Zuolong Zhang

Zuolong Zhang

A boy wandering in the beach of AI-assisted drug discovery

Posts by Collection

portfolio

publications

Enhancing generalizability and performance in drug–target interaction identification by integrating pharmacophore and pre-trained models

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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GraphkmerDTA: integrating local sequence patterns and topological information for drug-target binding affinity prediction and applications in multi-target anti-Alzheimer’s drug discovery

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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talks

Conference Proceeding talk on AI for Drug Discovery

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.

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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