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2024 Artificial Intelligence for Biopharma Conference
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The First Global AI Drug Development Competition
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Published in Bioinformatics, 2024
oral presentation in ISMB 2024
Recommended citation: Zuolong Zhang, Xin He, Dazhi Long, Gang Luo, Shengbo Chen, Enhancing generalizability and performance in drug–target interaction identification by integrating pharmacophore and pre-trained models, Bioinformatics, Volume 40, Issue Supplement_1, July 2024, Pages i539–i547, https://doi.org/10.1093/bioinformatics/btae240
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Published in Journal of Chemical Information and Modeling, 2025
Recommended citation: Zhang, Z., Liu, F., Shang, X., Chen, S., Zuo, F., Wu, Y., & Long, D. (2025). ComNet: A Multiview Deep Learning Model for Predicting Drug Combination Side Effects. Journal of chemical information and modeling, 10.1021/acs.jcim.4c01737. Advance online publication. https://doi.org/10.1021/acs.jcim.4c01737
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Published in Molecular Diversity, 2025
Recommended citation: Zhang, Z., Luo, G., Ma, Y. et al. GraphkmerDTA: integrating local sequence patterns and topological information for drug-target binding affinity prediction and applications in multi-target anti-Alzheimer’s drug discovery. Mol Divers (2025). https://doi.org/10.1007/s11030-024-11065-7
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Published:
we propose a novel DTA prediction method named HeteroDTA. Specifically, a multi-view compound feature extraction module is constructed to model the atom–bond graph and pharmacophore graph. The residue concat graph and protein sequence are also utilized to model protein structure and function. Moreover, to enhance the generalization capability and reduce the dependence on task-specific labeled data, pre-trained models are utilized to initialize the atomic features of the compounds and the embedding representations of the protein sequence. A context-aware nonlinear feature fusion method is also proposed to learn interaction patterns between compounds and proteins. Experimental results on public benchmark datasets show that HeteroDTA significantly outperforms existing methods. In addition, HeteroDTA shows excellent generalization performance in cold-start experiments and superiority in the representation learning ability of drug–target pairs. Finally, the effectiveness of HeteroDTA is demonstrated in a real-world drug discovery study.
Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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