Activity Cliff Prediction: Dataset and Benchmark

Activity cliffs (ACs), which are generally defined as pairs of structurally similar molecules that are active against the same bio-target but significantly different in the binding potency, are of great importance to drug discovery. Up to date, the AC prediction problem, i.e., to predict whether a pair of molecules exhibit the AC relationship, has not yet been fully explored. In this paper, we first introduce ACNet, a large-scale dataset for AC prediction. ACNet curates over 400K Matched Molecular Pairs (MMPs) against 190 targets, including over 20K MMP-cliffs and 380K non-AC MMPs, and provides five subsets for model development and evaluation. Then, we propose a baseline framework to benchmark the predictive performance of molecular representations encoded by deep neural networks for AC prediction, and 16 models are evaluated in experiments. Our experimental results show that deep learning models can achieve good performance when the models are trained on tasks with adequate amount of data, while the imbalanced, low-data and out-of-distribution features of the ACNet dataset still make it challenging for deep neural networks to cope with. In addition, the traditional ECFP method shows a natural advantage on MMP-cliff prediction, and outperforms other deep learning models on most of the data subsets. To the best of our knowledge, our work constructs the first large-scale dataset for AC prediction, which may stimulate the study of AC prediction models and prompt further breakthroughs in AI-aided drug discovery. The codes and dataset can be accessed by https://drugai.github.io/ACNet/

Research on Safety Resilience Evaluation Model of Data Center Physical Infrastructure: An ANP-Based Approach

With the development of the digital economy, the number and scale of data centers are expanding rapidly. Data centers are playing an increasingly important role in social and economic development. However, a short downtime of a data center may result in huge losses. The safety management of data centers’ physical infrastructure is of great significance to address this concern. We applied resilience theory to the safety management of data center physical infrastructures. We analyzed the resilience connotation and evaluated the system resilience using the resilience indexes. The data center infrastructure was regarded as a system of systems. Through theoretical analysis, the resilience framework of data center infrastructures was established, which formed the main dimensions of resilience assessment. The Delphi method determined the resilience indices, and the ANP method was adopted to set up the evaluation model. The results revealed the important indexes affecting data center infrastructure system safety resilience. Based on the findings, this paper argues for improving redundancy and adaptability, paying attention to the resilience management of energy flow and thermal flow, and establishing an automatic systematic data management system. These suggested measures would not only effectively make contributions to the data center infrastructure safety management theory but also provide an important reference for construction industry practices

Assessment of VIIRS on the Identification of Harmful Algal Bloom Types in the Coasts of the East China Sea

Visible Infrared Imaging Radiometer Suite (VIIRS) data were systematically evaluated and used to detect harmful algal bloom (HAB) and classify algal bloom types in coasts of the East China Sea covered by optically complex and sediment-rich waters. First, the accuracy and spectral characteristics of VIIRS retrieved normalized water-leaving radiance or the equivalent remote sensing reflectance from September 2019 to October 2020 that were validated by the long-term observation data acquired from an offshore platform and underway measurements from a cruise in the Changjiang Estuary and adjacent East China Sea. These data were evaluated by comparing them with data from the Moderate-Resolution Imaging Spectroradiometer. The bands of 486, 551, and 671 nm provided much higher quality than those of 410 and 443 nm and were more suitable for HAB detection. Secondly, the performance of four HAB detection algorithms were compared. The Ratio of Algal Bloom (RAB) algorithm is probably more suitable for HAB detection in the study area. Importantly, although RAB was also verified to be applicable for the detection of different kinds of HAB (Prorocentrum donghaiense, diatoms, Ceratium furca, and Akashiwo sanguinea), the capability of VIIRS in the classification of those algal species was limited by the lack of the critical band near 531 nm