Identification of Potential DCAF1 Inhibitors Through Virtual Screening Based on Molecular Docking and Molecular Dynamics Simulations

Abstract DCAF1 (DDB1‐ and CUL4‐associated factor 1) is an emerging anticancer target; however, few inhibitors have been identified so far. In this study, molecular docking of 24 known inhibitors against DCAF1 structures revealed a significant correlation between Glide SP docking scores and experimental Log10KD values ( R 2 = 0.723), thereby validating the reliability of this methodology. A virtual screening of 1.4 million ChemDiv compounds against the DCAF1 protein structure using this validated model led to the identification of 52 potential hit compounds after applying several stringent filters, including Glide docking, MM/GBSA scoring, REOS rules, and cluster analysis. Among these hits, six compounds with particularly favorable binding energies were subjected to further in‐depth analysis using 500 ns molecular dynamics simulations. The results from these simulations demonstrated that these compounds can bind to DCAF1 with high affinity, potentially impairing its biological function. Overall, this computational workflow successfully identified novel DCAF1 inhibitor candidates, which are promising leads for further optimization and experimental validation. This study represents an advancement in the search for effective inhibitors of this important yet underexplored anticancer target.