Year: 2026
Type: preprint
Abstract: Molecular size is intrinsically linked to composition, structure, and properties, but most 3D molecular generators treat size as a predefined input rather than a quantity to be generated. We introduce Equivariant-Free Transformer-Autoencoded Latent Flow Matching, a two-stage framework that samples a fixed-dimensional latent vector using flow matching and employs an autoregressive Transformer to determine molecular size, atom types, coordinates, and chemical attributes. Canonical atom ordering and rigid-pose alignment enable Transformers without equivariant layers, while decoded attributes guide bond reconstruction. On PCQM4Mv2, unconditional generation yields 87.9\% unique and novel molecules passing sanitization and PoseBusters checks, exceeding baselines with lower end-to-end training and sampling time and higher end-to-end throughput. Across ten target HOMO-LUMO gaps, internal ranking retains 30\% of the screened candidates and increases the density functional theory-verified hit rate within 0.1 eV of the target from 25.0\% to 52.4\%, while largely preserving novelty and diversity. These results demonstrate fixed-dimensional latent generation with autoregressive decoding as a practical approach to molecular design without prespecifying size.
Source: arXiv (Cornell University)
Institution: University of Michigan
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
Awards: Toward Automated Uncertainty Quantification in Causal Inference, ACED: Tail-aware Generative Modeling for Inverse Discovery of Molecules, Collaborative Research: ATD: Hawkes Process-Based Causal Relationship Discovery For Complex Threat Detection and Forecasting, CAREER: Bringing Structure to the Unstructured: Robust Causal and Statistical Modeling of High-dimensional Unstructured Data, G8101864701, G8876996369
OpenAlex ID: W7212001875