Author

Saeed Shirazian

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Open access Sep 2026

Leakage-aware machine learning for data-driven performance prediction of metal–organic framework systems

Metal–organic frameworks (MOFs) are highly tunable porous materials whose performance is governed by complex interactions among structural, chemical, material, and operating variables. This study develops a leakage-aware, data-driven framework for predicting two distinct MOF performance endpoints: loading capacity an...

N. Abu-Hamdeh, M. Ajour, A. Milyani et al. · Frontiers in Medicine · 0 citations

Open access Sep 2026

Artificial intelligence modeling of small molecule pharmaceutical solubility in supercritical fluid and comparative analysis under temperature and pressure variations

Enhancing the dissolution behavior of poorly water-soluble pharmaceuticals remains an important challenge in drug formulation and manufacturing, since limited aqueous solubility can substantially restrict therapeutic performance. Supercritical-fluid technologies provide a promising route for addressing this limitation,...

Hao-Lin Wu, Hadil Faris Alotaibi, A. S. Fahem et al. · Frontiers in Chemistry · 0 citations

Open access Aug 2026

Drugs solubility parameter prediction using modified Cubic Plus Chain equation of state.

The proposed CPC-TST model demonstrated good accuracy in predicting solubility parameter, particularly for strongly associating systems, while maintaining low computational complexity, and highlights the potential of the CPC-TST model as a robust and efficient alternative for modeling solubility behavior in pharmaceuti...

A. S. Alqahtani, Saeed Shirazian · European Journal of Pharmace... · 0 citations