Abstract:Estimating free energy differences quantifies thermodynamic preferences in molecular interactions, which is central to chemistry and drug discovery. Despite fruitful progress, existing methods still face key limitations: classical computational approaches remain prohibitively expensive due to their reliance on extensive molecular dynamics simulations, while deep learning-based methods are constrained by either less-expressive generative models or input dimensions tied to a specific system, resulting in negligible generalization. To address these challenges, we propose CARD, a generative framework that employs a novel radix-based decomposition to bijectively convert 3D coordinates into mixed discrete-continuous sequences, enabling coarse-to-fine autoregressive modeling with enhanced expressiveness. Notably, the model corresponds to a distribution with zero free energy, serving as a proposal for absolute free energy computation of arbitrary systems without relying on alchemical pathways. Experiments across diverse tasks demonstrate that CARD matches the accuracy of classical computational methods on unseen systems with diverse topologies, while achieving an approximately 40-fold speedup in inference.
| Comments: | ICML 2026 poster |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.02657 [cs.LG] |
| (or arXiv:2605.02657v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.02657 arXiv-issued DOI via DataCite (pending registration) |
From: Ziyang Yu [view email]
[v1]
Mon, 4 May 2026 14:38:41 UTC (2,486 KB)
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