The paper introduces two closed‑form projection operators that enforce physical validity in biomolecular diffusion models during inference. One operator resolves inter‑chain van der Waals clashes, while the other restores ligand geometry, including bond lengths, angles, planarity, and chirality. These lightweight, model‑agnostic corrections preserve structural accuracy and ligand placement across multiple benchmarks without adding runtime or memory overhead.
arXiv:2606. 08375v1 Announce Type: new Abstract: All-atom generative modeling of 3D biomolecular complexes has emerged as the dominant paradigm for predicting the structure of proteins and protein-ligand systems.
The paper introduces MCTH (Monte Carlo Tree Hallucination), an inference-only framework that performs all‑atom biomolecular sequence‑structure co‑design by treating pretrained folding and inverse‑folding models as black‑box operators. MCTH uses Monte Carlo Tree Search to allocate a fixed inference budget across competing design trajectories, incorporating model confidence, uncertainty, and cross‑expert consensus. Experiments across protein‑RNA, protein‑DNA, protein‑protein, and protein‑ligand design show that adaptive search outperforms simpler sampling strategies, and evaluations with AlphaFold3 and Chai‑1 demonstrate transferability beyond the search‑time oracle.
Budgeted Quotient-Residual Guidance (QRG) is an inference-time correction that enables quotient-based lead‑optimization objectives—such as distances, contacts, and anchored substructures—to be actively used with frozen pocket‑conditioned molecular diffusion models without retraining. QRG lifts quotient covectors to metric‑horizontal ambient directions and delivers them through a trust budget set by the sampler’s step norm, providing a closed‑form sampler‑budget update and a product‑budget split for section and residual controls. Experiments on frozen TargetDiff backbones show that QRG improves ligand‑generation quality on fragment growing, scaffold hopping, and linker design while preserving novelty and diversity.
arXiv:2607. 28553v1 Announce Type: new Abstract: Predicting the 3D structures of atomic systems is fundamental to advancing material science and drug discovery.
arXiv:2608. 11475v1 Announce Type: cross Abstract: AlphaFold3-class structure predictors pair a representational trunk, which processes sequence and context, with a diffusion module, which generates atomic coordinates.
arXiv:2607. 03513v1 Announce Type: cross Abstract: We present AquaGen, the first all-atom, explicit solvent, periodic-boundary-condition-aware generative model that produces molecular configurations from the Boltzmann distribution at a fraction of the cost of molecular dynamics (MD).
arXiv:2602. 24007v3 Announce Type: replace-cross Abstract: Protein function relies on dynamic conformational ensembles, yet current generative models like AlphaFold3 often fail to produce ensembles that match experimental data.
arXiv:2508. 02641v2 Announce Type: replace-cross Abstract: Molecular crystal structure prediction (CSP) is essential for applications in pharmaceuticals and organic electronics.
arXiv:2606. 03199v1 Announce Type: new Abstract: Organic crystal structure prediction (CSP) is a requirement for computational modelling of organic solids, but traditionally costs several CPU-years per molecule.
arXiv:2606. 19377v1 Announce Type: cross Abstract: Computational enzyme design requires generating proteins that scaffold catalytic residues and ligands, a task that demands both geometric accuracy and structural diversity from the underlying generative model.
arXiv:2606. 13477v1 Announce Type: cross Abstract: Supramolecular chemistry, which includes the study of non-covalent host-guest assemblies, has advanced various applications.
arXiv:2607. 03787v1 Announce Type: new Abstract: Accurately modeling biomolecular interactions is a central bottleneck in biology and therapeutic discovery.