arXiv:2607.01800v1 Announce Type: cross Abstract: Large Language Models (LLMs) have recently shown promise in molecular discovery, yet a gap remains between their probabilistic nature over discrete sequential tokens and the rigid topological constraints of chemical space. This raises the question of whether molecular LLMs can generalize beyond the local neighborhoods induced by their sequence-based representations. To systematically investigate this question, we introduce a Molecular Perturbation framework that generates syntax-valid structural variants of training molecules under controlled G
The proliferation of LLMs is extending into specialized scientific domains like molecular discovery, necessitating evaluation of their fundamental generalization capabilities beyond text.
Understanding the true generalization power of LLMs in molecular science is crucial for guiding research, identifying limitations, and ensuring reliable application in drug discovery and materials science.
The focus shifts from merely demonstrating LLM utility in molecular science to rigorously testing their understanding of underlying chemical principles versus superficial pattern matching.
- · AI researchers specializing in domain-specific generalization
- · Pharmaceutical companies leveraging advanced AI for discovery
- · Chemical engineering
- · Developers of 'black box' molecular LLMs
- · Research relying on unvalidated LLM generalization claims
This research will lead to improved architectures and training methodologies for molecular LLMs that can handle complex chemical constraints.
More robust and generalizable molecular LLMs could significantly accelerate drug discovery and materials science by reducing experimental cycles.
The enhanced capability for de novo molecular design could lead to entirely new classes of therapeutics or industrial materials with unprecedented properties.
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Read at arXiv cs.CL