{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/chemberta-2-fine-tuning-for-molecules-hiv","title":"ChemBERTa-2: Fine-Tuning for Molecule’s HIV Replication Inhibition Prediction","arxiv_id":null,"date":"2023-09-15","proceeding":"ChemRxiv 2023 9","authors":["Sylwia Nowakowska"],"abstract":"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.","url_abs":"https://chemrxiv.org/engage/chemrxiv/article-details/65030b55b338ec988a780108","url_pdf":"https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/65030b55b338ec988a780108/original/chem-ber-ta-2-fine-tuning-for-molecule-s-hiv-replication-inhibition-prediction.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"chemberta-2-fine-tuning-for-molecules-hiv","repo_url":"https://github.com/SylwiaNowakowska/LLM_Fine_Tuning_Molecular_Properties","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"molecular-property-prediction","task_name":"Molecular Property Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/molecular-property-prediction-on-hiv-dataset","task":"Molecular Property Prediction","dataset":"HIV dataset","model":"ChemBERTa-2 Fine-tuned","rank_in_archive_order":4,"of":11,"metrics":{"AUC":"0.793"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}