  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. It has 5 subchallenges involving Cathepsin S and
five other kinase targets, namely VEGFR2, JAK2, p38-$\alpha$, TIE2, and ABL1.
There is a total of 26 official competitive tasks for GC3. Our predictions were
ranked 1st in 10 out of 26 official competitive tasks.
