Development of a novel machine learning approach to optimize important parameters for improving the solubility of an anti-cancer drug within green chemistry solvent

Alanazi, Muteb; Huwaimel, Bader; Alanazi, Jowaher; Alharby, Tareq Nafea

doi:10.1016/j.csite.2023.103273

Cited by 3 publications

(1 citation statement)

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“…Cross-validation is also important here. In fluid mechanics applications, ETR has been employed to predict various fluid-related properties, such as molecular separation in membrane systems, Vibrio fischeri toxicities, properties of ionic liquid, ink droplet velocity profiles of printing cells, and drug particle solubility optimization [10,[35][36][37][38].…”

Section: The Extra Trees Regressormentioning

confidence: 99%

Twofold Machine-Learning and Molecular Dynamics: A Computational Framework

Stavrogiannis,

Sofos,

Sagri

et al. 2023

Computers

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Data science and machine learning (ML) techniques are employed to shed light into the molecular mechanisms that affect fluid-transport properties at the nanoscale. Viscosity and thermal conductivity values of four basic monoatomic elements, namely, argon, krypton, nitrogen, and oxygen, are gathered from experimental and simulation data in the literature and constitute a primary database for further investigation. The data refers to a wide pressure–temperature (P-T) phase space, covering fluid states from gas to liquid and supercritical. The database is enriched with new simulation data extracted from our equilibrium molecular dynamics (MD) simulations. A machine learning (ML) framework with ensemble, classical, kernel-based, and stacked algorithmic techniques is also constructed to function in parallel with the MD model, trained by existing data and predicting the values of new phase space points. In terms of algorithmic performance, it is shown that the stacked and tree-based ML models have given the most accurate results for all elements and can be excellent choices for small to medium-sized datasets. In such a way, a twofold computational scheme is constructed, functioning as a computationally inexpensive route that achieves high accuracy, aiming to replace costly experiments and simulations, when feasible.

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