QSPR-based model extrapolation prediction of enthalpy of solvation

Yu, Xinliang; Acree, William E.

doi:10.1016/j.molliq.2023.121455

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…The descriptor SsNH2 is recognized as the most contributing feature in the developed model. In simple terms, the SsNH2 value indicates the summation value of the electronic state of a single-bonded NH 2 group present in a compound [ 61 ]. Higher values of SsNH2 have a negative impact on the cellular uptake of ENMOs in the U937 cell line (e.g., 69 , 71 , and 80 ).…”

Section: Resultsmentioning

confidence: 99%

Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification

Dasgupta,

Das,

Das

et al. 2024

Beilstein J. Nanotechnol.

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Nanoparticles (NPs) are considered as versatile tools in various fields including medicine, electronics, and environmental science. Understanding the structural aspects of surface modifiers in nanoparticles that govern their cellular uptake is crucial for optimizing their efficacy and minimizing potential cytotoxicity. The cellular uptake is influenced by multiple factors, namely, size, shape, and surface charge of NPs, as well as their surface functionalization. In the current study, classification-based ML models (i.e., Bayesian classification, random forest, support vector classifier, and linear discriminant analysis) have been developed to identify the features/fingerprints that significantly contribute to the cellular uptake of ENMOs in multiple cell types, including pancreatic cancer cells (PaCa2), human endothelial cells (HUVEC), and human macrophage cells (U937). The best models have been identified for each cell type and analyzed to detect the structural fingerprints/features governing the cellular uptake of ENMOs. The study will direct scientists in the design of ENMOs of higher cellular uptake efficiency for better therapeutic response.

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Section: Resultsmentioning

confidence: 99%

Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification

Dasgupta,

Das,

Das

et al. 2024

Beilstein J. Nanotechnol.

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“…60 A third interpretation could be that a higher SCBO value indicates a larger molecular size. 62 This can be observed in compounds like diphenylcyclopropenone ( 307 ), which have higher values of SCBO and the response, while compounds like 2-ethylbutyraldehyde ( 109 ) have lower values of SCBO and the response. The RASAR descriptor CV sim represents the coefficient of variation of the similarity values of the close source compounds for each query compound, and this contributes negatively towards toxicity.…”

Section: Interpretation Of the Different Pls Plotsmentioning

confidence: 98%

Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals

Banerjee,

Roy

2023

Environ. Sci.: Processes Impacts

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“…Quantitative Structure Property/Activity Relationship (QSPR/QSAR) models have been widely employed for several decades in chemistry-related fields to predict various endpoints of molecules (i.e., physico-chemical properties and biological activities, respectively) on the basis of their structure (e.g., descriptors, fingerprints, graphs), via mathematical methods. Successful QSPR/QSAR applications include very different endpoints such as critical temperature and pressure [1], normal boiling point [2], heat capacity [3], enthalpy of solvation [4]/vaporization [5,6], blood-brain barrier permeability [7], physico-chemical properties of polymers/fuels/ionic liquids [8][9][10][11][12][13][14][15], solubility [16][17][18][19][20][21], minimum ignition energy of combustible dusts [22] or antibacterial/antiviral properties [23,24].…”

Section: Introductionmentioning

confidence: 99%

On the Development of Descriptor-Based Machine Learning Models for Thermodynamic Properties: Part 1—From Data Collection to Model Construction: Understanding of the Methods and Their Effects

Trinh,

Tbatou,

Lasala

et al. 2023

Processes

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In the present work, a multi-angle approach is adopted to develop two ML-QSPR models for the prediction of the enthalpy of formation and the entropy of molecules, in their ideal gas state. The molecules were represented by high-dimensional vectors of structural and physico-chemical characteristics (i.e., descriptors). In this sense, an overview is provided of the possible methods that can be employed at each step of the ML-QSPR procedure (i.e., data preprocessing, dimensionality reduction and model construction) and an attempt is made to increase the understanding of the effects related to a given choice or method on the model performance, interpretability and applicability domain. At the same time, the well-known OECD principles for the validation of (Q)SAR models are also considered and addressed. The employed data set is a good representation of two common problems in ML-QSPR modeling, namely the high-dimensional descriptor-based representation and the high chemical diversity of the molecules. This diversity effectively impacts the subsequent applicability of the developed models to a new molecule. The data set complexity is addressed through customized data preprocessing techniques and genetic algorithms. The former improves the data quality while limiting the loss of information, while the latter allows for the automatic identification of the most important descriptors, in accordance with a physical interpretation. The best performances are obtained with Lasso linear models (MAE test = 25.2 kJ/mol for the enthalpy and 17.9 J/mol/K for the entropy). Finally, the overall developed procedure is also tested on various enthalpy and entropy related data sets from the literature to check its applicability to other problems and competing performances are obtained, highlighting that different methods and molecular representations can lead to good performances.

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QSPR-based model extrapolation prediction of enthalpy of solvation

Cited by 7 publications

References 29 publications

Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification

Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification

Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals

On the Development of Descriptor-Based Machine Learning Models for Thermodynamic Properties: Part 1—From Data Collection to Model Construction: Understanding of the Methods and Their Effects

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