TriFP-NGram: Integrating Three Complementary Fingerprint and N-Gram Features for Enhanced Drug-Target Affinity Prediction

Jiao Wang; Ge Kong; Juan Wang

ICASSP 2025accepted0 citations

TriFP-NGram: Integrating Three Complementary Fingerprint and N-Gram Features for Enhanced Drug-Target Affinity Prediction

Abstract

Accurate prediction of drug-target affinity plays a vital role in drug discovery and design. TriFP-NGram integrates multiple fingerprint and n-gram features to predict drug-target binding affinities. It surpasses current methods by leveraging a comprehensive set of molecular and protein features, enhancing predictive performance across six datasets. The innovative model architecture allows for deep feature extraction, including graph convolutional networks and selective receptive fields. Its reliable and efficient affinity predictions offer a significant advancement in drug discovery and design. The source code and datasets of TriFp-NGram are available at https://github.com/991212/TriFP-NGram.git

BibTeX

@inproceedings{icassp2025_trifpngramintegr,
  title = {TriFP-NGram: Integrating Three Complementary Fingerprint and N-Gram Features for Enhanced Drug-Target Affinity Prediction},
  author = {Jiao Wang and Ge Kong and Juan Wang},
  booktitle = {ICASSP 2025},
  year = {2025}
}