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Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of well-defined targets. While chemical-induced transcriptional profiles offer a comprehensive view of drug mechanisms, inherent noise often obscures the true signal, hindering their potential for meaningful insights. Here, we highlight the development of TranSiGen, a deep generative model employing self-supervised representation learning. TranSiGen analyzes basal cell gene expression and molecular structures to reconstruct chemical-induced transcriptional profiles with high accuracy. By capturing both cellular and compound information, TranSiGen-derived representations demonstrate efficacy in diverse downstream tasks like ligand-based virtual screening, drug response prediction, and phenotype-based drug repurposing. Notably, in vitro validation of TranSiGen\'s application in pancreatic cancer drug discovery highlights its potential for identifying effective compounds. We envisage that integrating TranSiGen into the drug discovery and mechanism research holds significant promise for advancing biomedicine.

The application of ChatGPTin the medical field has sparked debate regarding its accuracy. To address this issue, we present a Multi-Role ChatGPT Framework (MRCF), designed to improve ChatGPT\'s performance in medical data analysis by optimizing prompt words, integrating real-world data, and implementing quality control protocols. Compared to the singular ChatGPT model, MRCF significantly outperforms traditional manual analysis in interpreting medical data, exhibiting fewer random errors, higher accuracy, and better identification of incorrect information. Notably, MRCF is over 600 times more time-efficient than conventional manual annotation methods and costs only one-tenth as much. Leveraging MRCF, we have established two user-friendly databases for efficient and straightforward drug repositioning analysis. This research not only enhances the accuracy and efficiency of ChatGPT in medical data science applications but also offers valuable insights for data analysis models across various professional domains.

UNASSIGNED: Acanthamoeba infection is a serious public health concern, necessitating the development of effective and safe anti-Acanthamoeba chemotherapies. Poly (ADP-ribose) polymerases (PARPs) govern a colossal amount of biological processes, such as DNA damage repair, protein degradation and apoptosis. Multiple PARP-targeted compounds have been approved for cancer treatment. However, repurposing of PARP inhibitors to treat Acanthamoeba is poorly understood.
UNASSIGNED: In the present study, we attempted to fill these knowledge gaps by performing anti-Acanthamoeba efficacy assays, cell biology experiments, bioinformatics, and transcriptomic analyses.
UNASSIGNED: Using a homology model of Acanthamoeba poly (ADP-ribose) polymerases (PARPs), molecular docking of approved drugs revealed three potential inhibitory compounds: olaparib, venadaparib and AZ9482. In particular, venadaparib exhibited superior docking scores (-13.71) and favorable predicted binding free energy (-89.28 kcal/mol), followed by AZ9482, which showed a docking score of -13.20 and a binding free energy of -92.13 kcal/mol. Notably, the positively charged cyclopropylamine in venadaparib established a salt bridge (through E535) and a hydrogen bond (via N531) within the binding pocket. For comparison, AZ9482 was well stacked by the surrounding aromatic residues including H625, Y652, Y659 and Y670. In an assessment of trophozoites viability, AZ9482 exhibited a dose-and time-dependent anti-trophozoite effect by suppressing Acanthamoeba PARP activity, unlike olaparib and venadaparib. An Annexin V-fluorescein isothiocyanate/propidium iodide apoptosis assay revealed AZ9482 induced trophozoite necrotic cell death rather than apoptosis. Transcriptomics analyses conducted on Acanthamoeba trophozoites treated with AZ9482 demonstrated an atlas of differentially regulated proteins and genes, and found that AZ9482 rapidly upregulates a multitude of DNA damage repair pathways in trophozoites, and intriguingly downregulates several virulent genes. Analyzing gene expression related to DNA damage repair pathway and the rate of apurinic/apyrimidinic (AP) sites indicated DNA damage efficacy and repair modulation in Acanthamoeba trophozoites following AZ9482 treatment.
UNASSIGNED: Collectively, these findings highlight AZ9482, as a structurally unique PARP inhibitor, provides a promising prototype for advancing anti-Acanthamoeba drug research.

BACKGROUND: The rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been made in studying binary relationships, further research is needed to deepen our understanding of ternary relationships. The application of graph neural networks in drug retargeting is increasing, but further research is needed to determine the appropriate modelling method for ternary relationships and how to capture their complex multi-feature structure.
RESULTS: The aim of this study was to construct relationships among drug, targets and diseases. To represent the complex relationships among these entities, we used a heterogeneous graph structure. Additionally, we propose a DTD-GNN model that combines graph convolutional networks and graph attention networks to learn feature representations and association information, facilitating a more thorough exploration of the relationships. The experimental results demonstrate that the DTD-GNN model outperforms other graph neural network models in terms of AUC, Precision, and F1-score. The study has important implications for gaining a comprehensive understanding of the relationships between drugs and diseases, as well as for further research and application in exploring the mechanisms of drug-disease interactions. The study reveals these relationships, providing possibilities for innovative therapeutic strategies in medicine.

The repurposing of existing drugs, referred to as theranostics, has made profound impacts on precision medicine. Indocyanine green (ICG), a well-established and clinical dye, has continued to be a star agent, described as a multifunctional molecule with concurrent photo- or sono-sensitiveness capabilities and co-delivery accessibility, showing remarkable potential in the area of unimodal or multimodal imaging-guided therapy of various diseases, leading to the extensive consideration of immediate clinical translations. In this review, we strive to bring the understanding of repurposing performance assessment for ICG into practice by clarifying the relationships between its features and applicability. Specifically, we address the obstacles encountered in the process of developing an ICG repurposing strategy, as well as the noteworthy advancements made in the field of ICG repurposing. We also go into detail about the structure-function correlations of drugs containing ICG and how different structural groups significantly affect the physicochemical properties.

Drug repositioning is a strategy of repurposing approved drugs for treating new indications, which can accelerate the drug discovery process, reduce development costs, and lower the safety risk. The advancement of biotechnology has significantly accelerated the speed and scale of biological data generation, offering significant potential for drug repositioning through biomedical knowledge graphs that integrate diverse entities and relations from various biomedical sources. To fully learn the semantic information and topological structure information from the biological knowledge graph, we propose a knowledge graph convolutional network with a heuristic search, named KGCNH, which can effectively utilize the diversity of entities and relationships in biological knowledge graphs, as well as topological structure information, to predict the associations between drugs and diseases. Specifically, we design a relation-aware attention mechanism to compute the attention scores for each neighboring entity of a given entity under different relations. To address the challenge of randomness of the initial attention scores potentially impacting model performance and to expand the search scope of the model, we designed a heuristic search module based on Gumbel-Softmax, which uses attention scores as heuristic information and introduces randomness to assist the model in exploring more optimal embeddings of drugs and diseases. Following this module, we derive the relation weights, obtain the embeddings of drugs and diseases through neighborhood aggregation, and then predict drug-disease associations. Additionally, we employ feature-based augmented views to enhance model robustness and mitigate overfitting issues. We have implemented our method and conducted experiments on two data sets. The results demonstrate that KGCNH outperforms competing methods. In particular, case studies on lithium and quetiapine confirm that KGCNH can retrieve more actual drug-disease associations in the top prediction results.

BACKGROUND: Accurately identifying the drug-target interactions (DTIs) is one of the crucial steps in the drug discovery and drug repositioning process. Currently, many computational-based models have already been proposed for DTI prediction and achieved some significant improvement. However, these approaches pay little attention to fuse the multi-view similarity networks related to drugs and targets in an appropriate way. Besides, how to fully incorporate the known interaction relationships to accurately represent drugs and targets is not well investigated. Therefore, there is still a need to improve the accuracy of DTI prediction models.
RESULTS: In this study, we propose a novel approach that employs Multi-view similarity network fusion strategy and deep Interactive attention mechanism to predict Drug-Target Interactions (MIDTI). First, MIDTI constructs multi-view similarity networks of drugs and targets with their diverse information and integrates these similarity networks effectively in an unsupervised manner. Then, MIDTI obtains the embeddings of drugs and targets from multi-type networks simultaneously. After that, MIDTI adopts the deep interactive attention mechanism to further learn their discriminative embeddings comprehensively with the known DTI relationships. Finally, we feed the learned representations of drugs and targets to the multilayer perceptron model and predict the underlying interactions. Extensive results indicate that MIDTI significantly outperforms other baseline methods on the DTI prediction task. The results of the ablation experiments also confirm the effectiveness of the attention mechanism in the multi-view similarity network fusion strategy and the deep interactive attention mechanism.
METHODS: https://github.com/XuLew/MIDTI.

BACKGROUND: Drug repurposing provides a cost-effective approach to address the need for lung cancer prevention and therapeutics. We aimed to identify actionable druggable targets using Mendelian randomization (MR).
METHODS: Summary-level data of gene expression quantitative trait loci (eQTLs) were sourced from the eQTLGen resource. We procured genetic associations with lung cancer and its subtypes from the TRICL, ILCCO studies (discovery) and the FinnGen study (replication). We implemented Summary-data-based Mendelian Randomization analysis to identify potential therapeutic targets for lung cancer. Colocalization analysis was further conducted to assess whether the identified signal pairs shared a causal genetic variant.
RESULTS: In the main analysis dataset, we identified 55 genes that demonstrate a causal relationship with lung cancer and its subtypes. However, in the replication cohort, only three genes were found to have such a causal association with lung cancer and its subtypes, and of these, HYKK (also known as AGPHD1) was consistently present in both the primary analysis dataset and the replication cohort. Following HEIDI tests and colocalization analyses, it was revealed that HYKK (AGPHD1) is associated with an increased risk of squamous cell carcinoma of the lung, with an odds ratio and confidence interval of OR = 1.28,95%CI = 1.24 to 1.33.
CONCLUSIONS: We have found that the HYKK (AGPHD1) gene is associated with an increased risk of squamous cell carcinoma of the lung, suggesting that this gene may represent a potential therapeutic target for both the prevention and treatment of lung squamous cell carcinoma.

Chronic Heart Failure (CHF) is a significant global public health issue, with high mortality and morbidity rates and associated costs. Disease modules, which are collections of disease-related genes, offer an effective approach to understanding diseases from a biological network perspective. We employed the multi-Steiner tree algorithm within the NeDRex platform to extract CHF disease modules, and subsequently utilized the Trustrank algorithm to rank potential drugs for repurposing. The constructed disease module was then used to investigate the mechanism by which Panax ginseng ameliorates CHF. The active constituents of Panax ginseng were identified through a comprehensive review of the TCMSP database and relevant literature. The Swiss target prediction database was utilized to determine the action targets of these components. These targets were then cross-referenced with the CHF disease module in the STRING database to establish protein-protein interaction (PPI) relationships. Potential action pathways were uncovered through Gene Ontology (GO) and KEGG pathway enrichment analyses on the DAVID platform. Molecular docking, the determination of the interaction of biological macromolecules with their ligands, and visualization were conducted using Autodock Vina, PLIP, and PyMOL, respectively. The findings suggest that drugs such as dasatinib and mitoxantrone, which have low docking scores with key disease proteins and are reported in the literature as effective against CHF, could be promising. Key components of Panax ginseng, including ginsenoside rh4 and ginsenoside rg5, may exert their effects by targeting key proteins such as AKT1, TNF, NFKB1, among others, thereby influencing the PI3K-Akt and calcium signaling pathways. In conclusion, drugs like dasatinib and midostaurin may be suitable for CHF treatment, and Panax ginseng could potentially mitigate the progression of CHF through a multi-component-multi-target-multi-pathway approach. Disease module analysis emerges as an effective strategy for exploring drug repurposing and the mechanisms of traditional Chinese medicine in disease treatment.