tox21 for chemical clustering, to enrich clustered chemicals with scientific evidence mined from the Translator towards drug repurposing. 129 chemical clusters have been generated and three of them have been further investigated for drug repurposing candidate identification, which is detailed as case studies.

Machine Learning Algorithms Identify Target Genes and the Molecular Mechanism of Matrine against Diffuse Large B-cell Lymphoma

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma worldwide. Novel treatment strategies are still needed for this disease.

OBJECTIVE

The present study aimed to systematically explore the potential targets and molecular mechanisms of matrine in the treatment of DLBCL.

METHODS

Potential matrine targets were collected from multiple platforms. Microarray data and clinical characteristics of DLBCL were downloaded from publicly available database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to identify the hub genes of DLBCL using R software. Then, the shared target genes between matrine and DLBCL were identified as the potential targets of matrine against DLBCL. The least absolute shrinkage and selection operator (LASSO) algorithm was used to determine the final core target genes, which were further verified by molecular docking simulation and receiver operating characteristic (ROC) curve analysis. Functional analysis was also performed to elucidate the potential mechanisms.

RESULTS

A total of 222 matrine target genes and 1269 DLBCL hub genes were obtained through multiple databases and machine learning algorithms. From the nine shared target genes of matrine and DLBCL, five final core target genes, including CTSL, NR1H2, PDPK1, MDM2, and JAK3, were identified. Molecular docking showed that the binding of matrine to the core genes was stable. ROC curves also suggested close associations between the core genes and DLBCL. Additionally, functional analysis showed that the therapeutic effect of matrine against DLBCL may be related to the PI3K-Akt signaling pathway.

CONCLUSION

Matrine may target five genes and the PI3K-Akt signaling pathway in DLBCL treatment.

Antifungal Activity of Chitosan against Histoplasma capsulatum in Planktonic and Biofilm Forms: A Therapeutic Strategy in the Future?

Histoplasmosis is a respiratory disease caused by Histoplasma capsulatum, a dimorphic fungus, with high mortality and morbidity rates, especially in immunocompromised patients. Considering the small existing therapeutic arsenal, new treatment approaches are still required. Chitosan, a linear polysaccharide obtained from partial chitin deacetylation, has anti-inflammatory, antimicrobial, biocompatibility, biodegradability, and non-toxicity properties. Chitosan with different deacetylation degrees and molecular weights has been explored as a potential agent against fungal pathogens. In this study, the chitosan antifungal activity against H. capsulatum was evaluated using the broth microdilution assay, obtaining minimum inhibitory concentrations (MIC) ranging from 32 to 128 µg/mL in the filamentous phase and 8 to 64 µg/mL in the yeast phase. Chitosan combined with classical antifungal drugs showed a synergic effect, reducing chitosan's MICs by 32 times, demonstrating that there were no antagonistic interactions relating to any of the strains tested. A synergism between chitosan and amphotericin B or itraconazole was detected in the yeast-like form for all strains tested. For H. capsulatum biofilms, chitosan reduced biomass and metabolic activity by about 40% at 512 µg/mL. In conclusion, studying chitosan as a therapeutic strategy against Histoplasma capsulatum is promising, mainly considering its numerous possible applications, including its combination with other compounds.

The OREGANO knowledge graph for computational drug repurposing

Drug repositioning is a faster and more affordable solution than traditional drug discovery approaches. From this perspective, computational drug repositioning using knowledge graphs is a very promising direction. Knowledge graphs constructed from drug data and information can be used to generate hypotheses (molecule/drug - target links) through link prediction using machine learning algorithms. However, it remains rare to have a holistically constructed knowledge graph using the broadest possible features and drug characteristics, which is freely available to the community. The OREGANO knowledge graph aims at filling this gap. The purpose of this paper is to present the OREGANO knowledge graph, which includes natural compounds related data. The graph was developed from scratch by retrieving data directly from the knowledge sources to be integrated. We therefore designed the expected graph model and proposed a method for merging nodes between the different knowledge sources, and finally, the data were cleaned. The knowledge graph, as well as the source codes for the ETL process, are openly available on the GitHub of the OREGANO project ( https://gitub.u-bordeaux.fr/erias/oregano ).

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.

Exploring NCATS In-House Biomedical Data for Evidence-based Drug Repurposing

Drug repurposing is a strategy for identifying new uses of approved or investigational drugs that are outside the scope of the original medical indication. Even though many repurposed drugs have been found serendipitously in the past, the increasing availability of large volumes of biomedical data has enabled more systemic, data-driven approaches for drug candidate identification. At National Center of Advancing Translational Sciences (NCATS), we invent new methods to generate new data and information publicly available to spur innovation and scientific discovery. In this study, we aimed to explore and demonstrate biomedical data generated and collected via two NCATS research programs, the Toxicology in the 21st Century program (Tox21) and the Biomedical Data Translator (Translator) for the application of drug repurposing. These two programs provide complementary types of biomedical data from uncovering underlying biological mechanisms with bioassay screening data from Tox21 for chemical clustering, to enrich clustered chemicals with scientific evidence mined from the Translator towards drug repurposing. 129 chemical clusters have been generated and three of them have been further investigated for drug repurposing candidate identification, which is detailed as case studies.

The critical role of mode of action studies in kinetoplastid drug discovery

Understanding the target and mode of action of compounds identified by phenotypic screening can greatly facilitate the process of drug discovery and development. Here, we outline the tools currently available for target identification against the neglected tropical diseases, human African trypanosomiasis, visceral leishmaniasis and Chagas' disease. We provide examples how these tools can be used to identify and triage undesirable mechanisms, to identify potential toxic liabilities in patients and to manage a balanced portfolio of target-based campaigns. We review the primary targets of drugs that are currently in clinical development that were initially identified via phenotypic screening, and whose modes of action affect protein turnover, RNA trans-splicing or signalling in these protozoan parasites.

Quinolines and isoquinolines as HIV-1 inhibitors: Chemical structures, action targets, and biological activities

Human immunodeficiency virus type 1 (HIV-1), a lentivirus that causes acquired immunodeficiency syndrome (AIDS), poses a serious threat to global public health. Since the advent of the first drug zidovudine, a number of anti-HIV agents acting on different targets have been approved to combat HIV/AIDS. Among the abundant heterocyclic families, quinoline and isoquinoline moieties are recognized as promising scaffolds for HIV inhibition. This review intends to highlight the advances in diverse chemical structures and abundant biological activity of quinolines and isoquinolines as anti-HIV agents acting on different targets, which aims to provide useful references and inspirations to design and develop novel HIV inhibitors for medicinal chemists.

Identification of LRRK2 Inhibitors through Computational Drug Repurposing

Parkinson's disease (PD) is the second most common neurodegenerative disorder that affects more than ten million people worldwide. However, the current PD treatments are still limited and alternative treatment strategies are urgently required. Leucine-rich repeat kinase 2 (LRRK2) has been recognized as a promising target for PD treatment. However, there are no approved LRRK2 inhibitors on the market. To rapidly identify potential drug repurposing candidates that inhibit LRRK2 kinase, we report a structure-based drug repurposing workflow that combines molecular docking, recursive partitioning model, molecular dynamics (MD) simulation, and molecular mechanics-generalized Born surface area (MM-GBSA) calculation. Thirteen compounds screened from our drug repurposing workflow were further evaluated through the experiment. The experimental results showed six drugs (Abivertinib, Aumolertinib, Encorafenib, Bosutinib, Rilzabrutinib, and Mobocertinib) with IC₅₀ less than 5 μM that were identified as potential LRRK2 kinase inhibitors. The most potent compound Abivertinib showed potent inhibitions with IC₅₀ toward G2019S mutation and wild-type LRRK2 of 410.3 nM and 177.0 nM, respectively. Our combination screening strategy had a 53% hit rate in this repurposing task. MD simulations and MM-GBSA free energy analysis further revealed the atomic binding mechanism between the identified drugs and G2019S LRRK2. In summary, the results showed that our drug repurposing workflow could be used to identify potent compounds for LRRK2. The potent inhibitors discovered in our work can be a starting point to develop more effective LRRK2 inhibitors.

An Integrative Heterogeneous Graph Neural Network-Based Method for Multi-Labeled Drug Repurposing

Drug repurposing is the process of discovering new indications (i.e., diseases or conditions) for already approved drugs. Many computational methods have been proposed for predicting new associations between drugs and diseases. In this article, we proposed a new method, called DR-HGNN, an integrative heterogeneous graph neural network-based method for multi-labeled drug repurposing, to discover new indications for existing drugs. For this purpose, we first used the DTINet dataset to construct a heterogeneous drug–protein–disease (DPD) network, which is a graph composed of four types of nodes (drugs, proteins, diseases, and drug side effects) and eight types of edges. Second, we labeled each drug–protein edge, dp_i,j = (d_i, p_j), of the DPD network with a set of diseases, {δ_i,j,1, … , δ_i,j,k} associated with both d_i and p_j and then devised multi-label ranking approaches which incorporate neural network architecture that operates on the heterogeneous graph-structured data and which leverages both the interaction patterns and the features of drug and protein nodes. We used a derivative of the GraphSAGE algorithm, HinSAGE, on the heterogeneous DPD network to learn low-dimensional vector representation of features of drugs and proteins. Finally, we used the drug–protein network to learn the embeddings of the drug–protein edges and then predict the disease labels that act as bridges between drugs and proteins. The proposed method shows better results than existing methods applied to the DTINet dataset, with an AUC of 0.964.

Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents

Repositioning or repurposing drugs account for a substantial part of entering approval pipeline drugs, which indicates that drug repositioning has huge market potential and value. Computational technologies such as machine learning methods have accelerated the process of drug repositioning in the last few decades years. The repositioning potential of type 2 diabetes mellitus (T2DM) drugs for various diseases such as cancer, neurodegenerative diseases, and cardiovascular diseases have been widely studied. Hence, the related summary about repurposing antidiabetic drugs is of great significance. In this review, we focus on the machine learning methods for the development of new T2DM drugs and give an overview of the repurposing potential of the existing antidiabetic agents.

DeepMHADTA: Prediction of Drug-Target Binding Affinity Using Multi-Head Self-Attention and Convolutional Neural Network

Drug-target interactions provide insight into the drug-side effects and drug repositioning. However, wet-lab biochemical experiments are time-consuming and labor-intensive, and are insufficient to meet the pressing demand for drug research and development. With the rapid advancement of deep learning, computational methods are increasingly applied to screen drug-target interactions. Many methods consider this problem as a binary classification task (binding or not), but ignore the quantitative binding affinity. In this paper, we propose a new end-to-end deep learning method called DeepMHADTA, which uses the multi-head self-attention mechanism in a deep residual network to predict drug-target binding affinity. On two benchmark datasets, our method outperformed several current state-of-the-art methods in terms of multiple performance measures, including mean square error (MSE), consistency index (CI), rm2, and PR curve area (AUPR). The results demonstrated that our method achieved better performance in predicting the drug–target binding affinity.

Hidradenitis Suppurativa and Comorbid Disorder Biomarkers, Druggable Genes, New Drugs and Drug Repurposing-A Molecular Meta-Analysis

Chronic inflammation and dysregulated epithelial differentiation, especially of hair follicle keratinocytes, have been suggested as the major pathogenetic pathways of hidradenitis suppurativa/acne inversa (HS). On the other hand, obesity and metabolic syndrome have additionally been considered as an important risk factor. With adalimumab, a drug has already been approved and numerous other compounds are in advanced-stage clinical studies. A systematic review was conducted to detect and corroborate HS pathogenetic mechanisms at the molecular level and identify HS molecular markers. The obtained data were used to confirm studied and off-label administered drugs and to identify additional compounds for drug repurposing. A robust, strongly associated group of HS biomarkers was detected. The triad of HS pathogenesis, namely upregulated inflammation, altered epithelial differentiation and dysregulated metabolism/hormone signaling was confirmed, the molecular association of HS with certain comorbid disorders, such as inflammatory bowel disease, arthritis, type I diabetes mellitus and lipids/atherosclerosis/adipogenesis was verified and common biomarkers were identified. The molecular suitability of compounds in clinical studies was confirmed and 31 potential HS repurposing drugs, among them 10 drugs already launched for other disorders, were detected. This systematic review provides evidence for the importance of molecular studies to advance the knowledge regarding pathogenesis, future treatment and biomarker-supported clinical course follow-up in HS.

Targeting Metalloenzymes by Boron-Containing Metal-Binding Pharmacophores

Metalloenzymes have critical roles in a wide range of biological processes and are directly involved in many human diseases; hence, they are considered as important targets for therapeutic intervention. The specific characteristics of metal ion(s)-containing active sites make exploitation of metal-binding pharmacophores (MBPs) critical to inhibitor development targeting metalloenzymes. This Perspective focuses on boron-containing MBPs, which display unique binding modes with metalloenzyme active sites, particularly via mimicking native substrates or tetrahedral transition states. The design concepts regarding boron-containing MBPs are highlighted through the case analyses on five distinct classes of clinically relevant nucleophilic metalloenzymes from medicinal chemistry perspectives. The challenges (e.g., selectivity) faced by some boron-containing MBPs and possible strategies (e.g., bioisosteres) for metalloenzyme inhibitor transformation are also discussed.