  Motivation: Many high-performance DTA models have been proposed, but they are
mostly black-box and thus lack human interpretability. Explainable AI (XAI) can
make DTA models more trustworthy, and can also enable scientists to distill
biological knowledge from the models. Counterfactual explanation is one popular
approach to explaining the behaviour of a deep neural network, which works by
systematically answering the question "How would the model output change if the
inputs were changed in this way?". Most counterfactual explanation methods only
operate on single input data. It remains an open problem how to extend
counterfactual-based XAI methods to DTA models, which have two inputs, one for
drug and one for target, that also happen to be discrete in nature.
  Methods: We propose a multi-agent reinforcement learning framework,
Multi-Agent Counterfactual Drug target binding Affinity (MACDA), to generate
counterfactual explanations for the drug-protein complex. Our proposed
framework provides human-interpretable counterfactual instances while
optimizing both the input drug and target for counterfactual generation at the
same time.
  Results: We benchmark the proposed MACDA framework using the Davis dataset
and find that our framework produces more parsimonious explanations with no
loss in explanation validity, as measured by encoding similarity and QED. We
then present a case study involving ABL1 and Nilotinib to demonstrate how MACDA
can explain the behaviour of a DTA model in the underlying substructure
interaction between inputs in its prediction, revealing mechanisms that align
with prior domain knowledge.
