Alec Lamens, Jürgen Bajorath
The rise of artificial intelligence (AI) has taken machine learning (ML) in molecular design to a new level. As ML increasingly relies on complex deep learning frameworks, the inability to understand predictions of black-box models has become a topical issue. Consequently, there is strong interest in the field of explainable AI (XAI) to bridge the gap between black-box models and the acceptance of their predictions, especially at interfaces with experimental disciplines. Therefore, XAI methods must go beyond extracting learning patterns from ML models and present explanations of predictions in a human-centered, transparent, and interpretable manner. In this Perspective, we examine current challenges and opportunities for XAI in molecular design and evaluate the benefits of incorporating domain-specific knowledge into XAI approaches for model refinement, experimental design, and hypothesis testing. In this context, we also discuss the current limitations in evaluating results from chemical language models that are increasingly used in molecular design and drug discovery.