{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T09:53:31Z","timestamp":1771062811812,"version":"3.50.1"},"reference-count":74,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T00:00:00Z","timestamp":1640649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Alibaba Innovative Research","award":["AN-GC-2020-002"],"award-info":[{"award-number":["AN-GC-2020-002"]}]},{"name":"Singapore Ministry of Education Academic Research Fund","award":["RG109\/19"],"award-info":[{"award-number":["RG109\/19"]}]},{"name":"Singapore Ministry of Education Academic Research Fund","award":["MOE2018-T2-1-033"],"award-info":[{"award-number":["MOE2018-T2-1-033"]}]},{"name":"Singapore Ministry of Education Academic Research Fund","award":["MOE-T2EP20120-0013"],"award-info":[{"award-number":["MOE-T2EP20120-0013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Artificial intelligence (AI)-based drug design has great promise to fundamentally change the landscape of the pharmaceutical industry. Even though there are great progress from handcrafted feature-based machine learning models, 3D convolutional neural networks (CNNs) and graph neural networks, effective and efficient representations that characterize the structural, physical, chemical and biological properties of molecular structures and interactions remain to be a great challenge. Here, we propose an equal-sized molecular 2D image representation, known as the molecular persistent spectral image (Mol-PSI), and combine it with CNN model for AI-based drug design. Mol-PSI provides a unique one-to-one image representation for molecular structures and interactions. In general, deep models are empowered to achieve better performance with systematically organized representations in image format. A well-designed parallel CNN architecture for adapting Mol-PSIs is developed for protein\u2013ligand binding affinity prediction. Our results, for the three most commonly used databases, including PDBbind-v2007, PDBbind-v2013 and PDBbind-v2016, are better than all traditional machine learning models, as far as we know. Our Mol-PSI model provides a powerful molecular representation that can be widely used in AI-based drug design and molecular data analysis.<\/jats:p>","DOI":"10.1093\/bib\/bbab527","type":"journal-article","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T12:30:43Z","timestamp":1638448243000},"source":"Crossref","is-referenced-by-count":24,"title":["Molecular persistent spectral image (Mol-PSI) representation for machine learning models in drug design"],"prefix":"10.1093","volume":"23","author":[{"given":"Peiran","family":"Jiang","sequence":"first","affiliation":[{"name":"Drug Discovery Intelligence , AI Center, , Wen Yi Xi Road, Yuhang District, Hangzhou City , 310000, Zhejiang, China"},{"name":"Alibaba Group DAMO Academy , AI Center, , Wen Yi Xi Road, Yuhang District, Hangzhou City , 310000, Zhejiang, China"}]},{"given":"Ying","family":"Chi","sequence":"additional","affiliation":[{"name":"Drug Discovery Intelligence , AI Center, , Wen Yi Xi Road, Yuhang District, Hangzhou City , 310000, Zhejiang, China"},{"name":"Alibaba Group DAMO Academy , AI Center, , Wen Yi Xi Road, Yuhang District, Hangzhou City , 310000, Zhejiang, China"}]},{"given":"Xiao-Shuang","family":"Li","sequence":"additional","affiliation":[{"name":"Drug Discovery Intelligence , AI Center, , Wen Yi Xi Road, Yuhang District, Hangzhou City, 310000, Zhejiang, China"},{"name":"Alibaba Group DAMO Academy , AI Center, , Wen Yi Xi Road, Yuhang District, Hangzhou City, 310000, Zhejiang, China"}]},{"given":"Zhenyu","family":"Meng","sequence":"additional","affiliation":[{"name":"Division of Mathematical Sciences, School of Physical and Mathematical Sciences , Nanyang Technological University, Singapore 637371"}]},{"given":"Xiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Division of Mathematical Sciences , , Nanyang Technological University, 637371, Singapore"},{"name":"School of Physical and Mathematical Sciences , , Nanyang Technological University, 637371, Singapore"},{"name":"Chern Institute of Mathematics and LPMC , Nankai University, 300071, Tianjin, China"}]},{"given":"Xian-Sheng","family":"Hua","sequence":"additional","affiliation":[{"name":"Drug Discovery Intelligence , AI Center, , Wen Yi Xi Road, Yuhang District, Hangzhou City , 310000, Zhejiang, China"},{"name":"Alibaba Group DAMO Academy , AI Center, , Wen Yi Xi Road, Yuhang District, Hangzhou City , 310000, Zhejiang, China"}]},{"given":"Kelin","family":"Xia","sequence":"additional","affiliation":[{"name":"Division of Mathematical Sciences , , Nanyang Technological University, 637371, Singapore"},{"name":"School of Physical and Mathematical Sciences , , Nanyang Technological University, 637371, Singapore"}]}],"member":"286","published-online":{"date-parts":[[2021,12,28]]},"reference":[{"issue":"11","key":"2023032209273682700_","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.1016\/j.drudis.2017.08.010","article-title":"From machine learning to deep learning: progress 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