# Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Prediction

arXiv: https://arxiv.org/abs/2103.12983 · HTML: https://mlsanity.com/paper/2103.12983/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 46 references that are in the map.
- First posted on arXiv 24 Mar 2021.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [Counterfactual Visual Explanations](https://mlsanity.com/paper/1904.07451/) (2019)
- [Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives](https://mlsanity.com/paper/1802.07623/) (2018)
- [Actor-Attention-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1810.02912/) (2018)
- [Understanding Neural Networks through Representation Erasure](https://mlsanity.com/paper/1612.08220/) (2016)
- [Optimization of Molecules via Deep Reinforcement Learning](https://mlsanity.com/paper/1810.08678/) (2018)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [Generating Visual Explanations](https://mlsanity.com/paper/1603.08507/) (2016)
- [DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models](https://mlsanity.com/paper/2008.08353/) (2020)
- [WideDTA: prediction of drug-target binding affinity](https://mlsanity.com/paper/1902.04166/) (2019)
- [Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry](https://mlsanity.com/paper/1811.11310/) (2018)
- [GEFA: Early Fusion Approach in Drug-Target Affinity Prediction](https://mlsanity.com/paper/2009.12146/) (2020)
- [The Multi-Agent Reinforcement Learning in Malm"O (MARL"O) Competition](https://mlsanity.com/paper/1901.08129/) (2019)
- [Interpretable Deep Learning in Drug Discovery](https://mlsanity.com/paper/1903.02788/) (2019)
- [Edge Attention-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1802.04944/) (2018)
- [Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network](https://mlsanity.com/paper/1805.10988/) (2018)
- [Explaining Deep Graph Networks with Molecular Counterfactuals](https://mlsanity.com/paper/2011.05134/) (2020)

## Built on by (most cited first, 8 of 8)

- [Explainability in reinforcement learning: perspective and position](https://mlsanity.com/paper/2203.11547/) (2022)
- [Generative Explanations for Graph Neural Network: Methods and Evaluations](https://mlsanity.com/paper/2311.05764/) (2023)
- [Privacy Implications of Explainable AI in Data-Driven Systems](https://mlsanity.com/paper/2406.15789/) (2024)
- [Unstructured and structured data: Can we have the best of both worlds with large language models?](https://mlsanity.com/paper/2304.13010/) (2023)
- [Fair Recourse for All: Ensuring Individual and Group Fairness in Counterfactual Explanations](https://mlsanity.com/paper/2601.20449/) (2026)
- [Knowledge Distillation-Based Model Extraction Attack using GAN-based Private Counterfactual Explanations](https://mlsanity.com/paper/2404.03348/) (2024)
- [Where Black-box Drug-Target Interaction Prediction Models Look: Cross-Method Explainability](https://mlsanity.com/paper/2606.14245/) (2026)
- [Class-Aware Reinforcement Learning for Counterfactual Explanation Generation](https://mlsanity.com/paper/2607.27905/) (2026)

## Abstract (arXiv)

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.