{"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/automatic-annotation-augmentation-boosts","title":"Automatic Annotation Augmentation Boosts Translation between Molecules and Natural Language","arxiv_id":"2502.06634","date":"2025-02-10","proceeding":null,"authors":["Zhiqiang Zhong","Simon Sataa-Yu Larsen","Haoyu Guo","Tao Tang","Kuangyu Zhou","Davide Mottin"],"abstract":"Recent advancements in AI for biological research focus on integrating molecular data with natural language to accelerate drug discovery. However, the scarcity of high-quality annotations limits progress in this area. This paper introduces LA$^3$, a Language-based Automatic Annotation Augmentation framework that leverages large language models to augment existing datasets, thereby improving AI training. We demonstrate the effectiveness of LA$^3$ by creating an enhanced dataset, LaChEBI-20, where we systematically rewrite the annotations of molecules from an established dataset. These rewritten annotations preserve essential molecular information while providing more varied sentence structures and vocabulary. Using LaChEBI-20, we train LaMolT5 based on a benchmark architecture to learn the mapping between molecular representations and augmented annotations. Experimental results on text-based *de novo* molecule generation and molecule captioning demonstrate that LaMolT5 outperforms state-of-the-art models. Notably, incorporating LA$^3$ leads to improvements of up to 301% over the benchmark architecture. Furthermore, we validate the effectiveness of LA$^3$ notable applications in *image*, *text* and *graph* tasks, affirming its versatility and utility.","url_abs":"https://arxiv.org/abs/2502.06634v1","url_pdf":"https://arxiv.org/pdf/2502.06634v1.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":"automatic-annotation-augmentation-boosts","repo_url":"https://github.com/zhiqiangzhongddu/la3","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"drug-discovery","task_name":"Drug Discovery"},{"task_slug":"molecule-captioning","task_name":"Molecule Captioning"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"text-based-de-novo-molecule-generation","task_name":"Text-based de novo Molecule Generation"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/molecule-captioning-on-chebi-20","task":"Molecule Captioning","dataset":"ChEBI-20","model":"LaMolT5-Large","rank_in_archive_order":12,"of":33,"metrics":{"BLEU-2":"60.2","BLEU-4":"52.1","METEOR":"63.4","ROUGE-1":"65.5","ROUGE-2":"51.2","ROUGE-L":"59.8","Text2Mol":"59.7"},"uses_additional_data":false},{"leaderboard":"/sota/molecule-captioning-on-chebi-20","task":"Molecule Captioning","dataset":"ChEBI-20","model":"LaMolT5-Base","rank_in_archive_order":20,"of":33,"metrics":{"BLEU-2":"57.4","BLEU-4":"48.5","METEOR":"59.6","ROUGE-1":"63.4","ROUGE-2":"47.8","ROUGE-L":"56.4","Text2Mol":"59.9"},"uses_additional_data":false},{"leaderboard":"/sota/molecule-captioning-on-chebi-20","task":"Molecule Captioning","dataset":"ChEBI-20","model":"LaMolT5-Small","rank_in_archive_order":29,"of":33,"metrics":{"BLEU-2":"53.9","BLEU-4":"44.6","METEOR":"56.6","ROUGE-1":"62.0","ROUGE-2":"46.9","ROUGE-L":"56.3","Text2Mol":"58.8"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}