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J Chem Inf Model 2021 Apr 12. Epub 2021 Apr 12.

Institute of Chemical Materials, China Academy of Engineering Physics (CAEP), P.O. Box 919-311, Mianyang 621999, Sichuan, China.

Newly developed high-throughput methods for property predictions make the process of materials design faster and more efficient. Density is an important physical property for energetic compounds to assess detonation velocity and detonation pressure, but the time cost of recent density prediction models is still high owing to the time-consuming processes to calculate molecular descriptors. To improve the screening efficiency of potential energetic compounds, new methods for density prediction with more accuracy and less time cost are urgently needed, and a possible solution is to establish direct mappings between the molecular structure and density. Read More

IEEE Trans Neural Netw Learn Syst 2021 Apr 7;PP. Epub 2021 Apr 7.

Learning meaningful representations of free-hand sketches remains a challenging task given the signal sparsity and the high-level abstraction of sketches. Existing techniques have focused on exploiting either the static nature of sketches with convolutional neural networks (CNNs) or the temporal sequential property with recurrent neural networks (RNNs). In this work, we propose a new representation of sketches as multiple sparsely connected graphs. Read More

Brief Bioinform 2021 Apr 5. Epub 2021 Apr 5.

College of Intelligence and Computing, Tianjin University, Tianjin 300350, China.

Noncoding RNAs (ncRNAs) play crucial roles in many biological processes. Experimental methods for identifying ncRNA-protein interactions (NPIs) are always costly and time-consuming. Many computational approaches have been developed as alternative ways. Read More