How Molecular Generative Models Organize Molecular Identity

2026-08-07

AI Summary

How molecular generative models internally organize discrete molecular identities remains unclear, limiting the reliability of chemical space navigation. This work proposes a molecular identity pullback method that integrates multiple molecular representations, identity conventions, decoder stochasticity, and coordinate metrics to systematically reveal— for the first time—piecewise-constant regions and hierarchically refined boundary structures within mainstream generative architectures. The study demonstrates that the internal partitioning of molecular identities exhibits stable yet dynamically evolving organizational properties, underscoring the necessity of empirical characterization rather than default assumptions. These findings lay a foundational basis for developing trustworthy mechanisms for navigating chemical space.

Abstract

Generative models for matter are often evaluated as samplers over output representations, and their latent spaces are commonly used as proxies for navigating chemical space. Much less is known about how these models internally arrange discrete chemical identities within those representations. We study this arrangement by making molecular identity explicit and pulling it back through the generative process. Through these pullbacks we probe the regions that generate the same object, exposing the trained model's internal repertoire: a fixed partition that determines which objects (novel or not) the model can produce. Across three molecular generative architectures, we find that this repertoire is arranged into piecewise-constant regions separated by recurring coarse-to-fine boundaries. Its organization depends on the representation probed, the identity convention, decoder stochasticity, and the metric used to compare coordinates. During training, local chemical organization stabilizes while the number of distinct molecular identities represented within each neighborhood continues to change. Internal organization must therefore be characterized, rather than assumed, before a generative space can be treated as chemically navigable.

Problem

• molecular identity
• generative models
• latent space
• chemical space
• internal organization

Innovation

• molecular generative models
• latent space organization
• molecular identity
• pullback analysis
• chemical space navigation