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.
arXiv:2411. 06608v3 Announce Type: replace Abstract: We introduce MolMiner, a fragment-based, geometry-aware, and order-agnostic autoregressive model for molecular design.
PocketVE is a protein-pocket-conditioned variance‑exploding diffusion framework that integrates stable 3D coordinate denoising, classifier‑free property guidance, and adaptive protein perturbation. It improves 3D validity from 58.6% to 80.6% and reduces strain energy from 457.4 to 127.9 on CrossDocked2020 while maintaining competitive docking and property scores. The study shows moderate guidance balances target objectives with geometric quality, and diagnostics confirm enhanced pocket compatibility.
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:2606. 23856v1 Announce Type: new Abstract: Generative molecular models for drug design are a promising direction with much active research.
arXiv:2609.00189v1 Announce Type: new Abstract: Goal-directed optimization is essential for steering molecular generators to propose candidates with desired properties. However, it is often implement...
arXiv:2607. 19519v1 Announce Type: cross Abstract: Most 3D properties relevant to molecular design, including free energies and shape descriptors, are $\textit{expectations}$ over the Boltzmann distribution over 3D configurations of a molecular graph.
NEAT-POCKET is a pocket‑conditioned extension of the autoregressive NEAT model that generates 3D molecules atom by atom within protein binding pockets, maintaining atom permutation invariance and explicitly modeling hydrogen atoms. It outperforms existing baselines on the CrossDocked and SPINDR datasets, achieving competitive structure‑based generation performance while sampling significantly faster. The model also supports pocket‑conditioned fragment completion, a capability directly useful for lead optimization and scaffold elaboration in drug design.
arXiv:2606. 01220v1 Announce Type: cross Abstract: Generating molecules that simultaneously satisfy drug-like properties and conform to the 3D structure of a target protein is a core challenge in structure-based drug design (SBDD).
FuseDiff is an end‑to‑end diffusion model designed for dual‑target structure‑based drug design, jointly generating a ligand graph and two pocket‑specific binding poses conditioned on both target pockets. It employs a message‑passing backbone with Dual‑target Local Context Fusion (DLCF) to fuse ligand atom contexts from both pockets, preserving symmetry while enabling expressive joint modeling. The model enforces topological consistency across the two poses and allows target‑specific geometric adaptation, achieving state‑of‑the‑art docking performance and enabling systematic assessment of dual‑target pose quality before docking‑based pose search.
arXiv:2607. 11978v1 Announce Type: cross Abstract: Precision molecular design aims to discover personalized drug candidates through joint control of multiple conditions, such as biological relevance and molecular design strategies.
arXiv:2606. 01628v1 Announce Type: cross Abstract: Biomolecules such as proteins and small-molecule ligands play a central role in biological systems, arising from the tight interplay between sequence and three-dimensional structure.
arXiv:2606. 08802v1 Announce Type: new Abstract: Standard flow and diffusion pre-training matches the distribution of available data (e.