arXiv:2512.22597v2 Announce Type: replace Abstract: Exploring molecular energy landscapes and identifying ground-state conformations are central challenges in computational chemistry. However, generating diverse low-energy conformers from molecular graphs remains expensive with traditional physics-based pipelines. Existing learning-based approaches remain fragmented: generative models capture conformational diversity but often lack reliable energy calibration, whereas deterministic predictors focus on a single structure and fail to represent ensemble variability. Here we introduce EnFlow, to o
The convergence of advanced generative AI models and increasing computational power is enabling more sophisticated approaches to chemistry and material science.
This development can significantly accelerate molecular discovery for therapeutics, materials, and other applications by reducing the computational cost and improving efficiency.
The process of identifying stable, low-energy molecular conformations shifts from expensive, traditional physics-based methods to more efficient, AI-driven generative models.
- · Pharmaceuticals
- · Material Science
- · AI/ML Research Institutions
- · Computational Chemistry
- · Traditional physics-based simulation software developers (if they fail to adapt)
Faster discovery of new drugs and materials with desired properties.
Reduced R&D costs and shortened time-to-market for novel chemical entities.
Potential for designing entirely new classes of molecules with unprecedented functions, accelerating advancements in fields like synthetic biology and clean energy.
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