{"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/integrating-convolutional-layers-and-biformer","title":"Integrating convolutional layers and biformer network with forward-forward and backpropagation training","arxiv_id":null,"date":"2025-02-28","proceeding":"Scientific Reports 2025 2","authors":["Ali Kianfar","Parvin Razzaghi","Zahra Asgari"],"abstract":"Accurate molecular property prediction is crucial for drug discovery and computational chemistry, facilitating the identification of promising compounds and accelerating therapeutic development. Traditional machine learning falters with high-dimensional data and manual feature engineering, while existing deep learning approaches may not capture complex molecular structures, leaving a research gap. We introduce Deep-CBN, a novel framework designed to enhance molecular property prediction by capturing intricate molecular representations directly from raw data, thus improving accuracy and efficiency. Our methodology combines convolutional neural networks (CNNs) with a BiFormer attention mechanism, employing both the forward-forward algorithm and backpropagation. The model operates in three stages: (1) feature learning, extracting local features from SMILES strings using CNNs; (2) attention refinement, capturing global context with a BiFormer module enhanced by the forward-forward algorithm; and (3) prediction subnetwork tuning, fine-tuning via backpropagation. Evaluations on benchmark datasets—including Tox21, BBBP, SIDER, ClinTox, BACE, HIV, and MUV—show that Deep-CBN achieves near-perfect ROC-AUC scores, significantly outperforming state-of-the-art methods. These findings demonstrate its effectiveness in capturing complex molecular patterns, offering a robust tool to accelerate drug discovery processes.","url_abs":"https://www.nature.com/articles/s41598-025-92218-y#Abs1","url_pdf":"https://www.nature.com/articles/s41598-025-92218-y.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":"integrating-convolutional-layers-and-biformer","repo_url":"https://github.com/akianfar/Deep-CBN","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"computational-chemistry","task_name":"Computational chemistry"},{"task_slug":"drug-discovery","task_name":"Drug Discovery"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"molecular-property-prediction","task_name":"Molecular Property Prediction"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"property-prediction","task_name":"Property Prediction"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/molecular-property-prediction-on-bace-1","task":"Molecular Property Prediction","dataset":"BACE","model":"Deep-CBN","rank_in_archive_order":7,"of":20,"metrics":{"ROC-AUC":"83.6"},"uses_additional_data":false},{"leaderboard":"/sota/molecular-property-prediction-on-bbbp-1","task":"Molecular Property Prediction","dataset":"BBBP","model":"Deep-CBN","rank_in_archive_order":12,"of":29,"metrics":{"ROC-AUC":"75.8"},"uses_additional_data":false},{"leaderboard":"/sota/molecular-property-prediction-on-hiv-1","task":"Molecular Property Prediction","dataset":"HIV","model":"Deep-CBN","rank_in_archive_order":1,"of":4,"metrics":{"ROC-AUC":"97.3"},"uses_additional_data":false},{"leaderboard":"/sota/molecular-property-prediction-on-muv-1","task":"Molecular Property Prediction","dataset":"MUV","model":"Deep-CBN","rank_in_archive_order":1,"of":5,"metrics":{"ROC-AUC":"99.8"},"uses_additional_data":false},{"leaderboard":"/sota/molecular-property-prediction-on-sider-1","task":"Molecular Property Prediction","dataset":"SIDER","model":"Deep-CBN","rank_in_archive_order":2,"of":19,"metrics":{"ROC-AUC":"78.2"},"uses_additional_data":false},{"leaderboard":"/sota/molecular-property-prediction-on-tox21-1","task":"Molecular Property Prediction","dataset":"Tox21","model":"Deep-CBN","rank_in_archive_order":1,"of":20,"metrics":{"ROC-AUC":"92.4"},"uses_additional_data":false},{"leaderboard":"/sota/molecular-property-prediction-on-clintox-1","task":"Molecular Property Prediction","dataset":"clintox","model":"Deep-CBN","rank_in_archive_order":1,"of":20,"metrics":{"ROC-AUC":"99.2"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}