Chemical properties are linear steerable directions in a molecular VAE

measured in 1 paper

Elabid et al. train a custom Transformer-VAE (latent dim 256) from scratch on 794,403 RDKit-valid SELFIES molecules [elabid-etal-2026-molecules-meet-language] Linear probes on frozen latent codes recover chemical properties (cLogP, FractionCSP3, TPSA, HBA, BertzCT) [elabid-etal-2026-molecules-meet-language] The probe directions are approximately linear and globally steerable, robust to confound-residualized R^2 ruling out sequence-length artifacts [elabid-etal-2026-molecules-meet-language] Latent traversal along a probe direction produces monotonic, chemically coherent property changes, enabling controllable generation [elabid-etal-2026-molecules-meet-language]

Structure

Linear Direction

Context

molecular representation learning, chemical property steering, VAE latent space, confound control, controllable generation

Confirmed in models

Custom Transformer-VAE, Autoregressive MultiSlotting variant (trained from scratch on SELFIES-tokenized molecules) · Custom Molecular Transformer-VAE (purpose-built for SELFIES generation)

Method

Linear probing, Activation Steering (Addition)

Papers

Molecules Meet Language: Confound-Aware Representation Learning and Chemical Property Steering in Transformer-VAE Latent Spaces — Elabid, Zakaria, Brzoza, Bartosz, Andrzejewski, Jan, Cangi, Attila2026 · arXiv:2605.06303