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An adaptive graph learning method for automated molecular interactions and properties predictions.

Y. Li, C. Hsieh, R. Lu, X. Gong, X. Wang, P. Li, S. Liu, Y. Tian, D. Jiang, J. Yan, Q. Bai, H. Liu, S. Zhang, и X. Yao. Nat. Mach. Intell., 4 (7): 645-651 (2022)

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Li Li

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Improving drug-target affinity prediction via feature fusion and knowledge distillation.R. Lu, J. Wang, P. Li, Y. Li, S. Tan, Y. Pan, H. Liu, P. Gao, G. Xie, и X. Yao. Briefings Bioinform., (мая 2023)An effective self-supervised framework for learning expressive molecular global representations to drug discovery.P. Li, J. Wang, Y. Qiao, H. Chen, Y. Yu, X. Yao, P. Gao, G. Xie, и S. Song. Briefings Bioinform., (2021)HCL: Improving Graph Representation with Hierarchical Contrastive Learning.J. Wang, W. Li, C. Hou, X. Tang, Y. Qiao, R. Fang, P. Li, P. Gao, и G. Xie. ISWC, том 13489 из Lecture Notes in Computer Science, стр. 108-124. Springer, (2022)TrimNet: learning molecular representation from triplet messages for biomedicine.P. Li, Y. Li, C. Hsieh, S. Zhang, X. Liu, H. Liu, S. Song, и X. Yao. Briefings Bioinform., (2021)An adaptive graph learning method for automated molecular interactions and properties predictions.Y. Li, C. Hsieh, R. Lu, X. Gong, X. Wang, P. Li, S. Liu, Y. Tian, D. Jiang, J. Yan и 4 other автор(ы). Nat. Mach. Intell., 4 (7): 645-651 (2022)Simulated annealing for optimization of graphs and sequences.X. Liu, P. Li, F. Meng, H. Zhou, H. Zhong, J. Zhou, L. Mou, и S. Song. CoRR, (2021)Learn molecular representations from large-scale unlabeled molecules for drug discovery.P. Li, J. Wang, Y. Qiao, H. Chen, Y. Yu, X. Yao, P. Gao, G. Xie, и S. Song. CoRR, (2020)Deep generative model for drug design from protein target sequence.Y. Chen, Z. Wang, L. Wang, J. Wang, P. Li, D. Cao, X. Zeng, X. Ye, и T. Sakurai. J. Cheminformatics, 15 (1): 38 (декабря 2023)Deep geometric representations for modeling effects of mutations on protein-protein binding affinity.X. Liu, Y. Luo, P. Li, S. Song, и J. Peng. PLoS Comput. Biol., (2021)PASH at TREC 2020 Deep Learning Track: Dense Matching for Nested Ranking.Y. Qiao, H. Chen, L. Cao, L. Chen, P. Li, J. Wang, P. Gao, Y. Ni, и G. Xie. TREC, том 1266 из NIST Special Publication, National Institute of Standards and Technology (NIST), (2020)

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Li Li

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