Exploring drug–target interactions by biomedical experiments requires a lot of human, financial, and material resources. To save time and cost to meet the needs of the present generation, machine learning methods have been introduced into the prediction of drug–target interactions. The large amount of available drug and target data in existing databases, the evolving and innovative computer technologies, and the inherent characteristics of various types of machine learning have made machine learning techniques the mainstream method for drug–target interaction prediction research. In this review, details of the specific applications of machine learning in drug–target interaction prediction are summarized, the characteristics of each algorithm are analyzed, and the issues that need to be further addressed and explored for future research are discussed. The aim of this review is to provide a sound basis for the construction of high-performance models.

In this work, a promising dual-gated thin film transistor (TFT) structure has been proposed and introduced in the shift register (SR)-integrated circuits to reduce the rising time. The threshold voltage can be simultaneously changed by the top gate and the bottom gate in the proposed dual-gated TFTs. When the SR circuits start to export the scan signals in the displays, the driving currents in the SR circuits are increased by switching the working station of driving TFTs from the enhancement characterization to the depletion characterization. Subsequently, the detailed smart spice simulation has been used to study the function of the proposed SR circuits. In the next step, the proposed SR circuits have been fabricated in a G4.5 active-matrix organic light-emitting diode manufacture factory. The simulated and experimental results indicate that the shift register pulses with the full swing amplitude can be obtained in the SR circuits. Moreover, in contrast to the conventional SR circuits employing with the single-gated TFTs, it has been found that the rising time of the output signals can be reduced from 3.75 μs to 1.23 μs in the proposed SR circuits with the dual-gated TFTs, thus exhibiting the significant improvement of the driving force in the proposed SR circuits. Finally, we demonstrated a 31-inch 4K AMOLED display with the proposed SR circuits.