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

In drug discovery, it is crucial to assess the drug–target binding affinity (DTA). Although molecular docking is widely used, computational efficiency limits its application in large-scale virtual screening. Deep learning-based methods learn virtual scoring functions from labeled datasets and can quickly predict affinity. However, there are three limitations. First, existing methods only consider the atom-bond graph or one-dimensional sequence representations of compounds, ignoring the information about functional groups (pharmacophores) with specific biological activities. Second, relying on limited labeled datasets fails to learn comprehensive embedding representations of compounds and proteins, resulting in poor generalization performance in complex scenarios. Third, existing feature fusion methods cannot adequately capture contextual interaction information.

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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