Unveiling interatomic distances influencing the reaction coordinates in alanine dipeptide isomerization: An explainable deep learning approach

Deep learning can identify simpler molecular descriptors than traditionally used: The study demonstrates that a molecule's structural changes during chemical reactions can be predicted using basic interatomic distances and bond angles rather than complex dihedral angles, suggesting that AI can sometimes find more straightforward representations of complex biological processes.

Explainable AI (XAI) revealed which molecular features actually matter: By applying XAI techniques, the researchers identified specific interatomic distances that are most important for understanding how alanine dipeptide changes shape, moving beyond "black box" predictions to show why the model makes its decisions—a key requirement for trustworthy AI in scientific research.

Simpler models may be more interpretable and generalizable: The finding that interatomic distances alone can capture the transition state suggests that AI models using simpler, more fundamental variables may be easier for scientists to understand and potentially more reliable when applied to related molecular systems.

XAI enables validation of AI predictions against domain expertise: By quantifying the importance of each input variable, the explainability framework allows chemists to verify whether the AI's conclusions align with established chemical knowledge, providing a mechanism to catch errors or biases in AI-generated insights.