arXiv:2603.23398v2 Announce Type: replace Abstract: Generative modeling of discrete data, such as graphs, underpins many scientific and industrial applications, including molecular discovery and materials design. In these domains, probabilistic inference is particularly valuable, as it enables composable generation and principled incorporation of desired constraints, such as structural or functional properties. Energy-based models naturally support this goal by capturing relative likelihoods and enabling composable inference by directly enforcing constraints during inference. However, discrete
The continuous advancements in AI research, particularly in generative models and probabilistic inference, enable more sophisticated approaches to discrete data generation. This aligns with the increasing demand for AI tools in discovery processes across various scientific fields.
This development allows for more controlled and interpretable generative AI, directly addressing critical needs in fields like molecular and materials science where specific structural or functional constraints are paramount. It offers a path to accelerate innovation in these domains by enabling targeted design.
The ability to enforce desired constraints directly during inference in generative models for discrete data fundamentally enhances the utility and reliability of AI in scientific discovery. It moves beyond purely black-box generation to more principled and purpose-driven AI design.
- · Pharmaceuticals
- · Biotechnology
- · Materials science
- · Computational chemists
- · Traditional drug discovery pipelines
- · Trial-and-error materials research
Accelerated discovery of novel molecules and materials with desired properties becomes more feasible.
Reduced R&D costs and shortened time-to-market for new products in health and industrial sectors.
Potential for entirely new classes of therapeutics or materials that were previously too complex to design manually.
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Read at arXiv cs.LG