DeepRSMA: a cross-fusion-based deep learning method for RNA-small molecule binding affinity prediction.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: RNA is implicated in numerous aberrant cellular functions and disease progressions, highlighting the crucial importance of RNA-targeted drugs. To accelerate the discovery of such drugs, it is essential to develop an effective computational method for predicting RNA-small molecule affinity (RSMA). Recently, deep learning-based computational methods have been promising due to their powerful nonlinear modeling ability. However, the leveraging of advanced deep learning methods to mine the diverse information of RNAs, small molecules, and their interaction still remains a great challenge.

Authors

  • Zhijian Huang
    School of Computer Science and Engineering, Central South University, 410075, Changsha, China.
  • Yucheng Wang
    Machine Intellection Department, Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore.
  • Song Chen
    Department of Nuclear Medicine, The First Hospital of China Medical University, Shenyang, China.
  • Yaw Sing Tan
    Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore 138671, Singapore.
  • Lei Deng
    1] Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China [2] Optical Memory National Engineering Research Center, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
  • Min Wu
    Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China.