{"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/ligand-pose-optimization-with-atomic-grid","title":"Ligand Pose Optimization with Atomic Grid-Based Convolutional Neural Networks","arxiv_id":"1710.07400","date":"2017-10-20","proceeding":null,"authors":["Matthew Ragoza","Lillian Turner","David Ryan Koes"],"abstract":"Docking is an important tool in computational drug discovery that aims to\npredict the binding pose of a ligand to a target protein through a combination\nof pose scoring and optimization. A scoring function that is differentiable\nwith respect to atom positions can be used for both scoring and gradient-based\noptimization of poses for docking. Using a differentiable grid-based atomic\nrepresentation as input, we demonstrate that a scoring function learned by\ntraining a convolutional neural network (CNN) to identify binding poses can\nalso be applied to pose optimization. We also show that an iteratively-trained\nCNN that includes poses optimized by the first CNN in its training set performs\neven better at optimizing randomly initialized poses than either the first CNN\nscoring function or AutoDock Vina.","url_abs":"http://arxiv.org/abs/1710.07400v1","url_pdf":"http://arxiv.org/pdf/1710.07400v1.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":"ligand-pose-optimization-with-atomic-grid","repo_url":"https://github.com/gnina/gnina","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"drug-discovery","task_name":"Drug Discovery"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}