The protein folding problem (PFP) has puzzled researchers for over fifty years, with its solution promising significant scientific advances, given the critical roles proteins play in biological functions. DeepMind’s AlphaFold2 (AF2) has dramatically advanced the field by accurately predicting 215 million protein structures from diverse organisms – far surpassing previous methods. In the context of AF2 winning the CASP14 protein structure prediction competition and its developers winning the Nobel Prize, many headlines have claimed that AF2 has solved the PFP. I propose to adopt a more nuanced view of AF2, taking into account the various objectives associated with the PFP and the objects of understanding its solution involves. I will argue that, despite its empirical success, AF2’s complexity and opacity limit its capacity to contribute directly to the scientific explanation of the PFP and, consequently, to its scientific understanding. I will present four conditions for scientific understanding mediated by a method: information integration, abilities, the generation of potential explanations and the provision of actual explanations. Based on those, I will show in a review of scientific articles that, despite its opacity, the use of AF2 can, and already has, enhanced objectual and ultimately explanatory understanding of certain research questions in protein biology, even if its inner workings remain mysterious. To make this claim, the interplay between scientists and the predictions plays a significant role, which indicates a new dynamic in scientific understanding, in which explanatory understanding is gained in a two-step adaptive process.
Annika Schuster (Wed,) studied this question.