Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges


Advanced mathematics, such as multiscale weighted colored subgraph and element specific persistent homology, and machine learning including deep neural networks were integrated to construct mathematical deep learning models for pose and binding affinity prediction and ranking in the last two D3R Grand Challenges in computer-aided drug design and discovery. D3R Grand Challenge 2 focused on the pose prediction, binding affinity ranking and free energy prediction for Farnesoid X receptor ligands. Our models obtained the top place in absolute free energy prediction for free energy set 1 in stage 2. The latest competition, D3R Grand Challenge 3 (GC3), is considered as the most difficult challenge so far. It has five subchallenges involving Cathepsin S and five other kinase targets, namely VEGFR2, JAK2, p38-α, TIE2, and ABL1. There is a total of 26 official competitive tasks for GC3. Our predictions were ranked 1st in 10 out of these 26 tasks.

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This work was supported in part by NSF Grants IIS-1302285, DMS-1721024 and DMS-1761320 and MSU Center for Mathematical Molecular Biosciences Initiative.

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Correspondence to Guo-Wei Wei.

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Nguyen, D.D., Cang, Z., Wu, K. et al. Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges. J Comput Aided Mol Des 33, 71–82 (2019).

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  • Drug design
  • Pose prediction
  • Binding affinity
  • Machine learning
  • Algebraic topology
  • Graph theory