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Abstract 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.

Abstract Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

Abstract Background Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects. Results Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of “interaction-affinity-binding sites”. Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN. Conclusions Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .

Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing. However, traditional machine learning methods for calculating DTA often lack accuracy, posing a significant challenge in accurately predicting DTA. Fortunately, deep learning has emerged as a promising approach in computational biology, leading to the development of various deep learning-based methods for DTA prediction. To support researchers in developing novel and highly precision methods, we have provided a comprehensive review of recent advances in predicting DTA using deep learning. We firstly conducted a statistical analysis of commonly used public datasets, providing essential information and introducing the used fields of these datasets. We further explored the common representations of sequences and structures of drugs and targets. These analyses served as the foundation for constructing DTA prediction methods based on deep learning. Next, we focused on explaining how deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer, and Graph Neural Networks (GNNs), were effectively employed in specific DTA prediction methods. We highlighted the unique advantages and applications of these models in the context of DTA prediction. Finally, we conducted a performance analysis of multiple state-of-the-art methods for predicting DTA based on deep learning. The comprehensive review aimed to help researchers understand the shortcomings and advantages of existing methods, and further develop high-precision DTA prediction tool to promote the development of drug discovery.

Nymphaea atrans belongs to the subgenus Anecphya and displays varied flower colors over successive days. Because of its excellent ornamental characteristics, this species is widely cultivated in waterscape gardens worldwide. Here, we have sequenced the complete chloroplast genome of N. atrans. The whole genome size is 160,990 bp in length with four subregions: large and small single-copy regions of 90,879 and 19,699 bp, respectively, separated by a pair of inverted repeat regions of 25,206 bp each. A total of 126 genes were annotated, including 82 coding genes, eight ribosomal RNAs, and 36 transfer RNAs. The total GC content of the complete genome was 39%. Phylogenetic analysis showed that N. atrans was closely related to N. immutabilis. In this study, we have provided the chloroplast genome of N. atrans as a valuable resource for further phylogenetic analysis of Nymphaea species.

Anisotropic monolayer materials have always been investigated by physical researchers. In this paper, we report a stable two-dimensional TiNI monolayer with anisotropic mechanical, optical, and electrical conduction properties. By combining the methods of non-equilibrium Green’s function and density function theory, we obtain two-dimensional TiNI materials with mechanical, optical, and electronic transport properties that depend on the lattice orientation. In addition, the maximum Young’s modulus of the single-layer TiNI can reach 160 N/m2. The calculate result of electrical transport properties also indicates the anisotropic electron transport performance of TiNI monolayer. Moreover, the electron transport intensity along the direction b is about six times the conduction intensity along the direction a. The anisotropic mechanical and optical properties, as well as the tunable band gap and special electron transport characteristics, enable a promising future for monolayer TiNI materials in nano-optoelectronics.

C11H10F2N4O, monoclinic, P21/n (no. 14), a = 8.7616(10) Å, b = 14.2418(17) Å, c = 9.7191(12) Å, β = 107.6020°, V = 1156.0(2) Å3, Z = 4, Rgt(F) = 0.0431, wRref(F2) = 0.1263, T = 296(2) K.

C13H16O6, monoclinic, P21/c (no. 14), a = 12.180(3) Å, b = 13.813(4) Å, c = 7.842(2) Å, β = 93.917(4)°, V = 1316.2(6) Å3, Z = 4, Rgt(F) = 0.0442, wRref(F2) = 0.1375, T = 296(2) K.

Two new oxidovanadium(V) complexes, [VOL1(HQ)] (1) and [VOL2(SAH)] (2), were prepared by the reaction of [VO(acac)2] (where acac = acetylacetonate) with N'-(3-ethoxy-2-hydroxybenzylidene)nicotinohydrazide (H2L1) and 8-hydroxyquinoline (HHQ), and N'-(2-hydroxy-4-methoxybenzylidene)nicotinohydrazide (H2L2) and salicylhydroxamic acid (HSAH), respectively, in methanol. Crystal and molecular structures of the complexes were determined by elemental analysis, infrared spectroscopy and single crystal X-ray diffraction. The V atoms in both complexes are in octahedral coordination. Thermal stability of the complexes was studied. Both complexes can decrease the blood glucose level in alloxan-diabetic mice, but the blood glucose level in the treated normal mice was not altered. 搜索 复制