Recently, Arattu Thodika et al. demonstrated a notable advance in using machine learning potentials (MLPs) to accelerate QM/MM simulations of hydride transfer reactions catalyzed by dihydrofolate reductase (DHFR). Their work explores the transferability of pre-trained MLPs across different enzyme variants, showing promising results for singlepoint mutations and homologous enzymes but revealing limitations when transitioning to aqueous environments. While their approach achieves computational efficiency compared to traditional QM/MM methods, the need for extensive training data and the challenges in transferability across substantially different environments highlight areas requiring further development.