With the advancements in Artificial Intelligence (AI) technology, Large Language Models (LLMs) provide outstanding capabilities for natural language understanding and generation, enhancing various domains. In psychiatry, LLMs can empower healthcare by analyzing vast amounts of medical data to improve diagnostic accuracy, enhance therapeutic communication, and personalize patient care with their strength in understanding and generating human-like text. In clinical AI, developing and utilizing robust and interpretable models has been a longstanding challenge. This survey investigates the current psychiatric practice of LLMs, along with a series of corpus resources that could be used for training psychiatric LLMs. We discuss the limitations concerning LLM reproducibility, capabilities, usability, interpretability in clinical settings, and ethical considerations. Additionally, we propose potential future directions for research, clinical application, and education in psychiatric LLMs. Finally, we discuss the challenge of integrating LLMs into the evolving landscape of healthcare in real-world scenarios.

Drug-Target Interactions (DTIs) identification is a crucial phase in drug development to screen potential drugs and targets and improve the efficiency of drug development. Accompanied by advances in Artificial Intelligence (AI) technology, Deep Learning (DL) methods have been introduced to the field and designed for DTI prediction, which can expedite the Research and Development (R&D) cycle. However, with the diverse DTIs involving various types of drugs and target protein molecules, it is still challenging to reveal the rules behind those interactions and further make reliable suggestions for the pharmaceutical industry. Currently, AI technology is expected to promote the ability to characterize the molecules against limited knowledge about DTIs. In this study, we accordingly propose a novel DTI prediction method named TopoPharmDTI, dedicated to better characterizing both the drug and target molecules, where an innovative dual-tier drug features fusion strategy is designed for drug molecules and a most adaptive Large Language Model (LLM) for target protein sequence representation is chosen under the evaluation of six top models by far. Our method achieves considerable prediction performance and a promising capability to identify the binding domains on the target protein. The method is freely available at http://ex.nenucompbio.com/TopoPharmDTI.html.