ChemBERTaPolyPharm: Modeling polypharmacy side effects with ChemBERTa and PubMed Encoders
Gromova, A. A.; Maida, A. S.
Show abstract
Polypharmacy side effects occur when drug combinations trigger unexpected interactions, altering therapeutic outcomes. During which the activity of one drug may change favorably or unfavorably if taken with the other drug. Drug interactions are rare and are only observed in clinical studies. Thus, the discovery and detection of polypharmacy side effects remains a challenge. Our approach achieves an impressive average F1 score of 0.93, demonstrating its efficacy in capturing drug interaction patterns. 1 Submission of papers to NeurIPS 2025Please read the instructions below carefully and follow them faithfully.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Interpretable and Generalizable Attention-Based Model for Predicting Drug-Target Interaction Using 3D Structure of Protein Binding Sites: SARS-CoV-2 Case Study and in-Lab Validation 97%
- A Survey and Systematic Assessment of Computational Methods for Drug Response Prediction 97%
- GexMolGen: Cross-modal Generation of Hit-like Molecules via Large Language Model Encoding of Gene Expression Signatures 96%
Similar papers in this journal
- Network-based estimation of therapeutic efficacy and adverse reaction potential for prioritisation of anti-cancer drug combinations 95%
- TCMM: A Unified Database for Traditional Chinese Medicine Modernization and Therapeutic Innovations 95%
- Artemis: Harnessing Knowledge Graphs for Next-Generation Drug Target Prioritization 94%
Similar papers in this journal
- A Graph-Attention-Based Deep Learning Network for Predicting Biotech-Small-Molecule Drug Interactions 98%
- Mining drug-target interactions from biomedical literature using chemical and gene descriptions-based ensemble transformer model. 97%
- FLONE: fully Lorentz network embedding for inferring novel drug targets 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.