GX-TGVAE: Constraint-Masked Graph Generation for Controllable Molecular Exploration

Distributional fidelity in molecular generation alone establishes neither molecule-specific information retention nor controllable out-of-distribution (OOD) exploration. GX-TGVAE connects these capabilities through continuous latent conditioning and constraint-masked atom- and bond-level graph construction. It learns deterministic canonical reconstruction and steers exploration using locally estimated tangent/normal geometry and property gradients. On MOSES, it achieves the highest exact reconstruction among the compared latent baselines and broader structural reach, although distributional fidelity and reconstruction rankings differ across architecture variants. On ZINC, property-guided normal exploration exceeds a random-normal control in Novel Top 5% docking score at the two deeper tested exploration depths. Against the OOD generation baseline MOOD, GX-TGVAE shows high conditional utility within training-similarity bands, but its base sampler lacks sampling mass for fixed-attempt discovery and deep-OOD reach. This distinguishes exploration breadth from reached-region utility. Harvest reallocates sampling budget to selected neighborhoods, improving this deficit and exceeding MOOD's hit yield under a matched evaluation-time budget, while MOOD retains higher upper-tail docking scores. Together, these results identify faithful representation, directional control, and sampling-mass allocation as distinct but complementary design axes for molecular OOD generation.