Abstract
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.
Original language | English |
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Title of host publication | 2019 IEEE International conference on bioinformatics and biomedicine (BIBM) |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 311-317 |
Number of pages | 7 |
ISBN (Electronic) | 9781728118673, 9781728118680 |
DOIs | |
Publication status | Published - 07 Feb 2020 |
Event | 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Hard Rock Hotel San Diego, San Diego, United States Duration: 18 Nov 2019 → 21 Nov 2019 https://ieeebibm.org/BIBM2019/ https://missouri.app.box.com/s/ak2td86vxzo8rx28qe6og4qng7xbzitb (program) https://ieeexplore.ieee.org/xpl/conhome/8965270/proceeding (proceedings) |
Conference
Conference | 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
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Country/Territory | United States |
City | San Diego |
Period | 18/11/19 → 21/11/19 |
Internet address |