  Molecular simulations are essential tools in computational chemistry,
enabling the prediction and understanding of molecular interactions and
thermodynamic properties of biomolecules. However, traditional force fields
face significant challenges in accurately representing novel molecules and
complex chemical environments due to the labor-intensive process of manually
setting optimization parameters and the high computational cost of quantum
mechanical calculations. To overcome these difficulties, we fine-tuned a
high-accuracy DPA-2 pre-trained model and applied it to optimize force field
parameters on-the-fly, significantly reducing computational costs. Our method
combines this fine-tuned DPA-2 model with a node-embedding-based similarity
metric, allowing seamless augmentation to new chemical species without manual
intervention. We applied this process to the TYK2 inhibitor and PTP1B systems
and demonstrated its effectiveness through the improvement of free energy
perturbation calculation results. This advancement contributes valuable
insights and tools for the computational chemistry community.
