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Ensemble-Aware Structural Encoding (EASE) is a new framework that conditions protein encoders on peptide conformational ensembles rather than single sequences or structures. By selecting central conformers that capture a peptide’s major structural states and fusing their representations with a permutation‑invariant operator, EASE produces a peptide‑level representation that better reflects conformational variability. In evaluations on six peptide property‑prediction tasks, EASE consistently outperforms single‑structure baselines, achieving an average absolute gain of about 3.0% and improving further as more conformers are incorporated.
arXiv:2607. 21561v1 Announce Type: new Abstract: Molecular property prediction from structure often uses a single representative conformation, even though many molecules exist as conformational ensembles in solution.
arXiv:2607.21561v2 Announce Type: replace Abstract: Molecular graph encoding often relies on a single, static structure, ignoring the thermodynamic ensemble of molecules that are present in solution....
arXiv:2606. 05474v1 Announce Type: cross Abstract: Protein binder design has largely optimized for affinity alone, leaving conformational selectivity unaddressed: for allosteric targets such as kinases, nuclear receptors, and GPCRs, a binder that engages both active and inactive states provides no functional specificity regardless of how tightly it binds.
arXiv:2606. 14737v1 Announce Type: cross Abstract: Molecular dynamics (MD) simulations generate trajectories in a high-dimensional configuration space whose analysis critically depends on molecular descriptors, typically handcrafted observables or learned kinetic embeddings.
The paper proposes a new framework called ensemble-conditioned guidance that reframes molecular design as an optimisation over both the modes and properties of a molecule’s conformational ensemble. It allows 3D generative models to be conditioned simultaneously on multiple axes—such as shapes, pharmacophore profiles, or protein pockets—by adaptively combining vector fields from each condition. The authors introduce adaptive symmetry learning for composable conditions across reference frames, extend the framework to support flexible-size generation, and demonstrate its effectiveness on new benchmarks and practical drug‑discovery tasks, showing improved outcomes when conditioning on additional states compared to single‑state approaches.