Yuel: Compound-Protein Interaction Prediction with High Generalizability
By Jian Wang, N. V. Dokholyan. 2021
Virtual drug screening has the potential to revolutionize the stagnant drug discovery field due to its low cost and fast speed. Predicting binding affinities between small molecules and the protein target is at the core of computational drug screening. Deep learning-based approaches have recently been adapted to predict binding affinities and claim to achieve high prediction accuracy in their tests, however, we show that current approaches are not reliable for virtual drug screening due to the lack of generalizability, i.e. the ability to predict interactions between unknown proteins and unknown small molecules. To address this shortcoming, we develop a compound-protein interaction predictor, Yuel. Upon comprehensive tests on various datasets, we find that out of all the deep-learning approaches surveyed, only Yuel can predict interactions between unknown compounds and unknown proteins. Additionally, Yuel can also be utilized to identify compound atoms and proteins residues that are involved in the binding.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Wei Lu
- Qifeng Wu
- Jixian Zhang
- Jiahua Rao
- Chengtao Li
- Shuangjia Zheng
- Shuya Nakata
- Gabriele Corso
Citations in the corpus
- TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction (2022, methodology): “…et al. [2004], prior machine learning-based works either use complex-free models to predict the binding affinity directly from protein-ligand pairs Wang and Dokholyan [2021], Li et al. [2020], Tsubaki et al. [2019], Gao et al. [2018], Karimi et al. [2019], Zheng et al. [2020] or make predictions…”