{"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/deepaffinity-interpretable-deep-learning-of","title":"DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks","arxiv_id":"1806.07537","date":"2018-06-20","proceeding":null,"authors":["Mostafa Karimi","Di wu","Zhangyang Wang","Yang shen"],"abstract":"Motivation: Drug discovery demands rapid quantification of compound-protein\ninteraction (CPI). However, there is a lack of methods that can predict\ncompound-protein affinity from sequences alone with high applicability,\naccuracy, and interpretability.\n  Results: We present a seamless integration of domain knowledges and\nlearning-based approaches. Under novel representations of\nstructurally-annotated protein sequences, a semi-supervised deep learning model\nthat unifies recurrent and convolutional neural networks has been proposed to\nexploit both unlabeled and labeled data, for jointly encoding molecular\nrepresentations and predicting affinities. Our representations and models\noutperform conventional options in achieving relative error in IC$_{50}$ within\n5-fold for test cases and 20-fold for protein classes not included for\ntraining. Performances for new protein classes with few labeled data are\nfurther improved by transfer learning. Furthermore, separate and joint\nattention mechanisms are developed and embedded to our model to add to its\ninterpretability, as illustrated in case studies for predicting and explaining\nselective drug-target interactions. Lastly, alternative representations using\nprotein sequences or compound graphs and a unified RNN/GCNN-CNN model using\ngraph CNN (GCNN) are also explored to reveal algorithmic challenges ahead.\n  Availability: Data and source codes are available at\nhttps://github.com/Shen-Lab/DeepAffinity\n  Supplementary Information: Supplementary data are available at\nhttp://shen-lab.github.io/deep-affinity-bioinf18-supp-rev.pdf","url_abs":"http://arxiv.org/abs/1806.07537v2","url_pdf":"http://arxiv.org/pdf/1806.07537v2.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":"deepaffinity-interpretable-deep-learning-of","repo_url":"https://github.com/Shen-Lab/DeepAffinity","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"deepaffinity-interpretable-deep-learning-of","repo_url":"https://github.com/Yindong-Zhang/GraphConvolutionDrugTargetInteration","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"drug-discovery","task_name":"Drug Discovery"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/drug-discovery-on-bindingdb-ic50","task":"Drug Discovery","dataset":"BindingDB IC50","model":"DeepAffinity","rank_in_archive_order":2,"of":2,"metrics":{"Pearson Correlation":"0.84","RMSE":"0.78"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.07537","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.07537"}},"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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