ACGCN: Graph Convolutional Networks for Activity Cliff Prediction between Matched Molecular Pairs

Junhui Park, Gaeun Sung, Seunghyun Lee, Seungho Kang, Chunkyun Park

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


One of the interesting issues in drug-target interaction studies is the activity cliff (AC), which is usually defined as structurally similar compounds with large differences in activity toward a common target. The AC is of great interest in medicinal chemistry as it may provide clues to understanding the complex properties of the target proteins, paving the way for practical applications aimed at the discovery of more potent drugs. In this paper, we propose graph convolutional networks for the prediction of AC and designate the proposed models as Activity Cliff prediction using Graph Convolutional Networks (ACGCNs). The results show that ACGCNs outperform several off-the-shelf methods when predicting ACs of three popular target data sets for thrombin, Mu opioid receptor, and melanocortin receptor. Finally, we utilize gradient-weighted class activation mapping to visualize activation weights at nodes in the molecular graphs, demonstrating its potential to contribute to the ability to identify important substructures for molecular docking.

Original languageEnglish
JournalJournal of Chemical Information and Modeling
Publication statusAccepted/In press - 2022

Bibliographical note

Funding Information:
S.K. and J.P. were supported by the National Research Foundation (NRF), Korea, under project BK21 FOUR (2021-11-0077). S.K. was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2020R1F1A1A01048240).

Publisher Copyright:
© 2022 American Chemical Society.

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Chemical Engineering(all)
  • Computer Science Applications
  • Library and Information Sciences


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