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The exploration of drug-target interactions (DTI) is an essential stage in the drug development pipeline. Thanks to the assistance of computational models, notably in the deep learning approach, scientists have been able to shorten the time spent on this stage. Widely practiced deep learning algorithms such as convolutional neural networks and recurrent neural networks are commonly employed in DTI prediction projects. However, they can hardly utilize the natural graph structure of molecular inputs. For that reason, a graph neural network (GNN) is an applicable choice for learning the chemical and structural characteristics of molecules when it represents molecular compounds as graphs and learns the compound features from those graphs. In an effort to construct an advanced deep learning-based model for DTI prediction, we propose Deep Neural Computation (DeepNC), which is a framework utilizing three GNN algorithms: Generalized Aggregation Networks (GENConv), Graph Convolutional Networks (GCNConv), and Hypergraph Convolution-Hypergraph Attention (HypergraphConv). In short, our framework learns the features of drugs and targets by the layers of GNN and 1-D convolution network, respectively. Then, representations of the drugs and targets are fed into fully-connected layers to predict the binding affinity values. The models of DeepNC were evaluated on two benchmarked datasets (Davis, Kiba) and one independently proposed dataset (Allergy) to confirm that they are suitable for predicting the binding affinity of drugs and targets. Moreover, compared to the results of baseline methods that worked on the same problem, DeepNC proves to improve the performance in terms of mean square error and concordance index.

Elevations in the intracellular Ca2+ concentration are a phenomena commonly observed during stem cell differentiation but cease after the process is complete. The transient receptor potential melastatin 4 (TRPM4) is an ion channel that controls Ca2+ signals in excitable and nonexcitable cells. However, its role in stem cells remains unknown. The aim of this study was to characterize TRPM4 in rat dental follicle stem cells (DFSCs) and to determine its impact on Ca2+ signaling and the differentiation process. We identified TRPM4 gene expression in DFSCs, but not TRPM5, a closely related channel with similar function. Perfusion of cells with increasing buffered Ca2+ resulted in a concentration-dependent activation of currents typical for TRPM4, which were also voltage-dependent and had Na+ conductivity. Molecular suppression with shRNA decreased channel activity and cell proliferation during osteogenesis but not adipogenesis. As a result, enhanced mineralization and phosphatase enzyme activity were observed during osteoblast formation, although DFSCs failed to differentiate into adipocytes. Furthermore, the normal agonist-induced first and secondary phases of Ca2+ signals were transformed into a gradual and sustained increase which confirmed the channels' ability to control Ca2+ signaling. Using whole genome microarray analysis, we identified several genes impacted by TRPM4 during DFSC differentiation. These findings suggest an inhibitory role for TRPM4 on osteogenesis while it appears to be required for adipogenesis. The data also provide a potential link between the Ca2+ signaling pattern and gene expression during stem cell differentiation. S TEM C ells 2013;31:167-177 [ABSTRACT FROM AUTHOR]