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: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...
arXiv:2607. 12380v1 Announce Type: new Abstract: Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its own graph, equivariant, or frame-based architecture.
arXiv:2510. 27497v2 Announce Type: replace-cross Abstract: Transformer-based autoregressive models have emerged as a unifying paradigm across modalities such as text and images, but their extension to 3D molecule generation remains underexplored.
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:2606. 25006v1 Announce Type: new Abstract: Target-specific peptide design requires sequence and structure co-design under full atom geometric constraints.
arXiv:2608. 14076v1 Announce Type: cross Abstract: Transition-state (TS) structures define the energetic barriers and mechanistic pathways of elementary chemical reactions, yet their identification remains computationally demanding because conventional saddle-point searches require expensive quantum-mechanical calculations.
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:2610.02186v1 Announce Type: cross Abstract: Molecular learning models are strongly shaped by their underlying representations. Yet standard sequential and graph formalisms struggle to explicitl...
Molecular crystal structure prediction (CSP) is important in pharmaceuticals, agrochemicals, and organic electronics, where subtle differences in molecular conformation and packing can strongly affect...
Target-specific peptide design requires sequence and structure co-design under full atom geometric constraints. Latent generative frameworks offer an effective route for this problem by compressing fine grained atomic structures into block level latent representations and performing conditional generation in a compact latent space.
Zatom-2 is a multitask generative model for atomistic data that has been pretrained on about five million structures from the OMol25 and OMat24 datasets. It uses a multiscale Transformer with conditional flow matching to support tasks such as generation, structure prediction, and energy/force prediction for both molecules and materials. The model outperforms its predecessor, Zatom-1, on molecular distribution fidelity and benchmark generation tasks, and improves protein backbone designability from 67.8% to 74.8% after finetuning on 2,000 protein domains.
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.