Improving protein-ligand complex generation with force field guidance
, and generates over 1000 ligands with better docking scores than native ligands. These results demonstrate that lightweight, physics-based guidance can significantly enhance generative drug design while preserving chemical validity and diversity. SCIENTIFIC CONTRIBUTION: We introduce a novel, training-free force field guidance framework that steers ligand generation using empirical molecular mechanics (e.g., MMFF94) during diffusion or flow-based sampling-without modifying or retraining the base generative model (e.g., EDM or Semflaflow by [24]). Our method operates as a plug-in during inference time, leveraging energy feedback to generate poses with lower strain and having better predicted interactions with the protein structure. Our main contributions are as follows: Energy-based guidance without retraining: Unlike methods that require gradients from neural affinity predictors (e.g., BADGER [26]), our approach injects classical force field feedback (MMFF94) directly during the posterior sampling step. Improved docking and strain metrics: In benchmarks against unconditional EDM and Semflaflow, our guided inference yields consistently better AutoDock Vina scores and lower ligand strain energy, even after optimizing the final structures using the same force field. Compatibility and flexibility: Because the guidance module is external, it can be applied broadly to multiple generative backbones-without retraining or architecture modifications, and can be applied to arbitrary differentiable potential energy functions. Theoretical guarantee of stability. We demonstrate in Appendix B that the gradient correction step corresponds to a descent step on the energy under standard smoothness assumptions. While the full sampling update also includes model-driven (and, in the diffusion case, stochastic) components, this result formalizes how the guidance term locally biases the trajectory toward lower-energy regions and provides a principled justification for its stabilizing effect.
Authors
- Alessandro Tibo (ORCID: https://orcid.org/0000-0002-9070-740X)
- Brad Rees
- Joe Eaton (ORCID: https://orcid.org/0000-0001-7540-995X)
- Ola Engkvist (ORCID: https://orcid.org/0000-0003-4970-6461)
- Hassan Sirelkhatim (ORCID: https://orcid.org/0009-0005-0944-3005)
- Tingyu Wang (ORCID: https://orcid.org/0000-0003-2520-0511)
- Helen Lai
- Xiaoyun Wang
- Howard Huang
- Jon Paul Janet
Institutions
- AstraZeneca (United Kingdom) (GB)
- AstraZeneca (Brazil) (BR)
- Nvidia (United States) (US)
- AstraZeneca (Sweden) (SE)
- AstraZeneca (Germany) (DE)
- Chalmers University of Technology (SE)
Publication Details
- Journal
- Journal of Cheminformatics
- Published
- 2026-05-02
- DOI
- https://doi.org/10.1186/s13321-026-01198-2
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00