TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments(BIOINFORMATICS 2020) https://doi.org/10.1093/bioinformatics/btaa524
[EMNLP 2025]* FusionDTI utilises a Token-level Fusion module to effectively learn fine-grained information for Drug-Target Interaction Prediction.
Code for "T Cell Receptor Specificity Prediction with Bimodal Attention Networks" (https://doi.org/10.1093/bioinformatics/btab294, ISMB 2021)
Code for AAAI 2024 paper "PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction"
Mitigating Activity Cliff-induced Discrepancies in Deep Learning of Compound-Protein Interaction
Deep Belief Network for Compound-Protein Interaction Prediction.
Mnemosyne is a multimodal, deep learning-based model designed for predicting potential drug candidates for Alzheimer's disease.
Research implementation and experimental study of PerceiverCPI for compound-protein interaction prediction.
To associate your repository with the compound-protein-interaction topic, visit your repo's landing page and select "manage topics."