{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T07:07:54Z","timestamp":1773558474662,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T00:00:00Z","timestamp":1732752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Kunshan Municipal Government research funding","award":["23KKSGR027"],"award-info":[{"award-number":["23KKSGR027"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,11,28]]},"DOI":"10.1145\/3715020.3715040","type":"proceedings-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T12:42:44Z","timestamp":1749040964000},"page":"14-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Molecular Docking Performance with a GNN-Based Algorithm Selection Model"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7266-7386","authenticated-orcid":false,"given":"Yiliang","family":"Yuan","sequence":"first","affiliation":[{"name":"Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6885-6775","authenticated-orcid":false,"given":"Mustafa","family":"Misir","sequence":"additional","affiliation":[{"name":"Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,6,4]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"publisher","unstructured":"P.\u00a0C. Agu C.\u00a0A. Afiukwa O.\u00a0U. Orji E.\u00a0M. Ezeh I.\u00a0H. Ofoke C.\u00a0O. Ogbu E.\u00a0I. Ugwuja and P.\u00a0M. Aja. 2023. Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management. Scientific Reports 13 (2023) 13398. 10.1038\/s41598-023-40160-2","DOI":"10.1038\/s41598-023-40160-2"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"publisher","unstructured":"Laith Alzubaidi Jinglan Zhang Amjad\u00a0J. Humaidi Ayad Al-Dujaili Ye Duan Omran Al-Shamma J. Santamar\u00eda Mohammed\u00a0A. Fadhel Muthana Al-Amidie and Laith Farhan. 2021. Review of deep learning: concepts CNN architectures challenges applications future directions. Journal of Big Data 8 (2021) 53. 10.1186\/s40537-021-00444-8","DOI":"10.1186\/s40537-021-00444-8"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"publisher","unstructured":"Tianlai Chen Xiwen Shu Huiyuan Zhou Floyd\u00a0A Beckford and Mustafa Misir. 2023. Algorithm selection for protein\u2013ligand docking: strategies and analysis on ACE. Scientific Reports 13 1 (2023) 8219. 10.1038\/s41598-023-35132-5","DOI":"10.1038\/s41598-023-35132-5"},{"key":"e_1_3_3_1_5_2","volume-title":"International Conference on Learning Representations (ICLR)","author":"Corso Gabriele","year":"2023","unstructured":"Gabriele Corso, Hannes St\u00e4rk, Bowen Jing, Regina Barzilay, and Tommi Jaakkola. 2023. DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"publisher","unstructured":"Irini Doytchinova. 2022. Drug Design\u2014Past Present Future. Molecules 27 5 (2022). 10.3390\/molecules27051496","DOI":"10.3390\/molecules27051496"},{"key":"e_1_3_3_1_7_2","unstructured":"Lukas Fehring Jonas Hanselle and Alexander Tornede. 2022. HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. arxiv:https:\/\/arXiv.org\/abs\/2210.17341\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2210.17341"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"publisher","unstructured":"Richard\u00a0A. Friesner Jay\u00a0L. Banks Robert\u00a0B. Murphy Thomas\u00a0A. Halgren Jasna\u00a0J. Klicic Daniel\u00a0T. Mainz Matthew\u00a0P. Repasky Eric\u00a0H. Knoll Mee Shelley Jason\u00a0K. Perry David\u00a0E. Shaw Perry Francis and Peter\u00a0S. Shenkin. 2004. Glide: A New Approach for Rapid Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy. Journal of Medicinal Chemistry 47 7 (Mar 2004) 1739\u20131749. 10.1021\/jm0306430","DOI":"10.1021\/jm0306430"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"publisher","unstructured":"Inbal Halperin Buyong Ma Haim Wolfson and Ruth Nussinov. 2002. Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins: Structure Function and Bioinformatics 47 4 (2002) 409\u2013443. 10.1002\/prot.10115","DOI":"10.1002\/prot.10115"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"publisher","unstructured":"Nafisa\u00a0M. Hassan Amr\u00a0A. Alhossary Yuguang Mu and Chee-Keong Kwoh. 2017. Protein-Ligand Blind Docking Using QuickVina-W with Inter-Process Spatio-Temporal Integration. Scientific Reports 7 (2017) 15451. 10.1038\/s41598-017-15571-7","DOI":"10.1038\/s41598-017-15571-7"},{"key":"e_1_3_3_1_11_2","unstructured":"Weihua Hu Bowen Liu Joseph Gomes Marinka Zitnik Percy Liang Vijay Pande and Jure Leskovec. 2020. Strategies for Pre-training Graph Neural Networks. arxiv:https:\/\/arXiv.org\/abs\/1905.12265\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/1905.12265"},{"key":"e_1_3_3_1_12_2","volume-title":"Advances in Neural Information Processing Systems","author":"Jamasb Arian\u00a0Rokkum","year":"2022","unstructured":"Arian\u00a0Rokkum Jamasb, Ramon\u00a0Vi\u00f1as Torn\u00e9, Eric\u00a0J Ma, Yuanqi Du, Charles Harris, Kexin Huang, Dominic Hall, Pietro Lio, and Tom\u00a0Leon Blundell. 2022. Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks. In Advances in Neural Information Processing Systems, Alice\u00a0H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho (Eds.). https:\/\/openreview.net\/forum?id=9xRZlV6GfOX"},{"key":"e_1_3_3_1_13_2","unstructured":"Thomas\u00a0N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. arxiv:https:\/\/arXiv.org\/abs\/1609.02907\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/1609.02907"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","unstructured":"David\u00a0Ryan Koes Matthew\u00a0P. Baumgartner and Carlos\u00a0J. Camacho. 2013. Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise. Journal of Chemical Information and Modeling 53 8 (2013) 1893\u20131904. 10.1021\/ci300604z","DOI":"10.1021\/ci300604z"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"publisher","unstructured":"Irwin\u00a0D. Kuntz Jeffrey\u00a0M. Blaney Stuart\u00a0J. Oatley Robert Langridge and Thomas\u00a0E. Ferrin. 1982. A geometric approach to macromolecule-ligand interactions. Journal of Molecular Biology 161 2 (1982) 269\u2013288. 10.1016\/0022-2836(82)90153-X","DOI":"10.1016\/0022-2836(82)90153-X"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"publisher","unstructured":"Zhihai Liu Minyi Su Li Han Jie Liu Qifan Yang Yan Li and Renxiao Wang. 2017. Forging the Basis for Developing Protein\u2013Ligand Interaction Scoring Functions. Accounts of Chemical Research 50 2 (2017) 302\u2013309. 10.1021\/acs.accounts.6b00491","DOI":"10.1021\/acs.accounts.6b00491"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10170"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"crossref","unstructured":"Wei Lu Qifeng Wu Jixian Zhang Jiahua Rao Chengtao Li and Shuangjia Zheng. 2022. Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction. Advances in Neural Information Processing Systems (2022).","DOI":"10.1101\/2022.06.06.495043"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"publisher","unstructured":"Andrew\u00a0T. McNutt Paul Francoeur Rishal Aggarwal Tomohide Masuda Rocco Meli Matthew Ragoza Jocelyn Sunseri and David\u00a0Ryan Koes. 2021. GNINA 1.0: Molecular Docking with Deep Learning. Journal of Cheminformatics 13 (2021). 10.1186\/s13321-021-00522-2","DOI":"10.1186\/s13321-021-00522-2"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"publisher","unstructured":"Erxue Min Xifeng Guo Qiang Liu Gen Zhang Jianjing Cui and Jun Long. 2018. A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture. IEEE Access 6 (2018) 39501\u201339514. 10.1109\/ACCESS.2018.2855437","DOI":"10.1109\/ACCESS.2018.2855437"},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"publisher","unstructured":"Ammar Mohammed and Rania Kora. 2023. A comprehensive review on ensemble deep learning: Opportunities and challenges. Journal of King Saud University - Computer and Information Sciences 35 2 (2023) 757\u2013774. 10.1016\/j.jksuci.2023.01.014","DOI":"10.1016\/j.jksuci.2023.01.014"},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"publisher","unstructured":"Ghaith Mqawass and P. Popov. 2024. GraphLambda: Fusion Graph Neural Networks for Binding Affinity Prediction. Journal of Chemical Information and Modeling 64 7 (2024) 2323\u20132330. 10.1021\/acs.jcim.3c00771","DOI":"10.1021\/acs.jcim.3c00771"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"publisher","unstructured":"Mustafa M\u0131s\u0131r and Mich\u00e8le Sebag. 2017. Alors: An algorithm recommender system. Artificial Intelligence 244 (2017) 291\u2013314. 10.1016\/j.artint.2016.12.001","DOI":"10.1016\/j.artint.2016.12.001"},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"crossref","unstructured":"Ivan Olier Noureddin Sadawi G\u00a0Richard Bickerton Joaquin Vanschoren Crina Grosan Larisa Soldatova and Ross\u00a0D King. 2018. Meta-QSAR: a large-scale application of meta-learning to drug design and discovery. Machine Learning 107 (2018) 285\u2013311.","DOI":"10.1007\/s10994-017-5685-x"},{"key":"e_1_3_3_1_25_2","doi-asserted-by":"publisher","unstructured":"Xinru Qiu Han Li Greg Ver\u00a0Steeg and Adam Godzik. 2024. Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development. Biomolecules 14 3 (2024). 10.3390\/biom14030339","DOI":"10.3390\/biom14030339"},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"publisher","unstructured":"Wen Song Yi Liu Zhiguang Cao Yaoxin Wu and Qiqiang Li. 2023. Instance-specific algorithm configuration via unsupervised deep graph clustering. Engineering Applications of Artificial Intelligence 125 (2023) 106740. 10.1016\/j.engappai.2023.106740","DOI":"10.1016\/j.engappai.2023.106740"},{"key":"e_1_3_3_1_27_2","first-page":"20503","volume-title":"International Conference on Machine Learning","author":"St\u00e4rk Hannes","year":"2022","unstructured":"Hannes St\u00e4rk, Octavian Ganea, Lagnajit Pattanaik, Regina Barzilay, and Tommi Jaakkola. 2022. Equibind: Geometric deep learning for drug binding structure prediction. In International Conference on Machine Learning. PMLR, 20503\u201320521."},{"key":"e_1_3_3_1_28_2","doi-asserted-by":"publisher","unstructured":"Norberto S\u00e1nchez-Cruz. 2023. Deep graph learning in molecular docking: Advances and opportunities. Artificial Intelligence in the Life Sciences 3 (2023) 100062. 10.1016\/j.ailsci.2023.100062","DOI":"10.1016\/j.ailsci.2023.100062"},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"publisher","unstructured":"Oleg Trott and Arthur\u00a0J. Olson. 2010. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function efficient optimization and multithreading. Journal of Computational Chemistry 31 2 (2010) 455\u2013461. 10.1002\/jcc.21334","DOI":"10.1002\/jcc.21334"},{"key":"e_1_3_3_1_30_2","unstructured":"Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. arxiv:https:\/\/arXiv.org\/abs\/1710.10903\u00a0[stat.ML] https:\/\/arxiv.org\/abs\/1710.10903"},{"key":"e_1_3_3_1_31_2","doi-asserted-by":"publisher","unstructured":"Jos\u00e9\u00a0Luis Vel\u00e1zquez-Libera Fabio Dur\u00e1n-Verdugo Alejandro Vald\u00e9s-Jim\u00e9nez Gabriel N\u00fa\u00f1ez-Vivanco and Julio Caballero. 2020. LigRMSD: a web server for automatic structure matching and RMSD calculations among identical and similar compounds in protein-ligand docking. Bioinformatics 36 9 (2020) 2912\u20132914. 10.1093\/bioinformatics\/btaa018","DOI":"10.1093\/bioinformatics\/btaa018"},{"key":"e_1_3_3_1_32_2","doi-asserted-by":"publisher","unstructured":"Andrew\u00a0D White. 2021. Deep Learning for Molecules and Materials. Living Journal of Computational Molecular Science 3 1 (2021) 1499. 10.33011\/livecoms.3.1.1499","DOI":"10.33011\/livecoms.3.1.1499"},{"key":"e_1_3_3_1_33_2","doi-asserted-by":"publisher","unstructured":"D.H. Wolpert and W.G. Macready. 1997. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1 1 (1997) 67\u201382. 10.1109\/4235.585893","DOI":"10.1109\/4235.585893"},{"key":"e_1_3_3_1_34_2","doi-asserted-by":"publisher","unstructured":"Tian Xie and Jeffrey\u00a0C. Grossman. 2018. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. Phys. Rev. Lett. 120 (2018) 145301. Issue 14. 10.1103\/PhysRevLett.120.145301","DOI":"10.1103\/PhysRevLett.120.145301"},{"key":"e_1_3_3_1_35_2","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks? arxiv:https:\/\/arXiv.org\/abs\/1810.00826\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/1810.00826"},{"key":"e_1_3_3_1_36_2","doi-asserted-by":"publisher","unstructured":"Lin Xu Frank Hutter Holger\u00a0H. Hoos and Kevin Leyton-Brown. 2008. SATzilla: portfolio-based algorithm selection for SAT. J. Artif. Int. Res. 32 1 (jun 2008) 565\u2013606. 10.5555\/1622673.1622687","DOI":"10.5555\/1622673.1622687"},{"key":"e_1_3_3_1_37_2","unstructured":"Yuejiang Yu Shuqi Lu Zhifeng Gao Hang Zheng and Guolin Ke. 2023. Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking? arxiv:https:\/\/arXiv.org\/abs\/2302.07134\u00a0[q-bio.BM] https:\/\/arxiv.org\/abs\/2302.07134"},{"key":"e_1_3_3_1_38_2","doi-asserted-by":"publisher","unstructured":"Jie Zhou Ganqu Cui Shutao Hu Zhengyan Zhang Cheng Yang Zhiyuan Liu Li Wang Chang Li and Maosong Sun. 2020. Graph Neural Networks: A Review of Methods and Applications. AI Open 1 (2020) 57\u201381. 10.1016\/j.aiopen.2021.01.001","DOI":"10.1016\/j.aiopen.2021.01.001"}],"event":{"name":"ICCBB 2024: 2024 8th International Conference on Computational Biology and Bioinformatics","location":"Kyoto Japan","acronym":"ICCBB 2024"},"container-title":["Proceedings of the 2024 8th International Conference on Computational Biology and Bioinformatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3715020.3715040","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3715020.3715040","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:56:52Z","timestamp":1750298212000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3715020.3715040"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,28]]},"references-count":37,"alternative-id":["10.1145\/3715020.3715040","10.1145\/3715020"],"URL":"https:\/\/doi.org\/10.1145\/3715020.3715040","relation":{},"subject":[],"published":{"date-parts":[[2024,11,28]]},"assertion":[{"value":"2025-06-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}