{"updated":"2025-01-19T07:41:20.799590+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00241342","sets":["1164:5352:11553:11808"]},"path":["11808"],"owner":"44499","recid":"241342","title":["High-Dimensional Neural Networkモデルによる汎用的な粗視化AI力場の構築"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-11-27"},"_buckets":{"deposit":"74f46d96-0841-449d-9f09-a8653e983dd6"},"_deposit":{"id":"241342","pid":{"type":"depid","value":"241342","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"High-Dimensional Neural Networkモデルによる汎用的な粗視化AI力場の構築","author_link":["664182","664178","664181","664180","664179","664183","664184"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"High-Dimensional Neural Networkモデルによる汎用的な粗視化AI力場の構築"},{"subitem_title":"Development of a Generalized Coarse-Grained AI Force Field using a High-Dimensional Neural Network","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2024-11-27","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"富士通株式会社"},{"subitem_text_value":"富士通株式会社"},{"subitem_text_value":"富士通株式会社"},{"subitem_text_value":"富士通株式会社"},{"subitem_text_value":"国立研究開発法人理化学研究所"},{"subitem_text_value":"国立研究開発法人理化学研究所"},{"subitem_text_value":"京都大学／国立研究開発法人理化学研究所"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Fujitsu Limited","subitem_text_language":"en"},{"subitem_text_value":"Fujitsu Limited","subitem_text_language":"en"},{"subitem_text_value":"Fujitsu Limited","subitem_text_language":"en"},{"subitem_text_value":"Fujitsu Limited","subitem_text_language":"en"},{"subitem_text_value":"RIKEN Center for Computational Science","subitem_text_language":"en"},{"subitem_text_value":"RIKEN Center for Computational Science","subitem_text_language":"en"},{"subitem_text_value":"Kyoto University / RIKEN Center for Computational Science","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/241342/files/IPSJ-BIO24080014.pdf","label":"IPSJ-BIO24080014.pdf"},"date":[{"dateType":"Available","dateValue":"2026-11-27"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-BIO24080014.pdf","filesize":[{"value":"3.3 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"41"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"af305a12-20e4-4d42-b16a-04ce90feec1f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"山田, 芙夕楓"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大山, 洋介"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"長坂, 侑亮"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"奥野, 伸吾"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"金田, 亮"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"徳久, 淳師"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"奥野, 恭史"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12055912","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8590","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"高精度なエネルギー予測はタンパク質ダイナミクスの解明に不可欠であり，機械学習を用いた AI 力場の開発が注目されている．しかしながら，既存の AI 力場ではタンパク質の種類ごとに個別の学習データとモデルが必要となり，汎化性能に関する課題が存在する．本研究では，新規タンパク質の全原子エネルギー予測可能な汎用的な粗視化 AI 力場を開発した．複数種のタンパク質の大規模構造データと単一の高次元ニューラルネットワークを用いて学習を行う手法を提案した．またメモリ容量制限を超えるデータセットに対応可能な新規データ読み出し方法を提案した．結果，AlphaFold ShortMD データセットにおいては相関係数（R値）0.82，Enhanced MD Sampling データセットにおいては R 値 0.74 という高い予測精度を実現した．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Accurate energy prediction is crucial for understanding protein dynamics, leading to significant interest in the development of machine-learning based AI force fields. However, existing AI force fields require individual training data and models for each protein type, resulting in challenges regarding generalization performance. This study presents a generalized coarse-grained AI force field capable of predicting all-atom energies of novel proteins. We propose a training method using a large-scale structural dataset of multiple protein types and a single high-dimensional neural network. Furthermore, a novel data reading method is proposed to handle datasets exceeding memory capacity limitations. The resulting model achieved high prediction accuracy, with correlation coefficients (R-values) of 0.82 and 0.74 for the AlphaFold ShortMD and Enhanced MD Sampling datasets, respectively.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"9","bibliographic_titles":[{"bibliographic_title":"研究報告バイオ情報学（BIO）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-11-27","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"14","bibliographicVolumeNumber":"2024-BIO-80"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:45:56.686494+00:00","id":241342,"links":{}}