{"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/bapulm-binding-affinity-prediction-using","title":"BAPULM: Binding Affinity Prediction using Language Models","arxiv_id":"2411.04150","date":"2024-11-06","proceeding":null,"authors":["Radheesh Sharma Meda","Amir Barati Farimani"],"abstract":"Identifying drug-target interactions is essential for developing effective therapeutics. Binding affinity quantifies these interactions, and traditional approaches rely on computationally intensive 3D structural data. In contrast, language models can efficiently process sequential data, offering an alternative approach to molecular representation. In the current study, we introduce BAPULM, an innovative sequence-based framework that leverages the chemical latent representations of proteins via ProtT5-XL-U50 and ligands through MolFormer, eliminating reliance on complex 3D configurations. Our approach was validated extensively on benchmark datasets, achieving scoring power (R) values of 0.925 $\\pm$ 0.043, 0.914 $\\pm$ 0.004, and 0.8132 $\\pm$ 0.001 on benchmark1k2101, Test2016_290, and CSAR-HiQ_36, respectively. These findings indicate the robustness and accuracy of BAPULM across diverse datasets and underscore the potential of sequence-based models in-silico drug discovery, offering a scalable alternative to 3D-centric methods for screening potential ligands.","url_abs":"https://arxiv.org/abs/2411.04150v1","url_pdf":"https://arxiv.org/pdf/2411.04150v1.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":"bapulm-binding-affinity-prediction-using","repo_url":"https://github.com/radh55sh/BAPULM","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"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":"molecular-representation","task_name":"molecular representation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/protein-ligand-affinity-prediction-on-csar","task":"Protein-Ligand Affinity Prediction","dataset":"CSAR-HiQ","model":"BAPULM","rank_in_archive_order":1,"of":3,"metrics":{"RMSE":"1.328±0.02"},"uses_additional_data":false},{"leaderboard":"/sota/protein-ligand-affinity-prediction-on-pdbbind","task":"Protein-Ligand Affinity Prediction","dataset":"PDBbind","model":"BAPULM","rank_in_archive_order":1,"of":7,"metrics":{"RMSE":"0.898±0.0172"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}