ArticleJournal of computer-aided molecular design2022
Affinity prediction using deep learning based on SMILES input for D3R grand challenge 4.
Article in Journal of computer-aided molecular design, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Artificial intelligence in drug development for delirium and Alzheimer's disease.Acta pharmaceutica Sinica. B · 2025Review
- DTBA-net: Drug-Target Binding Affinity prediction using feature selection in hybrid CNN model.Journal of computer-aided molecular design · 2025Article
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Authors and funding
4 authors.
Funding
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.
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Registered trials
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