Yi Zhang

Bio: Yi Zhang is an academic researcher from University of Minnesota. The author has contributed to research in topics: Autoencoder & Data center. The author has co-authored 1 publications.

An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design.

Abstract: As a promising tool to navigate in the vast chemical space, artificial intelligence (AI) is leveraged for drug design. From the year 2017 to 2021, the number of applications of several recent AI models (i.e. graph neural network (GNN), recurrent neural network (RNN), variation autoencoder (VAE), generative adversarial network (GAN), flow and reinforcement learning (RL)) in drug design increases significantly. Many relevant literature reviews exist. However, none of them provides an in-depth summary of many applications of the recent AI models in drug design. To complement the existing literature, this survey includes the theoretical development of the previously mentioned AI models and detailed summaries of 42 recent applications of AI in drug design. Concretely, 13 of them leverage GNN for molecular property prediction and 29 of them use RL and/or deep generative models for molecule generation and optimization. In most cases, the focus of the summary is the models, their variants, and modifications for specific tasks in drug design. Moreover, 60 additional applications of AI in molecule generation and optimization are briefly summarized in a table. Finally, this survey provides a holistic discussion of the abundant applications so that the tasks, potential solutions, and challenges in AI-based drug design become evident.

Application status and development of artificial intelligence technology in large data center

Abstract: The original data center management operation and maintenance is usually assisted by the data center environmental monitoring system. All along, the environmental monitoring system only collects and displays the running data of the testing equipment and does not participate in the equipment configuration and automatic control. Artificial Intelligence (AI), as a tool to explore and find the best management and operation and maintenance strategy, will make the intricate linkage and coordination of mechanical and electrical equipment in data centers easier to realize. This paper reveals the inevitability of the birth of AI data center and proposes a large data center security transmission model based on AI under the background of digital transformation, which reduces the resource consumption of protection routing and improves the survivability of data center network. The simulation results show that the number and link length of protection routes established by this algorithm are less. On the basis of ensuring that the data center is not affected by the failed nodes and the data is transmitted normally and effectively, the resource consumption of protection routes is reduced, and the survivability of the data center network is improved.