How good are AlphaFold models for docking-based virtual screening?

A crucial component in structure-based drug discovery is the availability of high -quality three-dimensional structures of the protein target. Whenever experi-mental structures were not available, homology modeling has been, so far, the method of choice. Recently, AlphaFold (AF), an artificial-intelligence-based pro-tein structure prediction method, has shown impressive results in terms of model accuracy. This outstanding success prompted us to evaluate how accurate AF models are from the perspective of docking-based drug discovery. We compared the high-throughput docking (HTD) performance of AF models with their corre-sponding experimental PDB structures using a benchmark set of 22 targets. The AF models showed consistently worse performance using four docking pro-grams and two consensus techniques. Although AlphaFold shows a remarkable ability to predict protein architecture, this might not be enough to guarantee that AF models can be reliably used for HTD, and post-modeling refinement stra-tegies might be key to increase the chances of success.