Explaining Protein Folding Networks Using Integrated Gradients and Attention Mechanisms

Protein folding prediction models like AlphaFold and ColabFold have revolutionized structural biology by providing accurate protein structures. However, these models present challenges when it comes to understanding how they arrive at their decisions. In this paper, we propose the application of Explainable AI (XAI) techniques, specifically Integrated Gradients and Attention Mechanisms, to elucidate the decision-making process of these complex networks. We conduct computational experiments to evaluate the effectiveness of these methods and discuss potential implications for the field.