Precise drug target discovery is pivotal to mitigating the escalating costs and high attrition rates that characterize pharmaceutical research and development. Given that traditional single-omics methods often fail to elucidate the systemic complexity of human diseases, deep learning (DL)-enabled multi-omics integration has emerged as a transformative frontier. This review systematically summarizes the advancements in DL-driven multi-omics integration for drug target discovery. First, the multi-omics data foundation and integration strategies are delineated, followed by an exploration of the DL architectures utilized for processing such data. Subsequently, the efficacy of DL-driven multi-omics integration is examined regarding the identification of novel disease drivers, prediction of synthetic lethality interactions, and prioritization of therapeutic targets. Finally, addressing persistent challenges related to data sparsity, model interpretability, and target druggability and validation hurdles, emerging opportunities driven by Generative AI, Large Multimodal Models (LMMs), Explainable AI (XAI), and multidimensional feasibility assessment frameworks are discussed in the context of advancing precision medicine.

Graphene can support surface plasmon polaritons (SPPs) in the terahertz band, and graphene SPP sensors are widely used in the field of terahertz micro- and nano-optical devices. In this paper, we propose an H-shaped graphene metasurface and investigate the plasmon-induced transparency (PIT) phenomenon in the proposed structure using the finite-difference time-domain (FDTD) method. Our results show that the Fermi energy levels, as well as certain shape parameters, can effectively modulate the PIT phenomenon in the proposed structure. Interestingly, changing some of these shape parameters can excite two dips into three. In terms of sensing performance, the maximum values of sensitivity and figure of merit (FOM) are 1.4028 THz/RIU and 17.97, respectively. These results offer valuable guidance for the use of terahertz optical graphene SPP sensors.

First-principles calculations based on density functional theory are employed to investigate the impact of vacancy defects on the optoelectronic properties of Bi₄O₅Br₂. The results indicate that vacancy defects induce minimal lattice distortion in Bi₄O₅Br₂ without compromising its structural stability. Oxygen or bromine vacancies are more likely to occur than bismuth vacancies. The introduction of a bismuth vacancy leads to n-type semiconductor behavior in the Bi₄O₅Br₂ system, while the creation of an oxygen vacancy reduces the bandgap and enhances the light absorption capacity. Bi₄O₅Br₂ with three coordinated oxygen vacancies exhibits a higher effective electron–hole pair mass ratio, which is advantageous for the efficient separation of electron–hole pairs. Bi₄O₅Br₂ with three coordinated oxygen vacancies exhibits enhanced absorption and reflection coefficients in the visible-light region compared to other systems, indicating that oxygen vacancy defects significantly promote visible-light absorption and electron–hole separation. This research provides new theoretical insights for understanding and optimizing the performance of photocatalysts based on Bi₄O₅Br₂.

For exploring the electron emission’s mechanism of nano-diamond under different temperature and vacuum degree, determine its limit working parameters, the nano-diamond coating’s field emission cathode was experimentally studied. The results show that the temperature rise has an impact on the field emission of titanium-based nano-diamonds. When the temperature exceeds 120 °C, the titanium matrix will emit a large number of electrons to form a larger emission current and drown the field emission current. The degree of vacuum will decrease as the temperature increases. This achievement provides a certain foundation for the design and manufacture of nano-diamond field emission displays.

The two-probe device of nanorod-coupled gold electrodes is constructed based on the triangular zinc oxide (ZnO) nanorod. The length-dependent electronic transport properties of the ZnO nanorod was studied by density functional theory (DFT) with the non-equilibrium Green’s function (NEGF). Our results show that the current of devices decreases with increasing length of the ZnO nanorod at the same bias voltage. Metal-like behavior for the short nanorod was observed under small bias voltage due to the interface state between gold and the ZnO nanorod. However, the influence of the interface on the device was negligible under the condition that the length of the ZnO nanorod increases. Moreover, the rectification behavior was observed for the longer ZnO nanorod, which was analyzed from the transmission spectra and molecular-projected self-consistent Hamiltonian (MPSH) states. Our results indicate that the ZnO nanorod would have potential applications in electronic-integrated devices.