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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Improving protein-ligand complex generation with force field guidance

Alessandro Tibo, Brad Rees, Joe Eaton, Ola Engkvist et al.
Journal of Cheminformatics
Computational Drug Discovery Methods
article

Improving protein-ligand complex generation with force field guidance

Alessandro Tibo, Brad Rees, Joe Eaton, Ola Engkvist, Hassan Sirelkhatim, Tingyu Wang, Helen Lai, Xiaoyun Wang, Howard Huang, Jon Paul Janet
article en

Abstract

, 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.

Journal of CheminformaticsVol. 18(1)
AstraZeneca (United Kingdom) (GB), AstraZeneca (Brazil) (BR), Nvidia (United States) (US), AstraZeneca (Sweden) (SE), AstraZeneca (Germany) (DE), Chalmers University of Technology (SE)
Affordable and clean energy
Openalex Percentile: Top 8%
Computational Drug Discovery Methods
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.