{"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/atomic-convolutional-networks-for-predicting","title":"Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity","arxiv_id":"1703.10603","date":"2017-03-30","proceeding":null,"authors":["Joseph Gomes","Bharath Ramsundar","Evan N. Feinberg","Vijay S. Pande"],"abstract":"Empirical scoring functions based on either molecular force fields or\ncheminformatics descriptors are widely used, in conjunction with molecular\ndocking, during the early stages of drug discovery to predict potency and\nbinding affinity of a drug-like molecule to a given target. These models\nrequire expert-level knowledge of physical chemistry and biology to be encoded\nas hand-tuned parameters or features rather than allowing the underlying model\nto select features in a data-driven procedure. Here, we develop a general\n3-dimensional spatial convolution operation for learning atomic-level chemical\ninteractions directly from atomic coordinates and demonstrate its application\nto structure-based bioactivity prediction. The atomic convolutional neural\nnetwork is trained to predict the experimentally determined binding affinity of\na protein-ligand complex by direct calculation of the energy associated with\nthe complex, protein, and ligand given the crystal structure of the binding\npose. Non-covalent interactions present in the complex that are absent in the\nprotein-ligand sub-structures are identified and the model learns the\ninteraction strength associated with these features. We test our model by\npredicting the binding free energy of a subset of protein-ligand complexes\nfound in the PDBBind dataset and compare with state-of-the-art cheminformatics\nand machine learning-based approaches. We find that all methods achieve\nexperimental accuracy and that atomic convolutional networks either outperform\nor perform competitively with the cheminformatics based methods. Unlike all\nprevious protein-ligand prediction systems, atomic convolutional networks are\nend-to-end and fully-differentiable. They represent a new data-driven,\nphysics-based deep learning model paradigm that offers a strong foundation for\nfuture improvements in structure-based bioactivity prediction.","url_abs":"http://arxiv.org/abs/1703.10603v1","url_pdf":"http://arxiv.org/pdf/1703.10603v1.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":"atomic-convolutional-networks-for-predicting","repo_url":"https://github.com/deepchem/deepchem","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"atomic-convolutional-networks-for-predicting","repo_url":"https://github.com/GilbertoQ/Bioinformatics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"atomic-convolutional-networks-for-predicting","repo_url":"https://github.com/deepchem/moleculenet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"drug-discovery","task_name":"Drug Discovery"},{"task_slug":"molecular-docking","task_name":"Molecular Docking"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1703.10603","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.10603"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/deepchem/moleculenet","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/GilbertoQ/Bioinformatics","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/deepchem/deepchem","reach":null}],"summary":{"ran_honours":2},"by_repo_kind":{"listed":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":2,"samples":[{"code_sha256_prefix":"3d2f44385efd80c4","entry":"batch_validation","repo":"GilbertoQ/Bioinformatics","repo_kind":"listed","path":"Atomnet_network/neural_network.py","file_url":"https://github.com/GilbertoQ/Bioinformatics/blob/HEAD/Atomnet_network/neural_network.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"3d2f44385efd80c4"}},{"code_sha256_prefix":"c4e1a06ab96e1598","entry":"generator_validation","repo":"GilbertoQ/Bioinformatics","repo_kind":"listed","path":"Atomnet_network/neural_network.py","file_url":"https://github.com/GilbertoQ/Bioinformatics/blob/HEAD/Atomnet_network/neural_network.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"c4e1a06ab96e1598"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}