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

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Advanced mathematics, such as multiscale weighted colored graph 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 (GC2) focused on the pose prediction and 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.