{"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/plapt-protein-ligand-binding-affinity","title":"PLAPT: Protein-Ligand Binding Affinity Prediction Using Pretrained Transformers","arxiv_id":null,"date":"2024-02-08","proceeding":"bioRxiv 2024 2","authors":["Tyler Rose","Nicolo Monti","Navvye Anand","Tianyu Shen"],"abstract":"Understanding protein-ligand binding affinity is crucial for drug discovery, enabling the identification of promising drug candidates efficiently. We introduce PLAPT, a novel model leveraging transfer learning from pre-trained transformers like ProtBERT and ChemBERTa to predict binding affinities with high accuracy. Our method processes one-dimensional protein and ligand sequences, leveraging a branching neural network architecture for feature integration and affinity estimation. We demonstrate PLAPT's superior performance through validation on multiple datasets, achieving state-of-the-art results while requiring significantly less computational resources for training compared to existing models. Our findings indicate that PLAPT offers a highly effective and accessible approach for accelerating drug discovery efforts.","url_abs":"https://www.biorxiv.org/content/10.1101/2024.02.08.575577v1","url_pdf":"https://www.biorxiv.org/content/10.1101/2024.02.08.575577v1.full.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":"plapt-protein-ligand-binding-affinity","repo_url":"https://github.com/trrt-good/WELP-PLAPT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"drug-discovery","task_name":"Drug Discovery"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"protein-ligand-affinity-prediction","task_name":"Protein-Ligand Affinity Prediction"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/protein-ligand-affinity-prediction-on-csar","task":"Protein-Ligand Affinity Prediction","dataset":"CSAR-HiQ","model":"PLAPT","rank_in_archive_order":2,"of":3,"metrics":{"RMSE":"1.349"},"uses_additional_data":false},{"leaderboard":"/sota/protein-ligand-affinity-prediction-on-pdbbind","task":"Protein-Ligand Affinity Prediction","dataset":"PDBbind","model":"PLAPT","rank_in_archive_order":2,"of":7,"metrics":{"RMSE":"1.211"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}