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.
arXiv:2609.08333v1 Announce Type: cross Abstract: In molecular discovery, molecule size is coupled to composition, structure, and other target properties. Yet most 3D generators require molecule size...
The paper introduces Equivariant-Free Transformer-Autoencoded Latent Flow Matching (EF‑TALFM), a two‑stage generative framework that uses a single fixed‑dimensional latent vector to produce variable‑size 3D molecules. The first stage samples the latent vector via flow matching, and the second stage employs an autoregressive Transformer decoder that determines molecule size while generating atom types, coordinates, and chemical states. EF‑TALFM outperforms prior methods on the PCQM4Mv2 benchmark, achieving higher uniqueness, novelty, and computational throughput, and its internal ranking improves the hit rate for target HOMO–LUMO gaps while maintaining novelty.
arXiv:2607. 09277v1 Announce Type: new Abstract: Three-dimensional (3D) molecule generation has been dominated by diffusion models, which achieve strong generation quality but typically require the molecular size to be specified a priori.
arXiv:2607. 20551v1 Announce Type: cross Abstract: Effective molecular representation learning is crucial for accurate molecular property prediction.
arXiv:2607. 18144v1 Announce Type: cross Abstract: Structure-based drug design (SBDD) leverages the 3D structure of protein targets, often complemented by other spatial constraints, to generate candidate binding molecules.
arXiv:2606. 23856v1 Announce Type: new Abstract: Generative molecular models for drug design are a promising direction with much active research.
arXiv:2609.16527v1 Announce Type: cross Abstract: Exploring the chemical space of flexible molecules remains challenging because the vast number of possible compounds and conformations, together with...
arXiv:2508.14748v2 Announce Type: replace-cross Abstract: The increasing variety of molecular data creates a need for generative models that can flexibly incorporate heterogeneous constraints across...
arXiv:2606. 13955v1 Announce Type: new Abstract: Latent diffusion is a promising framework for scalable 3D molecular generation, but it requires a latent space that remains smooth, valid, and navigable beyond posterior samples.
arXiv:2607. 05736v1 Announce Type: new Abstract: Molecular property prediction often relies on isolated data modalities, where continuous 3D graph neural networks (GNNs) struggle to efficiently capture long-range topological dependencies and exact macroscopic heuristics.
Exploring the chemical space of flexible molecules remains challenging because the vast number of possible compounds and conformations, together with the increasing cost and limited generalization of...