{"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/development-and-evaluation-of-a-deep-learning","title":"Development and evaluation of a deep learning model for protein-ligand binding affinity prediction","arxiv_id":"1712.07042","date":"2017-12-19","proceeding":null,"authors":["Marta M. Stepniewska-Dziubinska","Piotr Zielenkiewicz","Pawel Siedlecki"],"abstract":"Structure based ligand discovery is one of the most successful approaches for\naugmenting the drug discovery process. Currently, there is a notable shift\ntowards machine learning (ML) methodologies to aid such procedures. Deep\nlearning has recently gained considerable attention as it allows the model to\n\"learn\" to extract features that are relevant for the task at hand. We have\ndeveloped a novel deep neural network estimating the binding affinity of\nligand-receptor complexes. The complex is represented with a 3D grid, and the\nmodel utilizes a 3D convolution to produce a feature map of this\nrepresentation, treating the atoms of both proteins and ligands in the same\nmanner. Our network was tested on the CASF \"scoring power\" benchmark and Astex\nDiverse Set and outperformed classical scoring functions. The model, together\nwith usage instructions and examples, is available as a git repository at\nhttp://gitlab.com/cheminfIBB/pafnucy","url_abs":"http://arxiv.org/abs/1712.07042v2","url_pdf":"http://arxiv.org/pdf/1712.07042v2.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":"development-and-evaluation-of-a-deep-learning","repo_url":"https://gitlab.com/cheminfIBB/pafnucy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"development-and-evaluation-of-a-deep-learning","repo_url":"https://github.com/2023-MindSpore-1/ms-code-218/tree/main/pafnucy","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"development-and-evaluation-of-a-deep-learning","repo_url":"https://github.com/code-implementation1/Code9/tree/main/pafnucy","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"development-and-evaluation-of-a-deep-learning","repo_url":"https://github.com/code-implementation1/Code9/tree/main/pfnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"drug-discovery","task_name":"Drug Discovery"}],"methods":[{"method_slug":"3d-convolution","method_name":"3D Convolution"},{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.07042","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}