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

AI is transforming drug discovery but creating a transparency problem: As artificial intelligence and machine learning models become more complex in pharmaceutical research, they're increasingly difficult to understand and interpret—a critical issue when these systems influence decisions about which drugs to develop and test.

Explainable AI (XAI) makes drug discovery models more trustworthy: XAI techniques provide interpretable explanations for how AI models make predictions, enabling researchers and regulators to understand why a model recommends a particular drug candidate or predicts a compound will be toxic, rather than just accepting a "black box" recommendation.

XAI has practical applications across the drug development pipeline: The paper identifies specific use cases where explainability matters most—target identification (understanding which disease mechanisms to target), compound design (knowing what molecular features make promising drugs), and toxicity prediction (determining which candidates are likely to be unsafe).

Significant challenges remain in implementing XAI for drug discovery: While the survey identifies XAI as a promising approach, it acknowledges limitations and challenges in applying these techniques to pharmaceutical research, suggesting the field is still developing best practices for responsible AI deployment in this high-stakes domain.

XAI represents a path toward more responsible pharmaceutical AI: By making AI decision-making transparent and interpretable, XAI can help ensure that AI systems in drug discovery are trustworthy, accountable, and aligned with scientific rigor and patient safety standards.