FLOWR.root

FLOWR.root is a foundation model for structure-based drug design that unifies two tasks usually handled by separate systems: generating three-dimensional small molecules inside a protein binding pocket, and predicting how tightly the generated ligands will bind. Most generative models for pocket-aware ligand design produce geometries but leave scoring to external tools, while affinity predictors do not generate molecules. FLOWR.root couples both in a single equivariant network, and adds a calibrated confidence estimate for its predictions.

The model is an SE(3)-equivariant flow-matching architecture with three output heads — one for molecular structure, one for multi-endpoint affinity, and one for confidence. Flow matching provides a fast, simulation-free generative process that respects the rotational and translational symmetries of molecules in 3D space. FLOWR.root supports de novo generation, pocket-conditioned sampling, and fragment-level manipulation such as scaffold hopping, elaboration, and fragment- and core-growing, and it predicts several potency endpoints (pIC50, pKi, pKd, pEC50). It was developed primarily by the Machine Learning & Computational Sciences group at Pfizer Worldwide R&D, with collaborators at Jagiellonian University Medical College, Helmholtz Munich, and the Technical University of Munich, and posted as a preprint in October 2025.

By joining generation, affinity prediction, and confidence in one pocket-aware model, FLOWR.root aims to make in-silico ligand design and its downstream evaluation a single coherent workflow.

#Key Features

• Joint generation and affinity prediction: A single model produces 3D ligands and predicts multiple potency endpoints (pIC50, pKi, pKd, pEC50) with an accompanying confidence estimate.
• SE(3)-equivariant flow matching: The generative backbone respects 3D rotational and translational symmetry and samples molecules through fast, simulation-free flow-matching dynamics.
• Flexible conditional modes: Supports de novo design, pocket-conditioned sampling, scaffold hopping, elaboration, fragment-growing, core-growing, and interaction-constrained generation.
• Efficient adaptation and inference-time scaling: Offers parameter-efficient LoRA fine-tuning for domain adaptation and importance-sampling-based inference-time scaling to improve outputs.
• Open code and weights: The repository ships pretrained weights, a command-line workflow, and a FLOWR.ui web interface under an MIT license.

#Technical Details

FLOWR.root is an SE(3)-equivariant flow-matching model with roughly 33 million trainable parameters, structured as a shared backbone feeding structure, multi-affinity, and confidence heads. It was trained on large-scale ligand libraries together with mixed-fidelity protein-ligand complexes, then refined on curated co-crystal data. On the HiQBind affinity benchmark it reaches Pearson correlations of 0.92 for pIC50, 0.76 for pKi, and 0.57 for pKd; pocket-conditional generation achieves about 0.97 mean PoseBusters validity on CrossDocked2020 and SPINDR. On the FEP+/OpenFE benchmark it reports RMSE of 0.93 kcal/mol with Pearson 0.86, outperforming Boltz-2 and approaching FEP+. Inference of 100 ligands takes roughly 15 seconds on an H100 GPU. The work is a preprint awaiting peer review, and the authors describe the released checkpoint as an early release ahead of a fully converged model.

#Applications

FLOWR.root is aimed at computational chemists and drug-discovery teams doing structure-based design: proposing novel binders for a target pocket, growing or hopping scaffolds around known fragments, and prioritizing candidates by predicted potency and confidence in a single pass. Its LoRA fine-tuning path lets teams adapt the model to proprietary targets or chemistries, and the bundled web UI lowers the barrier for non-programmers to run interactive generation.

#Impact

By integrating 3D generation, multi-endpoint affinity prediction, and confidence estimation into one compact equivariant model — and reporting competitive results against physics-based free-energy methods and Boltz-2 — FLOWR.root points toward tighter, self-contained generative design loops for medicinal chemistry. Open weights, code, and a web interface under a permissive license lower the barrier to adoption, though as an early-release preprint its real-world validation is still ongoing.