FKSFold: Improving AlphaFold3-Type Predictions of Molecular Glue–Induced Ternary Complexes with Feynman–Kac–Steered Diffusion

We introduce an AI model, FKSFold, that uses Feynman–Kac steered diffusion as an inference-time strategy to improve AlphaFold3-type predictions of molecular-glue induced ternary complexes. FKSFold augments reverse diffusion with a Feynman–Kac derived steering term and uses the interface predicted TM-score (ipTM) as the guiding potential, coupled with particle-based sampling and adaptive resampling to bias trajectories toward high-quality interfaces without retraining the base models. Implemented on Chai-1r and Boltz-2 (named FKSFold-Chai and FKSFold-Boltz) – two open-source AF3-type architectures – FKSFold was benchmarked on eight ternary systems. FKSFold-Chai recovered three challenging complexes with sub-3 Å accuracy: VHL:MG:CDO1 (DockQ 0.922, iRMSD 0.629, fnat 0.963), FKBP12:MG:mTOR-FRB (DockQ 0.590, iRMSD 1.813, fnat 0.519), and FKBP12:MG:BRD9 (DockQ 0.841, iRMSD 0.801, fnat 0.913), while unmodified baselines failed. Other cases highlighted challenges – flexible loop rearrangements (e.g., CRBN:MG:VAV1-SH3c) and large conformational search spaces (e.g., NEK7, QDPR, HDAC1) – and, empirically, using few particles, short resampling intervals, and moderate-to-high lambda (steering strength) gave the best exploration–exploitation balance. Although the strategy is theoretically model-agnostic, on Boltz-2 ipTM head instability currently limits observed gains. The code for the two implementations, FKSFold-Chai and FKSFold-Boltz, is available at https://github.com/YDS-Pharmatech/FKSFold-Chai and https://github.com/YDS-Pharmatech/FKSFold-Boltz .