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Advanced mathematics, such as multiscale weighted colored graph and elementspecific persistent homology, and machine learning including deep neuralnetworks were integrated to construct mathematical deep learning models forpose and binding affinity prediction and ranking in the last two D3R grandchallenges in computer-aided drug design and discovery. D3R Grand Challenge 2(GC2) focused on the pose prediction and binding affinity ranking and freeenergy prediction for Farnesoid X receptor ligands. Our models obtained the topplace in absolute free energy prediction for free energy Set 1 in Stage 2. Thelatest competition, D3R Grand Challenge 3 (GC3), is considered as the mostdifficult challenge so far. It has 5 subchallenges involving Cathepsin S andfive other kinase targets, namely VEGFR2, JAK2, p38-$\alpha$, TIE2, and ABL1.There is a total of 26 official competitive tasks for GC3. Our predictions wereranked 1st in 10 out of 26 official competitive tasks.

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