# Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey

arXiv: https://arxiv.org/abs/2309.12177 · HTML: https://mlsanity.com/paper/2309.12177/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 94 references that are in the map.
- First posted on arXiv 21 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [A Survey on Neural Network Interpretability](https://mlsanity.com/paper/2012.14261/) (2020)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [Drug discovery with explainable artificial intelligence](https://mlsanity.com/paper/2007.00523/) (2020)
- [Artificial Intelligence in Drug Discovery: Applications and Techniques](https://mlsanity.com/paper/2106.05386/) (2021)
- [Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research](https://mlsanity.com/paper/2211.15343/) (2022)
- [Evolutionary learning of interpretable decision trees](https://mlsanity.com/paper/2012.07723/) (2020)
- [Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction](https://mlsanity.com/paper/2107.04119/) (2021)
- [Applications of artificial intelligence in drug development using real-world data](https://mlsanity.com/paper/2101.08904/) (2021)
- [GraphIX: Graph-based In silico XAI(explainable artificial intelligence) for drug repositioning from biopharmaceutical network](https://mlsanity.com/paper/2212.10788/) (2022)

## Built on by (most cited first, 4 of 4)

- [A Survey on Interpretability in Visual Recognition](https://mlsanity.com/paper/2507.11099/) (2025)
- [Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization](https://mlsanity.com/paper/2507.03318/) (2025)
- [A method for the systematic generation of graph XAI benchmarks via Weisfeiler-Leman coloring](https://mlsanity.com/paper/2505.12437/) (2025)
- [Towards a Transparent and Interpretable AI Model for Medical Image Classifications](https://mlsanity.com/paper/2509.16685/) (2025)

## Abstract (arXiv)

The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. Explainable Artificial Intelligence (XAI) is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models. In recent years, there has been an increasing interest in the application of XAI techniques to drug discovery. This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The article also covers the application of XAI in drug discovery, including target identification, compound design, and toxicity prediction. Furthermore, the article suggests potential future research directions for the application of XAI in drug discovery. The aim of this review article is to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field.