Membrane Science Meets Machine Learning: Future and Potential Use in Assisting Membrane Material Design and Fabrication

Talukder, Musabbir; Alshami, Ali; Tayyebi, Arash; Ismail, Nadhem; Yu, Xue

doi:10.1080/15422119.2023.2212295

Cited by 4 publications

(1 citation statement)

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“…Compared to molecular descriptors, fingerprinting methods provide a more dynamic representation that encompasses the characteristics of materials through their fragment features [ 25 ]. There are various types of molecular fingerprints, which are determined by the method used to convert the molecular fragment into a binary string [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

Prediction of organic compound aqueous solubility using machine learning: a comparison study of descriptor-based and fingerprints-based models

Tayyebi,

Alshami,

Rabiei

et al. 2023

J Cheminform

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A reliable and practical determination of a chemical species’ solubility in water continues to be examined using empirical observations and exhaustive experimental studies alone. Predictions of chemical solubility in water using data-driven algorithms can allow us to create a rationally designed, efficient, and cost-effective tool for next-generation materials and chemical formulations. We present results from two machine learning (ML) modeling studies to adequately predict various species’ solubility using data for over 8400 compounds. Molecular-descriptors, the most used method in previous studies, and Morgan fingerprint, a circular-based hash of the molecules' structures, were applied to produce water solubility estimates. We trained all models on 80% of the total datasets using the Random Forest (RFs) technique as the regressor and tested the prediction performance using the remaining 20%, resulting in coefficient of determination (R2) test values of 0.88 and 0.81 and root-mean-square deviation (RMSE) test values 0.64 and 0.80 for the descriptors and circular fingerprint methods, respectively. We interpreted the produced ML models and reported the most effective features for aqueous solubility measures using the Shapley Additive exPlanations (SHAP) and thermodynamic analysis. Low error, ability to investigate the molecular-level interactions, and compatibility with thermodynamic quantities made the fingerprint method a distinct model compared to other available computational tools. However, it is worth emphasizing that physicochemical descriptor model outperformed the fingerprint model in achieving better predictive accuracy for the given test set.

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Section: Methodsmentioning

confidence: 99%