PCIM-DTA: Pairwise Conditional Interaction Modeling for Drug–Target Affinity Prediction under Cold-Start Scenarios
Clicks: 1
ID: 327134
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
0.0
/100
1 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
Ranked #845 of 850 articles by views in BMC Bioinformatics
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 850 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
MOTIVATION: Cold-start drug-target affinity prediction remains challenging because static interaction mechanisms cannot adapt to individual drug-target pairs. RESULTS: We propose PCIM-DTA, which constructs pair-level interaction representations and derives a pair-specific condition vector from global drug and target features. The condition vector modulates attention, pair-token features, distribution-aware recalibration, and regression parameters, while graph message passing captures higher-order dependencies. Experiments on Davis and BindingDB-Kd show that PCIM-DTA achieves competitive or superior performance under Warm, Cold-drug, Cold-target, Cold-both, and Scaffold-drug settings. Ablation studies support the contribution of each component. AVAILABILITY AND IMPLEMENTATION: The datasets used in this study are publicly available, including the Davis and BindingDB-Kd datasets. The implementation code of PCIM-DTA is publicly available at https://github.com/1322469934/PCIM-DTA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
| Reference Key |
openalex_W7204785900
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Z. Zhou, Min Chen, 周文剑, Qiong Xiao |
| Journal | BMC Bioinformatics |
| Year | 2026 |
| DOI |
10.1093/bioinformatics/btag646
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.