HMMF: a hybrid multi-modal fusion framework for predicting drug side effect frequencies.

BACKGROUND: The identification of drug side effects plays a critical role in drug repositioning and drug screening. While clinical experiments yield accurate and reliable information about drug-related side effects, they are costly and time-consuming. Computational models have emerged as a promising alternative to predict the frequency of drug-side effects. However, earlier research has primarily centered on extracting and utilizing representations of drugs, like molecular structure or interaction graphs, often neglecting the inherent biomedical semantics of drugs and side effects. RESULTS: To address the previously mentioned issue, we introduce a hybrid multi-modal fusion framework (HMMF) for predicting drug side effect frequencies. Considering the wealth of biological and chemical semantic information related to drugs and side effects, incorporating multi-modal information offers additional, complementary semantics. HMMF utilizes various encoders to understand molecular structures, biomedical textual representations, and attribute similarities of both drugs and side effects. It then models drug-side effect interactions using both coarse and fine-grained fusion strategies, effectively integrating these multi-modal features. CONCLUSIONS: HMMF exhibits the ability to successfully detect previously unrecognized potential side effects, demonstrating superior performance over existing state-of-the-art methods across various evaluation metrics, including root mean squared error and area under receiver operating characteristic curve, and shows remarkable performance in cold-start scenarios.

GSL-DTI: Graph structure learning network for Drug-Target interaction prediction.

MOTIVATION: Drug-target interaction prediction is an important area of research to predict whether there is an interaction between a drug molecule and its target protein. It plays a critical role in drug discovery and development by facilitating the identification of potential drug candidates and expediting the overall process. Given the time-consuming, expensive, and high-risk nature of traditional drug discovery methods, the prediction of drug-target interactions has become an indispensable tool. Using machine learning and deep learning to tackle this class of problems has become a mainstream approach, and graph-based models have recently received much attention in this field. However, many current graph-based Drug-Target Interaction (DTI) prediction methods rely on manually defined rules to construct the Drug-Protein Pair (DPP) network during the DPP representation learning process. However, these methods fail to capture the true underlying relationships between drug molecules and target proteins. RESULTS: We propose GSL-DTI, an automatic graph structure learning model used for predicting drug-target interactions (DTIs). Initially, we integrate large-scale heterogeneous networks using a graph convolution network based on meta-paths, effectively learning the representations of drugs and target proteins. Subsequently, we construct drug-protein pairs based on these representations. In contrast to previous studies that construct DPP networks based on manual rules, our method introduces an automatic graph structure learning approach. This approach utilizes a filter gate on the affinity scores of DPPs and relies on the classification loss of downstream tasks to guide the learning of the underlying DPP network structure. Based on the learned DPP network, we transform the prediction of drug-target interactions into a node classification problem. The comprehensive experiments conducted on three public datasets have shown the superiority of GSL-DTI in the tasks of DTI prediction. Additionally, GSL-DTI provides a fresh perspective for advancing research in graph structure learning for DTI prediction.

Unlocking Synthesis of Polyhedral Oligomeric Silsesquioxane-Based Three-Dimensional Polycubane Covalent Organic Frameworks.

Reticular chemistry effectively yields porous structures with distinct topological lattices for a broad range of applications. Polyhedral oligomeric silsesquioxane (POSS)-based octatopic building blocks with a rare Oh symmetric configuration and attracting inorganic features have great potential for creating three-dimensional (3D) covalent organic frameworks (COFs) with new topologies. However, the intrinsic flexibility and intensive motion of cubane-type POSS molecules make the construction of 3D regular frameworks challenging. Herein, by fastening three or four POSS cores with per aromatic rigid linker from rational steric directions, we successfully developed serial crystalline 3D COFs with unpresented "the" and scu topologies. Both the experimental and theoretical results proved the formation of target 3D POSS-based COFs. The resultant hybrid networks with designable chemical skeletons and high surface areas maintain the superiorities of both the inorganic and organic components, such as their high compatibility with inorganic salts, abundant periodic electroactive sites, excellent thermal stability, and open multilevel nanochannels. Consequently, the polycubane COFs could serve as outstanding solid electrolytes with a high ionic conductivity of 1.23 × 10-4 S cm-1 and a lithium-ion transference number of 0.86 at room temperature. This work offers a pathway to generate ordered lattices with multiconnected flexible cube motifs and enrich the topologies of 3D COFs for potential applications.

Comparison of CT-derived Plaque Characteristic Index With CMR Perfusion for Ischemia Diagnosis in Stable CAD.

BACKGROUND: Coronary computed tomography angiography (CCTA) and cardiac magnetic resonance (CMR) have been used to diagnose lesion-specific ischemia in patients with coronary artery disease. The aim of this study was to investigate the diagnostic performance of CCTA-derived plaque characteristic index compared with myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) derived from CMR perfusion in the assessment of lesion-specific ischemia. METHODS: Between October 2020 and March 2022, consecutive patients with suspected or known coronary artery disease, who were clinically referred for invasive coronary angiography were prospectively enrolled. All participants sequentially underwent CCTA and CMR and invasive fractional flow reserve within 2 weeks. The diagnostic performance of CCTA-derived plaque characteristics, CMR perfusion-derived stress MBF, and MPR were compared. Lesions with fractional flow reserve ≤0.80 were considered to be hemodynamically significant stenosis. RESULTS: Nighty-two patients with 141 vessels were included in this study. Plaque length, minimum luminal area, plaque area, percent area stenosis, total atheroma volume, vessel volume, lipid-rich volume, spotty calcium, napkin-ring signs, stress MBF, and MPR in flow-limiting stenosis group were significantly different from nonflow-limiting group. The overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of lesion-specific ischemia diagnosis were 61.0%, 55.3%, 63.1%, 35.6%, and 79.3% for stress MBF, and 89.4%, 89.5%, 89.3%, 75.6%, 95.8% for MPR; meanwhile, 82.3%, 79.0%, 84.5%, 65.2%, and 91.6% for CCTA-derived plaque characteristic index. CONCLUSIONS: In our prospective study, CCTA-derived plaque characteristics and MPR derived from CMR performed well in diagnosing lesion-specific myocardial ischemia and were significantly better than stress MBF in stable coronary artery disease.

Excimer laser ablation combined with drug-coated balloon versus drug-coated balloon in the treatment of de novo atherosclerotic lesions in lower extremities (ELABORATE): study protocol for a real-world clinical trial.

BACKGROUND: The efficacy and validity of excimer laser ablation (ELA) in the in-stent restenosis (ISR) has been confirmed. However, its application in de novo atherosclerotic lesions of lower extremity artery disease (LEAD) has not been clearly defined and its procedure has not been standardized. METHODS: ELABORATE is a prospective, multicenter, real-world study designed to evaluate the efficacy and safety between ELA combined with drug-coated balloon (DCB) and DCB alone in de novo atherosclerotic lesions of LEAD. DISCUSSION: ELABORATE is a prospective, multicenter, real-world study designed to assess the efficacy and safety between ELA combined with drug-coated balloon (DCB) and DCB alone in patients with de novo atherosclerotic lesions of LEAD. According to the real-world situation, eligible patients will be allocated to ELA + DCB group (group E) and DCB group (group C). Baseline and follow-up information (at 3, 6, and 12 months) will be collected. The primary efficacy point is primary patency at 12-months, and the secondary efficacy points include clinically driven target lesion reintervention (CD-TLR), change of Rutherford class, ankle-brachial index and ulcer healing rate. These indexes will be assessed and recorded at 3, 6, and 12-month follow-up. Also, safety evaluation, including major adverse event, all-cause mortality through 30-day follow-up, unplanned major amputation, bailout stent and distal embolization, will also be evaluated by an independent core laboratory. All the data will be collected and recorded by the electric data capture system. This study will be finished in 3 years and the 12-month results will be available in 2023. All the patients will be followed for 5 years. Trial registration number Chinese Clinical Trial Registry (ChiCTR2100051263). Registered 17 September 2019. http://www.chictr.org.cn/listbycreater.aspx .

Supervised graph co-contrastive learning for drug-target interaction prediction.

MOTIVATION: Identification of Drug-Target Interactions (DTIs) is an essential step in drug discovery and repositioning. DTI prediction based on biological experiments is time-consuming and expensive. In recent years, graph learning-based methods have aroused widespread interest and shown certain advantages on this task, where the DTI prediction is often modeled as a binary classification problem of the nodes composed of drug and protein pairs (DPPs). Nevertheless, in many real applications, labeled data are very limited and expensive to obtain. With only a few thousand labeled data, models could hardly recognize comprehensive patterns of DPP node representations, and are unable to capture enough commonsense knowledge, which is required in DTI prediction. Supervised contrastive learning gives an aligned representation of DPP node representations with the same class label. In embedding space, DPP node representations with the same label are pulled together, and those with different labels are pushed apart. RESULTS: We propose an end-to-end supervised graph co-contrastive learning model for DTI prediction directly from heterogeneous networks. By contrasting the topology structures and semantic features of the drug-protein-pair network, as well as the new selection strategy of positive and negative samples, SGCL-DTI generates a contrastive loss to guide the model optimization in a supervised manner. Comprehensive experiments on three public datasets demonstrate that our model outperforms the SOTA methods significantly on the task of DTI prediction, especially in the case of cold start. Furthermore, SGCL-DTI provides a new research perspective of contrastive learning for DTI prediction. AVAILABILITY AND IMPLEMENTATION: The research shows that this method has certain applicability in the discovery of drugs, the identification of drug-target pairs and so on.

Drug-target interaction predication via multi-channel graph neural networks.

Drug-target interaction (DTI) is an important step in drug discovery. Although there are many methods for predicting drug targets, these methods have limitations in using discrete or manual feature representations. In recent years, deep learning methods have been used to predict DTIs to improve these defects. However, most of the existing deep learning methods lack the fusion of topological structure and semantic information in DPP representation learning process. Besides, when learning the DPP node representation in the DPP network, the different influences between neighboring nodes are ignored. In this paper, a new model DTI-MGNN based on multi-channel graph convolutional network and graph attention is proposed for DTI prediction. We use two independent graph attention networks to learn the different interactions between nodes for the topology graph and feature graph with different strengths. At the same time, we use a graph convolutional network with shared weight matrices to learn the common information of the two graphs. The DTI-MGNN model combines topological structure and semantic features to improve the representation learning ability of DPPs, and obtain the state-of-the-art results on public datasets. Specifically, DTI-MGNN has achieved a high accuracy in identifying DTIs (the area under the receiver operating characteristic curve is 0.9665).

Perovskite Quantum Dots Encapsulated in a Mesoporous Metal-Organic Framework as Synergistic Photocathode Materials.

Metal halide perovskite quantum dots, with high light-absorption coefficients and tunable electronic properties, have been widely studied as optoelectronic materials, but their applications in photocatalysis are hindered by their insufficient stability because of the oxidation and agglomeration under light, heat, and atmospheric conditions. To address this challenge, herein, we encapsulated CsPbBr3 nanocrystals into a stable iron-based metal-organic framework (MOF) with mesoporous cages (∼5.5 and 4.2 nm) via a sequential deposition route to obtain a perovskite-MOF composite material, CsPbBr3@PCN-333(Fe), in which CsPbBr3 nanocrystals were stabilized from aggregation or leaching by the confinement effect of MOF cages. The monodispersed CsPbBr3 nanocrystals (4-5 nm) within the MOF lattice were directly observed by transmission electron microscopy and corresponding mapping analysis and further confirmed by powder X-ray diffraction, infrared spectroscopy, and N2 adsorption characterizations. Density functional theory calculations further suggested a significant interfacial charge transfer from CsPbBr3 quantum dots to PCN-333(Fe), which is ideal for photocatalysis. The CsPbBr3@PCN-333(Fe) composite exhibited excellent and stable oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) catalytic activities in aprotic systems. Furthermore, CsPbBr3@PCN-333(Fe) composite worked as the synergistic photocathode in the photoassisted Li-O2 battery, where CsPbBr3 and PCN-333(Fe) acted as optical antennas and ORR/OER catalytic sites, respectively. The CsPbBr3@PCN-333(Fe) photocathode showed lower overpotential and better cycling stability compared to CsPbBr3 nanocrystals or PCN-333(Fe), highlighting the synergy between CsPbBr3 and PCN-333(Fe) in the composite.

Functionalization of Zirconium-Based Metal-Organic Layers with Tailored Pore Environments for Heterogeneous Catalysis.

Intriguing properties and functions are expected to implant into metal-organic layers (MOLs) to achieve tailored pore environments and multiple functionalities owing to the synergies among multiple components. Herein, we demonstrate a facile one-pot synthetic strategy to incorporate multiple functionalities into stable zirconium MOLs via secondary ligand pillaring. Through the combination of Zr6 -BTB (BTB=benzene-1,3,5-tribenzoate) layers and diverse secondary ligands (including ditopic and tetratopic linkers), 31 MOFs with multi-functionalities were systematically prepared. Notably, a metal-phthalocyanine fragment was successfully incorporated into this Zr-MOL system, giving rise to an ideal platform for the selective oxidation of anthracene. The organic functionalization of two-dimensional MOLs can generate tunable porous structures and environments, which may facilitate the excellent catalytic performance of as-synthesized materials.

Rh(III)-Catalyzed C-H Activation of Boronic Acid with Aryl Azide.

A Rh(III)-catalyzed C-H activation of boronic acid with aryl azide to obtain unsymmetric carbazoles, 1 H-indoles, or indolines has been developed. The reaction constructs dual distinct C-N bonds via sp2/sp3 C-H activation and rhodium nitrene insertion. Synthetically, this approach represents an access to widely used carbazole derivatives. The practical application to CBP and unsymmetric TCTA derivatives has also been performed. Mechanistic experiments and DFT calculations demonstrate that a five-membered rhodacycle species is the key intermediate.

Tandem Coupling of Azide with Isonitrile and Boronic Acid: Facile Access to Functionalized Amidines.

Amidine is a notable nitrogen-containing structural motif found in bioactive natural products and pharmaceuticals. Herein, a novel rhodium(I)-catalyzed tandem reaction of readily accessible azides with isonitriles and boronic acids via a carbodiimide intermediate is achieved. This protocol offers an alternative approach toward N-sulfonyl-, N-acyl-, and N- phosphoryl-functionalized, as well as general N-aryl and N-alkyl amidines with broad substrate scope. In addition, functionalized guanidines can also been synthesized when amines are used instead. The accomplishment of estrone-derived amidine and glibenclamide bioisosteres further reveals the practical utility of this strategy.

Phosphine-catalyzed highly enantioselective [3 + 3] cycloaddition of Morita-Baylis-Hillman carbonates with C,N-cyclic azomethine imines.

The first phosphine-catalyzed highly enantioselective [3 + 3] cycloaddition of Morita-Baylis-Hillman carbonates with C,N-cyclic azomethine imines is described. Using a spirocyclic chiral phosphine as the catalyst, a novel class of pharmaceutically interesting 4,6,7,11b-tetrahydro-1H-pyridazino[6,1-a]iso-quinoline derivatives were obtained in high yields with good to excellent diastereoselectivities and extremely excellent enantioselectivities (98->99% ee).