Papers › ChemBERTa-2: Fine-Tuning for Molecule’s HIV Replication Inhibition Prediction
ChemBERTa-2: Fine-Tuning for Molecule’s HIV Replication Inhibition Prediction
Sylwia Nowakowska
Two versions of Large Language ChemBERTa-2 models, pre-trained with two different methods, were fine-tuned in this work for HIV replication inhibition prediction. The best model achieved AUROC of 0.793. The changes in distributions of molecular embeddings prior to and following fine-tuning reveal models’ enhanced ability to differentiate between active and inactive HIV molecules.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Molecular Property Prediction | HIV dataset | ChemBERTa-2 Fine-tuned | AUC | 0.793 | #4 of 11 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections