Discovery of Novel Celecoxib Polymorphs Using AIMNet2 Machine Learning Interatomic Potential
Peikun Zheng, Yuriy Abramov, Changquan Calvin Sun, Olexandr Isayev
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Polymorphism plays a pivotal role in defining the solid-state properties of pharmaceutical compounds, yet the discovery and accurate energy ranking of polymorphs remain a challenge.
Abstract
Polymorphism plays a pivotal role in defining the solid-state properties of pharmaceutical compounds, yet the discovery and accurate energy ranking of polymorphs remain a challenge. Here, we leverage a fine-tuned machine-learned interatomic potential AIMNet2 to explore the polymorphic landscape of celecoxib, a clinically important COX-2 inhibitor. Our approach combines GPU-accelerated crystal structure generation, active learning-guided model refinement, and quasi-harmonic free-energy corrections. The workflow successfully reproduces the experimental energy hierarchy of known polymorphs and identifies several novel low-energy structures with distinct packing motifs. In addition, we evaluate the elastic properties and thermal expansion effects across polymorphs, revealing structural features that underpin mechanical flexibility and thermodynamic preferences. This study demonstrates the power of AIMNet2-based crystal structure prediction for resolving complex pharmaceutical polymorphism and offers a powerful tool for future polymorph discovery and solid-state optimization.
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Cite This Paper
@article{Zheng2025,
author = {Zheng, Peikun and Abramov, Yuriy and Sun, Changquan Calvin and Isayev, Olexandr},
title = {Discovery of Novel Celecoxib Polymorphs Using AIMNet2 Machine Learning Interatomic Potential},
year = {2025},
doi = {10.26434/chemrxiv-2025-nhmr1},
url = {http://dx.doi.org/10.26434/chemrxiv-2025-nhmr1},
publisher = {American Chemical Society (ACS)},
keywords = {machine learning, active learning},
researchAreas = {ml-potentials, experiment-automation, drug-discovery, materials-informatics},
highlight = {Polymorphism plays a pivotal role in defining the solid-state properties of pharmaceutical compounds, yet the discovery and accurate energy ranking of polymorphs remain a challenge.}
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