  Predicting the physical interaction of proteins is a cornerstone problem in
computational biology. New classes of learning-based algorithms are actively
being developed, and are typically trained end-to-end on protein complex
structures extracted from the Protein Data Bank. These training datasets tend
to be large and difficult to use for prototyping and, unlike image or natural
language datasets, they are not easily interpretable by non-experts. We present
Dock2D-IP and Dock2D-IF, two "toy" datasets that can be used to select
algorithms predicting protein-protein interactions$\unicode{x2014}$or any other
type of molecular interactions. Using two-dimensional shapes as input, each
example from Dock2D-IP ("interaction pose") describes the interaction pose of
two shapes known to interact and each example from Dock2D-IF ("interaction
fact") describes whether two shapes form a stable complex or not. We propose a
number of baseline solutions to the problem and show that the same underlying
energy function can be learned either by solving the interaction pose task
(formulated as an energy-minimization "docking" problem) or the
fact-of-interaction task (formulated as a binding free energy estimation
problem).
