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Affinity prediction using deep learning based on SMILES input for D3R grand challenge 4

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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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Data availability

All data are publicly available. Please refer to the code availability section for detail.

Code availability

The model code and data is available at : https://github.com/arwhirang/affinity_prediction_BGNN.

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Funding

The study is supported by National Research Council of Science & Technology (NST) grant by the Korea government (MSIP) (No. CAP-17-01-KIST Europe).

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Authors and Affiliations

  1. Kist Europe, Campus E7 1 66123, Saarbrücken , Germany

    Sangrak Lim, Yong Oh Lee, Juyong Yoon & Young Jun Kim

  2. Industrial and Data Engineering Department of Hongik University, Seoul, Republic of Korea

    Yong Oh Lee

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  1. Sangrak Lim
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  2. Yong Oh Lee
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  4. Young Jun Kim
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Contributions

The study was designed by SL, and YL. SL wrote the code and performed the analysis. The original manuscript was written by SL, and YL. All authors (SL, YL, JY, and YK) have reviewed and edited the manuscript. YL and YK acquired the funding. All authors have given approval to the final version of the manuscript.

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Correspondence to Sangrak Lim.

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Lim, S., Lee, Y.O., Yoon, J. et al. Affinity prediction using deep learning based on SMILES input for D3R grand challenge 4. J Comput Aided Mol Des 36, 225–235 (2022). https://doi.org/10.1007/s10822-022-00448-3

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  • DOI: https://doi.org/10.1007/s10822-022-00448-3

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