{"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/protein-ligand-scoring-with-convolutional","title":"Protein-Ligand Scoring with Convolutional Neural Networks","arxiv_id":"1612.02751","date":"2016-12-08","proceeding":null,"authors":["Matthew Ragoza","Joshua Hochuli","Elisa Idrobo","Jocelyn Sunseri","David Ryan Koes"],"abstract":"Computational approaches to drug discovery can reduce the time and cost\nassociated with experimental assays and enable the screening of novel\nchemotypes. Structure-based drug design methods rely on scoring functions to\nrank and predict binding affinities and poses. The ever-expanding amount of\nprotein-ligand binding and structural data enables the use of deep machine\nlearning techniques for protein-ligand scoring.\n  We describe convolutional neural network (CNN) scoring functions that take as\ninput a comprehensive 3D representation of a protein-ligand interaction. A CNN\nscoring function automatically learns the key features of protein-ligand\ninteractions that correlate with binding. We train and optimize our CNN scoring\nfunctions to discriminate between correct and incorrect binding poses and known\nbinders and non-binders. We find that our CNN scoring function outperforms the\nAutoDock Vina scoring function when ranking poses both for pose prediction and\nvirtual screening.","url_abs":"http://arxiv.org/abs/1612.02751v1","url_pdf":"http://arxiv.org/pdf/1612.02751v1.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":"protein-ligand-scoring-with-convolutional","repo_url":"https://github.com/gnina/gnina","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"protein-ligand-scoring-with-convolutional","repo_url":"https://github.com/GilbertoQ/Bioinformatics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"drug-design","task_name":"Drug Design"},{"task_slug":"drug-discovery","task_name":"Drug Discovery"},{"task_slug":"pose-prediction","task_name":"Pose Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1612.02751","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.02751"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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