Predicting HIV Resistance with the 3D Neighborhood Kernel

Recently, we developed the 3D Neighborhood Kernel (3DNK), which acts on 3D structures of small molecules and proteins. We showed its state-of-the-art performance on several biological datasets. However, 3D data are in many cases dicult to obtain. For this reason, we adopt a dierent strategy: instead of requiring actual 3D structures, we use as input protein sequences, of which we approximate the 3D structure through homology modelling. Then, we apply 3DNK on the approximated 3D protein structures and show that, on the task of predicting HIV resistance, we obtain better results than when using a kernel function based on the protein sequences alone.