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Explainable artificial intelligence for molecular design in pharmaceutical research
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DOI：10.1039/D5SC08461J.png)
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Abstract
En 中文
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.
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