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The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for identifying CDGs, termed ECD-CDGI. This model is the first to design an ECD-Attention encoder by combining the ECD technique with an attention mechanism. ECD-Attention encoder excels at generating robust gene representations that reveal the complex interdependencies among genes while reducing the impact of data noise. We concatenate topological embedding extracted from gene-gene networks through graph transformers to these gene representations. We conduct extensive experiments across three testing scenarios. Extensive experiments show that the ECD-CDGI model possesses the ability to not only be proficient in identifying known CDGs but also efficiently uncover unknown potential CDGs. Furthermore, compared to the GNN-based approach, the ECD-CDGI model exhibits fewer constraints by existing gene-gene networks, thereby enhancing its capability to identify CDGs. Additionally, ECD-CDGI is open-source and freely available. We have also launched the model as a complimentary online tool specifically crafted to expedite research efforts focused on CDGs identification. Author summary: Cancer has become a major disease threatening human life and health. Cancer usually originates from abnormal gene activities, such as mutations and copy number variations. Mutations in cancer driver genes are crucial for the selective growth of tumor cells. Identifying cancer driver genes is crucial in cancer-related research and treatment strategies, as it helps understand cancer occurrence and development. However, the complex gene-gene interactions, measurement errors, and the prevalence of unlabeled data significantly complicate the identification of these driver genes. We developed a new method that integrates an energy-constrained diffusion mechanism with an attention mechanism to uncover implicit gene dependencies in biomolecular networks and generate robust gene representations. Extensive experiments demonstrated that our model accurately identifies known cancer driver genes and effectively discovers potential ones. Furthermore, we analyzed and predicted patient-specific mutated genes, enhancing our understanding of their pathogenesis and advancing precision medicine. In summary, our method offers a promising tool for advancing the identification of cancer driver genes. [ABSTRACT FROM AUTHOR]

Motivation Accurate prediction of acute dermal toxicity (ADT) is essential for the safe and effective development of contact drugs. Currently, graph neural networks, a form of deep learning technology, accurately model the structure of compound molecules, enhancing predictions of their ADT. However, many existing methods emphasize atom-level information transfer and overlook crucial data conveyed by molecular bonds and their interrelationships. Additionally, these methods often generate "equal" node representations across the entire graph, failing to accentuate "important" substructures like functional groups, pharmacophores, and toxicophores, thereby reducing interpretability. Results We introduce a novel model, GraphADT, utilizing structure remapping and multi-view graph pooling (MVPool) technologies to accurately predict compound ADT. Initially, our model applies structure remapping to better delineate bonds, transforming "bonds" into new nodes and "bond-atom-bond" interactions into new edges, thereby reconstructing the compound molecular graph. Subsequently, we use MVPool to amalgamate data from various perspectives, minimizing biases inherent to single-view analyses. Following this, the model generates a robust node ranking collaboratively, emphasizing critical nodes or substructures to enhance model interpretability. Lastly, we apply a graph comparison learning strategy to train both the original and structure remapped molecular graphs, deriving the final molecular representation. Experimental results on public datasets indicate that the GraphADT model outperforms existing state-of-the-art models. The GraphADT model has been demonstrated to effectively predict compound ADT, offering potential guidance for the development of contact drugs and related treatments. Availability and implementation Our code and data are accessible at: https://github.com/mxqmxqmxq/GraphADT.git. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are found ubiquitously in biological cells and play a pivotal role in regulating the expression of numerous target genes. Therapies centered around miRNAs are emerging as a promising strategy for disease treatment, aiming to intervene in disease progression by modulating abnormal miRNA expressions. The accurate prediction of miRNA–drug resistance (MDR) is crucial for the success of miRNA therapies. Computational models based on deep learning have demonstrated exceptional performance in predicting potential MDRs. However, their effectiveness can be compromised by errors in the data acquisition process, leading to inaccurate node representations. To address this challenge, we introduce the GAM-MDR model, which combines the graph autoencoder (GAE) with random path masking techniques to precisely predict potential MDRs. The reliability and effectiveness of the GAM-MDR model are mainly reflected in two aspects. Firstly, it efficiently extracts the representations of miRNA and drug nodes in the miRNA–drug network. Secondly, our designed random path masking strategy efficiently reconstructs critical paths in the network, thereby reducing the adverse impact of noisy data. To our knowledge, this is the first time that a random path masking strategy has been integrated into a GAE to infer MDRs. Our method was subjected to multiple validations on public datasets and yielded promising results. We are optimistic that our model could offer valuable insights for miRNA therapeutic strategies and deepen the understanding of the regulatory mechanisms of miRNAs. Our data and code are publicly available at GitHub: https://github.com/ZZCrazy00/GAM-MDR. [ABSTRACT FROM AUTHOR]

Motivation Accurately predicting molecular metabolic stability is of great significance to drug research and development, ensuring drug safety and effectiveness. Existing deep learning methods, especially graph neural networks, can reveal the molecular structure of drugs and thus efficiently predict the metabolic stability of molecules. However, most of these methods focus on the message passing between adjacent atoms in the molecular graph, ignoring the relationship between bonds. This makes it difficult for these methods to estimate accurate molecular representations, thereby being limited in molecular metabolic stability prediction tasks. Results We propose the MS-BACL model based on bond graph augmentation technology and contrastive learning strategy, which can efficiently and reliably predict the metabolic stability of molecules. To our knowledge, this is the first time that bond-to-bond relationships in molecular graph structures have been considered in the task of metabolic stability prediction. We build a bond graph based on 'atom-bond-atom', and the model can simultaneously capture the information of atoms and bonds during the message propagation process. This enhances the model's ability to reveal the internal structure of the molecule, thereby improving the structural representation of the molecule. Furthermore, we perform contrastive learning training based on the molecular graph and its bond graph to learn the final molecular representation. Multiple sets of experimental results on public datasets show that the proposed MS-BACL model outperforms the state-of-the-art model. Availability and Implementation The code and data are publicly available at https://github.com/taowang11/MS. [ABSTRACT FROM AUTHOR]

Motivation Accurate inference of potential drug–protein interactions (DPIs) aids in understanding drug mechanisms and developing novel treatments. Existing deep learning models, however, struggle with accurate node representation in DPI prediction, limiting their performance. Results We propose a new computational framework that integrates global and local features of nodes in the drug–protein bipartite graph for efficient DPI inference. Initially, we employ pre-trained models to acquire fundamental knowledge of drugs and proteins and to determine their initial features. Subsequently, the MinHash and HyperLogLog algorithms are utilized to estimate the similarity and set cardinality between drug and protein subgraphs, serving as their local features. Then, an energy-constrained diffusion mechanism is integrated into the transformer architecture, capturing interdependencies between nodes in the drug–protein bipartite graph and extracting their global features. Finally, we fuse the local and global features of nodes and employ multilayer perceptrons to predict the likelihood of potential DPIs. A comprehensive and precise node representation guarantees efficient prediction of unknown DPIs by the model. Various experiments validate the accuracy and reliability of our model, with molecular docking results revealing its capability to identify potential DPIs not present in existing databases. This approach is expected to offer valuable insights for furthering drug repurposing and personalized medicine research. Availability and implementation Our code and data are accessible at: https://github.com/ZZCrazy00/DPI. [ABSTRACT FROM AUTHOR]

The development of effective inhibitors targeting the Kirsten rat sarcoma viral proto-oncogene (KRASG12D) mutation, a prevalent oncogenic driver in cancer, represents a significant unmet need in precision medicine. In this study, an integrated computational approach combining structure-based virtual screening and molecular dynamics simulation was employed to identify novel noncovalent inhibitors targeting the KRASG12D variant. Through virtual screening of over 1.7 million diverse compounds, potential lead compounds with high binding affinity and specificity were identified using molecular docking and scoring techniques. Subsequently, 200 ns molecular dynamics simulations provided critical insights into the dynamic behavior, stability, and conformational changes of the inhibitor-KRASG12D complexes, facilitating the selection of lead compounds with robust binding profiles. Additionally, in silico absorption, distribution, metabolism, excretion (ADME) profiling, and toxicity predictions were applied to prioritize the lead compounds for further experimental validation. The discovered noncovalent KRASG12D inhibitors exhibit promises as potential candidates for targeted therapy against KRASG12D-driven cancers. This comprehensive computational framework not only expedites the discovery of novel KRASG12D inhibitors but also provides valuable insights for the development of precision treatments tailored to this oncogenic mutation. [ABSTRACT FROM AUTHOR]

Exploring microbial stress responses to drugs is crucial for the advancement of new therapeutic methods. While current artificial intelligence methodologies have expedited our understanding of potential microbial responses to drugs, the models are constrained by the imprecise representation of microbes and drugs. To this end, we combine deep autoencoder and subgraph augmentation technology for the first time to propose a model called JDASA-MRD, which can identify the potential indistinguishable responses of microbes to drugs. In the JDASA-MRD model, we begin by feeding the established similarity matrices of microbe and drug into the deep autoencoder, enabling to extract robust initial features of both microbes and drugs. Subsequently, we employ the MinHash and HyperLogLog algorithms to account intersections and cardinality data between microbe and drug subgraphs, thus deeply extracting the multi-hop neighborhood information of nodes. Finally, by integrating the initial node features with subgraph topological information, we leverage graph neural network technology to predict the microbes' responses to drugs, offering a more effective solution to the 'over-smoothing' challenge. Comparative analyses on multiple public datasets confirm that the JDASA-MRD model's performance surpasses that of current state-of-the-art models. This research aims to offer a more profound insight into the adaptability of microbes to drugs and to furnish pivotal guidance for drug treatment strategies. Our data and code are publicly available at: https://github.com/ZZCrazy00/JDASA-MRD. [ABSTRACT FROM AUTHOR]

Studies have shown that the mechanism of action of many drugs is related to miRNA. In-depth research on the relationship between miRNA and drugs can provide theoretical foundations and practical approaches for various areas, such as drug target discovery, drug repositioning and biomarker research. Traditional biological experiments to test miRNA-drug susceptibility are costly and time-consuming. Thus, sequence- or topology-based deep learning methods are recognized in this field for their efficiency and accuracy. However, these methods have limitations in dealing with sparse topologies and higher-order information of miRNA (drug) feature. In this work, we propose GCFMCL, a model for multi-view contrastive learning based on graph collaborative filtering. To the best of our knowledge, this is the first attempt that incorporates contrastive learning strategy into the graph collaborative filtering framework to predict the sensitivity relationships between miRNA and drug. The proposed multi-view contrastive learning method is divided into topological contrastive objective and feature contrastive objective: (1) For the homogeneous neighbors of the topological graph, we propose a novel topological contrastive learning method via constructing the contrastive target through the topological neighborhood information of nodes. (2) The proposed model obtains feature contrastive targets from high-order feature information according to the correlation of node features, and mines potential neighborhood relationships in the feature space. The proposed multi-view comparative learning effectively alleviates the impact of heterogeneous node noise and graph data sparsity in graph collaborative filtering, and significantly enhances the performance of the model. Our study employs a dataset derived from the NoncoRNA and ncDR databases, encompassing 2049 experimentally validated miRNA-drug sensitivity associations. Five-fold cross-validation shows that the Area Under the Curve (AUC), Area Under the Precision-Recall Curve (AUPR) and F1-score (F1) of GCFMCL reach 95.28%, 95.66% and 89.77%, which outperforms the state-of-the-art (SOTA) method by the margin of 2.73%, 3.42% and 4.96%, respectively. Our code and data can be accessed at https://github.com/kkkayle/GCFMCL. [ABSTRACT FROM AUTHOR]

Circular RNA (circRNA) is closely associated with human diseases. Accordingly, identifying the associations between human diseases and circRNA can help in disease prevention, diagnosis and treatment. Traditional methods are time consuming and laborious. Meanwhile, computational models can effectively predict potential circRNA–disease associations (CDAs), but are restricted by limited data, resulting in data with high dimension and imbalance. In this study, we propose a model based on automatically selected meta-path and contrastive learning, called the MPCLCDA model. First, the model constructs a new heterogeneous network based on circRNA similarity, disease similarity and known association, via automatically selected meta-path and obtains the low-dimensional fusion features of nodes via graph convolutional networks. Then, contrastive learning is used to optimize the fusion features further, and obtain the node features that make the distinction between positive and negative samples more evident. Finally, circRNA–disease scores are predicted through a multilayer perceptron. The proposed method is compared with advanced methods on four datasets. The average area under the receiver operating characteristic curve, area under the precision-recall curve and F1 score under 5-fold cross-validation reached 0.9752, 0.9831 and 0.9745, respectively. Simultaneously, case studies on human diseases further prove the predictive ability and application value of this method. [ABSTRACT FROM AUTHOR]