Affinity prediction using deep learning based on SMILES input for D3R grand challenge 4
- Authors
- Lim, Sangrak; Lee, Yong Oh; Yoon, Juyong; Kim, Young Jun
- Issue Date
- 1-Mar-2022
- Publisher
- SPRINGER
- Keywords
- Molecular docking; Binding affinity; D3R-drug design data resource; Deep learning
- Citation
- JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, v.36, no.3, pp 225 - 235
- Pages
- 11
- Journal Title
- JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- Volume
- 36
- Number
- 3
- Start Page
- 225
- End Page
- 235
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/32971
- DOI
- 10.1007/s10822-022-00448-3
- ISSN
- 0920-654X
1573-4951
- Abstract
- Modern molecular docking comprises the prediction of pose and affinity. Prediction of docking poses is required for affinity prediction when three-dimensional coordinates of the ligand have not been provided. However, a large number of feature engineering is required for existing methods. In addition, there is a need for a robust model for the sequential combination of pose and affinity prediction due to the probabilistic deviation of the ligand position issue. We propose a pipeline using a bipartite graph neural network and transfer learning trained on a re-docking dataset. We evaluated our model on the released data from drug design data resource grand challenge 4 (D3R GC4). The two target protein data provided by the challenge have different patterns. The model outperformed the best participant by 9% on the BACE target protein from stage 2. Further, our model showed competitive performance on the CatS target protein.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/32971)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.