  Motivation: Several accurate deep learning models have been proposed to
predict drug-target affinity (DTA). However, all of these models are black box
hence are difficult to interpret and verify its result, and thus risking
acceptance. Explanation is necessary to allow the DTA model more trustworthy.
Explanation with counterfactual provides human-understandable examples. Most
counterfactual explanation methods only operate on single input data, which are
in tabular or continuous forms. In contrast, the DTA model has two discrete
inputs. It is challenging for the counterfactual generation framework to
optimize both discrete inputs at the same time.
  Results: 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. The result on the Davis dataset shows the advantages of the proposed
MACDA framework compared with previous works.
