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Life sciences · Preprint
arXiv · September 8, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is a preprint describing EF-TALFM, a novel two-stage generative framework for variable-size 3D molecules using fixed-dimensional latent representations and autoregressive decoding. The method shows improved performance on generative benchmarks (89.4% valid, unique, novel molecules) and property-guided hit rate (doubled DFT-verified hits at 0.1 eV tolerance) compared to published baselines, but has not undergone peer review and includes no experimental synthesis or validation.
Computational method development and benchmarking study. PCQM4Mv2 molecular dataset; exact training and test split sizes not stated.. Intervention: EF-TALFM: two-stage flow matching with autoregressive Transformer decoding for variable-size 3D molecule generation.. Compared with: UAE-3D and FlowMol baseline methods..
On PCQM4Mv2, EF-TALFM achieves 89.4% of molecules that are unique, training-set novel, pass sanitization and PoseBusters sanity checks, compared with 75.6% for UAE-3D and 69.8% for FlowMol. Across ten target HOMO–LUMO gaps, internal ranking doubles the DFT-verified hit rate within 0.1 eV while preserving 97% novelty among unique verified hits. EF-TALFM achieves higher measured computational throughput for training and sampling than comparison methods.
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This is an unreviewed computational method paper presenting a novel generative model architecture with validation on molecular benchmarks but no experimental synthesis or biological testing.
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In molecular discovery, molecule size is coupled to composition, structure, and other target properties. Yet most 3D generators require molecule size to be specified before generation. Here, we introduce Equivariant-Free Transformer-Autoencoded Latent Flow Matching, a two-stage generative framework that relies entirely on a single fixed-dimensional molecule-level latent representation to generate variable-size molecules. The second-stage flow matching model samples this latent vector, and an autoregressive Transformer decoder then determines molecule size while generating atom types, coordinates, and chemically informative states. Canonical atom ordering and rigid-pose alignment enable standard Transformers without equivariant layers, while joint decoding of molecular geometry and an enriched chemical state enables reliable, deterministic, chemistry-guided graph recovery without requiring a learned dense pairwise bond decoder. The same fixed-dimensional latent supports unconditional and property-conditioned flow matching, while optional property supervision adds an internal ranking readout, with no separate predictor or reference calculations. On PCQM4Mv2, EF-TALFM achieves the highest fraction of molecules that are unique, training-set novel, pass sanitization and PoseBusters sanity checks, 89.4\%, compared with 75.6\% for UAE-3D and 69.8\% for FlowMol. EF-TALFM also achieves higher measured computational throughput for training and sampling. Across ten target HOMO--LUMO gaps, internal ranking doubles the density functional theory (DFT)-verified hit rate within $0.1\,\mathrm{eV}$, while preserving 97\% novelty among unique verified hits. These results demonstrate that fixed-dimensional molecule-level generation followed by symmetry-resolved autoregressive realization provides a practical architecture for open-ended and property-directed 3D molecular design.
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