ForceFM: Enhancing Protein-Ligand Predictions through Force-Guided Flow Matching
HUANLEI GUO, Song LIU, Bingyi Jing
Molecular docking is a fundamental technique in structure-based drug discovery, essential for predicting the binding modes of protein-ligand complexes. Traditional docking methods, while reliable, suffer from slow computational speeds and limited adaptability. Recent deep learning (DL) approaches have significantly improved docking efficiency and accuracy. However, these methods struggle with generating conformations that are both chemically consistent and physically plausible due to limited incorporation of physical priors. To overcome these challenges, we propose ForceFM, a novel force-guided model that integrates a force-guided network into the generation process, steering ligand poses toward low-energy, chemically realistic conformations. Force guidance also halves inference cost compared with the unguided approaches. Importantly, replacing the guiding potential with diverse energy functions—including Vina, Glide, Gnina, and Confscore—preserves or improves performance, underscoring the method’s generality and robustness. These results highlight ForceFM's ability to set new standards in docking accuracy and physical consistency, surpassing the limitations of previous methods.