{"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/atomnet-a-deep-convolutional-neural-network","title":"AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery","arxiv_id":"1510.02855","date":"2015-10-10","proceeding":null,"authors":["Izhar Wallach","Michael Dzamba","Abraham Heifets"],"abstract":"Deep convolutional neural networks comprise a subclass of deep neural\nnetworks (DNN) with a constrained architecture that leverages the spatial and\ntemporal structure of the domain they model. Convolutional networks achieve the\nbest predictive performance in areas such as speech and image recognition by\nhierarchically composing simple local features into complex models. Although\nDNNs have been used in drug discovery for QSAR and ligand-based bioactivity\npredictions, none of these models have benefited from this powerful\nconvolutional architecture. This paper introduces AtomNet, the first\nstructure-based, deep convolutional neural network designed to predict the\nbioactivity of small molecules for drug discovery applications. We demonstrate\nhow to apply the convolutional concepts of feature locality and hierarchical\ncomposition to the modeling of bioactivity and chemical interactions. In\nfurther contrast to existing DNN techniques, we show that AtomNet's application\nof local convolutional filters to structural target information successfully\npredicts new active molecules for targets with no previously known modulators.\nFinally, we show that AtomNet outperforms previous docking approaches on a\ndiverse set of benchmarks by a large margin, achieving an AUC greater than 0.9\non 57.8% of the targets in the DUDE benchmark.","url_abs":"http://arxiv.org/abs/1510.02855v1","url_pdf":"http://arxiv.org/pdf/1510.02855v1.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":"atomnet-a-deep-convolutional-neural-network","repo_url":"https://github.com/GilbertoQ/Bioinformatics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"atomnet-a-deep-convolutional-neural-network","repo_url":"https://github.com/cool21th/ai_drug_discovery","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"drug-discovery","task_name":"Drug Discovery"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1510.02855","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1510.02855"}},"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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