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arXiv · 2511.02769

STAR-VAE: A Scalable Latent-Variable Transformer for Controllable Molecular Generation

Bum Chul Kwon·Ben Shapira·Moshiko Raboh·Shreyans Sethi·Shruti Murarka·Joseph A Morrone·Leili Zhang·Wendy Cornell·Jianying Hu·Parthasarathy Suryanarayanan
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Abstract

Many molecular Transformers lack probabilistic latent variables for posterior inference and latent interpolation. We introduce STAR-VAE, a SELFIES-encoded, Transformer-based, AutoRegressive Variational AutoEncoder combining a bidirectional encoder with an autoregressive decoder pretrained on 79 million PubChem molecules. A property signal jointly conditions the prior, posterior, and decoder, while LoRA adapters support fine-tuning on small datasets without modifying the backbone. STAR-VAE achieves 100% validity and near-perfect novelty under unconditional MOSES sampling, the lowest KL divergence on five of ten GuacaMol descriptors, Spearman \r{ho} = 0.62 at 98% validity for synthetic-accessibility conditioning, and directional docking-score control for three Tartarus protein targets. Across four ChEMBL targets, seed-based posterior sampling recovers target-associated held-out scaffolds while label-conditioned sampling produces structurally diverse outputs. Code is available at https://github.com/BiomedSciAI/STAR-VAE.

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Bum Chul Kwon, Ben Shapira, Moshiko Raboh, Shreyans Sethi, Shruti Murarka, Joseph A Morrone, Leili Zhang, Wendy Cornell, Jianying Hu, Parthasarathy Suryanarayanan. 2026-09-22. STAR-VAE: A Scalable Latent-Variable Transformer for Controllable Molecular Generation. https://arxiv.org/abs/2511.02769

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