Ensemble-Conditioned Molecular Design
cs.LG, cs.NE
Submitted: 2026-09-14
Updated: 2026-09-14
Comments: Code available at: https://github.com/rssrwn/ensemble-cond-design datasets and checkpoints available at: https://zenodo.org/records/22485204
Code: https://github.com/rssrwn/ensemble-cond-design
License: http://creativecommons.org/licenses/by/4.0/
The gist: Molecular design is typically approached as a problem of finding molecules which can adopt a single bioactive conformation.
Terminology
Abstract
Molecular design is typically approached as a problem of finding molecules which can adopt a single bioactive conformation. In reality, molecules occupy a distribution over conformations, and many of the properties which determine whether a candidate is viable depend on that distribution rather than on any single conformer. We reframe molecular design as an optimisation of both the modes and properties of molecules' conformational ensembles, where modes can be represented as shapes, pharmacophore profiles or protein pockets, and properties are aggregate scalars computed over the whole distribution. To realise this we introduce ensemble-conditioned guidance, a framework which conditions 3D molecular generative models on both axes simultaneously. Mode conditions are composed adaptively at inference by combining the vector fields produced under each condition. Conditions may be targeted or avoided, mixed across modalities and combined in arbitrary numbers, allowing a wide range of design tasks to be expressed with a single trained model. We introduce adaptive symmetry learning to allow conditions from different reference frames to be composed, and extend our generative framework to enable flexible-size generation. We evaluate on new benchmarks for multi-mode conditioning and ensemble property optimisation, and apply the framework to two practical drug discovery tasks, dual-target binder design and active-state-selective agonist design, where in both cases conditioning on the additional state improves the desired outcome over single-state conditioning.
Sources
- FLOWR: Flow Matching for Structure-Aware De Novo, Interaction- and Fragment-Based Ligand Generation
- Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models
- FuseDiff: Symmetry-Preserving Joint Diffusion for Dual-Target Structure-Based Drug Design
- Boltzmann-Expected Molecular Design with Decoupled Annealing Flows
- Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport
- Feynman-Kac-Flow: Inference Steering of Conditional Flow Matching to an Energy-Tilted Posterior
- Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models?
- Benchmarking structure-based three-dimensional molecular generative models using GenBench3D: ligand conformation quality matters
- AlloGen: Conformation-Selective Binder Generation with Differential State Scoring
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