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Related Concept Videos

Ligand Binding and Linkage00:49

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

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Related Experiment Video

Updated: Jul 8, 2026

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

FLOWR.ROOT - A flow matching-based foundation model for joint multi-purpose structure-aware 3D ligand generation and

Julian Cremer1, Tuan Le2, Mohammad M Ghahremanpour3

  • 1Machine Learning & Computational Sciences, Pfizer Worldwide R&D, Berlin, Germany. jn.cremer@icloud.com.

Nature Communications
|July 6, 2026
PubMed
Summary

FLOWR.ROOT is a new AI model for drug discovery. It generates 3D molecules and predicts their binding affinity, streamlining the design of new medicines.

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Area of Science:

  • Computational chemistry
  • Artificial intelligence in drug discovery
  • Molecular modeling

Background:

  • De novo drug design requires accurate prediction of ligand-target interactions.
  • Existing methods often lack the ability to unify ligand generation and binding affinity prediction.
  • The development of foundation models offers a promising avenue for integrated molecular design.

Purpose of the Study:

  • To introduce FLOWR.ROOT, a unified SE(3)-equivariant flow-matching foundation model.
  • To enable pocket-aware 3D ligand generation and multi-endpoint binding affinity prediction.
  • To provide pLDDT-based confidence estimation within a single framework.

Main Methods:

  • Utilizing SE(3)-equivariant flow matching for molecular generation.
  • Implementing a mixed isotropic-anisotropic prior placement strategy for diverse sampling.
  • Employing a three-stage training process: large-scale pre-training, refinement on co-crystal data, and LoRA finetuning.
  • Integrating joint structure-affinity modeling for inference-time guidance.

Main Results:

  • FLOWR.ROOT supports various generative tasks: pocket-conditional generation, interaction- and pharmacophore-conditional sampling, scaffold hopping, fragment growing/replacement.
  • The model achieves accurate binding affinity prediction (pIC50, pKi, pKd, pEC50) and confidence estimation (pLDDT).
  • Joint structure-affinity modeling allows for inference-time importance-sampling guidance without external scoring functions.

Conclusions:

  • FLOWR.ROOT successfully unifies 3D ligand generation and binding affinity prediction in a single foundation model.
  • The model demonstrates versatility across multiple drug design tasks, from hit identification to lead optimization.
  • Case studies on kinase selectivity and scaffold elaboration highlight the practical utility of FLOWR.ROOT in accelerating drug discovery pipelines.