DeepDock: Enhancing Ligand-protein Interaction Prediction by a Combination of Ligand and Structure Information
By Zhirui Liao, Ronghui You, Xiaodi Huang, Xiaojun Yao, Tao-wei Huang, Shanfeng Zhu. 2019
41 citations recorded by the source corpus.
The prediction of precise protein-ligand binding activities can accelerate drug discovery by virtual screening—a computational technique that predicts whether a small molecule ligand is able to bind to a specific target. Thus, it is crucial to improve the performance of virtual screening. However, previous models for solving this problem are either ligand-based or structure-based. In this paper, we propose a universal deep neural network model called DeepDock that predicts protein-ligand interaction by using both ligand and structure information. Using the combination of two types of information, our model consists of embedding, convolution, max pooling, and fully-connected layers. In particular, different types of inputs are concatenated before being fed into the fully-connected layers. In the experiments, we compare our approach to the competing methods against two benchmark datasets under different settings. The experiment results have demonstrated that DeepDock can improve predictive performance by more than 4% on both DUD-E and MUV datasets in terms of AUPR
Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, Computational Chemistry & Drug Discovery
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Citations in the corpus
- Accurate structure prediction of biomolecular interactions with AlphaFold 3 (2024, methodology): “Separately, we also train a version of AF3 that receives the ‘pocket information’ as used in some recent deep-learning work 24,26 (the results are shown in Extended Data Fig.”