BindingSiteDTI: differential-scale binding site modelling for drug–target interaction prediction

Feng Pan, Chong Yin, Si Qi Liu, Tao Huang, Zhaoxiang Bian, Pong Chi Yuen*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review


Motivation: Enhanced by contemporary computational advances, the prediction of drug–target interactions (DTIs) has become crucial in developing de novo and effective drugs. Existing deep learning approaches to DTI prediction are frequently beleaguered by a tendency to overfit specific molecular representations, which significantly impedes their predictive reliability and utility in novel drug discovery contexts. Furthermore, existing DTI networks often disregard the molecular size variance between macro molecules (targets) and micro molecules (drugs) by treating them at an equivalent scale that undermines the accurate elucidation of their interaction.

Results: We propose a novel DTI network with a differential-scale scheme to model the binding site for enhancing DTI prediction, which is named as BindingSiteDTI. It explicitly extracts multiscale substructures from targets with different scales of molecular size and fixed-scale substructures from drugs, facilitating the identification of structurally similar substructural tokens, and models the concealed relationships at the substructural level to construct interaction feature. Experiments conducted on popular benchmarks, including DUD-E, human, and BindingDB, shown that BindingSiteDTI contains significant improvements compared with recent DTI prediction methods.

Original languageEnglish
Article numberbtae308
Number of pages8
Issue number5
Publication statusPublished - May 2024

Scopus Subject Areas

  • Computational Mathematics
  • Molecular Biology
  • Biochemistry
  • Statistics and Probability
  • Computer Science Applications
  • Computational Theory and Mathematics


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