NanoMixHamster: a web-based tool for predicting cytotoxicity of TiO₂-based multicomponent nanomaterials toward Chinese hamster ovary (CHO-K1) cells

Figures & data

Table 1. Comparison of the developed nano-QSAR model in the context of potential use by stakeholders without specialistic knowledge in chemoinformatics or materials modeling.

Activity	Type of NPs	Machine Learning Methods	Descriptors Type	Required field of knowledge		Advanced descriptors used	End user-friendly	References
				Materials modeling	Chemoinformatics
Toxicity toward CHO-K1	Surface modified TiO2-based NPs	1: MLR, 2: PLS, Monte Carlo optimization, 3: Gaussian process approach, 4: MLR	1: Additive descriptors for mixtures: absolute electronegativity of the mixture system, 2: equivalent (Eq), 2nd ionization potential (2vpi), covalent radius (Rc), amount of Ag (Agamt), and thermal conductivity (Tc), 3: average size, BET surface, 4: Additive mixture descriptor	+	+	+	-	1: (Mikolajczyk et al. 2019), 2: (Roy et al. 2019), 3: (Mikolajczyk et al. 2017), 4: (Mikolajczyk et al. 2018)
Toxicity toward E.Coli	Metal oxide NPs	1: MLR, 2: PLS, Monte Carlo optimization, 3: MLR, 4: random forests ensemble classifier, 5: DBT tree enable; DTF independent trees classifier, 6: cluster analysis; read-across approach, 7: MLR, 8: Monte Carlo optimization, 9: SVM	1: enthalpy of formation; 2: SMILES-based descriptors, 3: metal electronegativity, the charge of metal cation corresponding to a given oxide; 4: LDM-based; fragmental SiRMS-based; ionic MLB- based;5: oxygen percent; molar refractivity; polar surface ratio, 6: enthalpy of formation; 7: absolute electronegativities of Me ion and MeOx; molar heat capacity; the average value of the α and β LUMO energy, 8: SMILES-based descriptors, 9: Ec conduction band; H hydration energy	+	+	+	-	1: (Puzyn et al. 2011), 2: (Toropov et al. 2012), 3: (Kar et al. 2014a, 2014b), 4: (Sizochenko et al. 2014), 5: (Singh and Gupta 2014), 6: (Gajewicz et al. 2014b), 7: (Pathakoti et al. 2014), 8: (Toropova et al. 2015), 9: (Kaweeteerawat et al. 2015)
Toxicity toward 264.7 7 RAW and BEAS-2B cells	Noble metals, metal oxide NPs and quantum dots	1: SOM; cluster analysis; association rules, 2: dose-response analysis, 3: dosimetry analysis, 4: mechanism- based classificator, 5: random forests ensemble classifier; causal inference methods, 6: SVM; logistic regression; quadric logistic regression; SOM, 7: regression tree; dose-response analysis	1: toxicity outcomes, 2: toxicity outcomes, 3: parameters calculated using “particle in a box” sedimentation model, 4: size, solubility, aggregating, shape, 5: LDM-based, fragmental SiRMS-based, ionic MLB- based, 6: atomization energy of MeOx; atomic mass of Me; primary size; conduction band energy; metal oxide ionization energy; electronegativity; ionic index (MLB-based), 7: metal dissolution; conductivity band energy	+	+	+	-	1: (Liu et al. 2014), 2: (Patel et al. 2013), 3: (Liu et al. 2015), 4: (Lai 2015), 5: (Sizochenko et al. 2015b), 6: (Liu et al. 2013a and b), 7: (Zhang et al. 2012)
Toxicity toward BEAS-2B cells	Metal oxide NPs	1: classification model	1: atomization energy; primary size; volume concentration; period of the nanoparticle metal	+	+	+	-	1: (Liu et al. 2011)
Toxicity toward 264.7 7 RAW	Noble metals, metal oxide NPs	1: Self-organizing map (SOM)	1: toxicity outcomes	-	+	-	-	1: (Rallo et al. 2011)
Toxicity toward HaCaTcells	Metal oxide NPs	1: MLR, 2: random forests ensemble classifier, 3: cluster analysis; read-across approach	1: enthalpy of formation a 0.5 × 0.5˟0.5 nm metal oxide nanocluster; the Mulliken’s electronegativity of the cluster, 2: LDM-based; fragmental SiRMS-based; ionic MLB- based, 3: Mulliken’s electronegativity of the cluster	+	+	+	-	1: (Gajewicz et al. 2014b), 2: (Sizochenko et al. 2014), 3: (Gajewicz et al. 2014a)
A549 cells	Metal oxide NPs	1: simple correlations using Spearmen’s rank	1: atomic number; point of zeta potential; number of binding sites; dissolution at pH = 7.4 and pH = 4.5	+	+	+	-	1: (Chusuei et al. 2013)
Human lung fibroblasts	SiO2	1: Monte Carlo optimization	1: SMILES-based descriptors	-	+	-	-	1: (Toropova et al. 2014)
Wistar rat lung cells	Noble metals, metal oxide NPs	1: linear correlation	1: Zeta potential at acid pH	-	+	-	-	1: (Cho et al. 2012)
PaCa2 cancer cells	NPs with same metal cores and different linkers	1: DBT tree enable; DTF independent trees classifier, 2: kNN, 3: PLS, 4: MLP-NN; MLR, 5: Bayesian linear; and nonlinear feature selection methods, 6: Naïve Bayes, logistic regression; kNN; SVM, 7: Monte Carlo optimization, 8: kNN	1: CDK descriptors: electronic, topological, geometrical, and 4 constitutional, 2: Mold2 descriptors, 3: Dragon 6, Cerius 2, PaDEL descriptors, 4: Dragon-based descriptors, 5: Dragon-based descriptors, 6: CH2 groups; primary, secondary, tertiary nitrogens; halogens, sulfur atoms; fused rings; hydrogen bonding, 7: SMILES-based descriptors, 8: MOE descriptors: number of nitrogens; different topological indices; logP; number of bonds	+	+	+	+ (2)	1: (Singh and Gupta 2014), 2: (Melagraki and Afantitis 2014), 3: (Kar et al. 2014a), 4: (Ghorbanzadeh, Fatemi, and Karimpour 2012), 5: (Epa et al. 2012; Winkler et al., 2014), 6: (Chau and Yap 2012), 7: (Toropov et al. 2013), 8: (Fourches et al. 2010)
Oxidative potential	Metal oxide NPs	1: Classification model	1: conduction band energy	+	+	+	-	1: (Burello and Worth 2011)
Aorta endothelial, vascular smooth muscle, hepatcyte, monocyte	Various NPs with diverse metal cores	1: DBT tree enable; DTF independent trees classifier, 2: SOM, Bayesian classifier, 3: SVM	1: descriptors adopted from (Shaw et al., 2008) and (Fourches et al., 2010), 2: spin-lattice relaxivity; zeta potential, 3: toxicity outcomes	+	+	+	-	1: (Singh and Gupta 2014), 2: (Liu et al. 2013), 3: (Fourches et al. 2010)
Endocytsis in U937 cells	Serum-coated Au	1: PLS	1: experimental conditions; TEM-based and shape descriptors	+	+	+	-	1: (Bigdeli, Hormozi-Nezhad, and Parastar 2015)
Thermal conductivity	Metals, metal oxide NPs	1: random forests ensemble classifier	1: LDM-based; concentration; TEM-based spherical size	+	+	+	-	1: (Sizochenko et al. 2015a)
Zeta potential	Metal oxide NPs	1: MLR	1: TEM-based spherical size; HOMO per metal atom	+	+	+	-	1: (Mikolajczyk et al. 2015)

Figure 1. Example of the prediction made for TiO₂ NPs modified with Ag and Pd that is inside the applicability domain.

Figure 2. The Insubria plot generated for the first considered Me_mix-TiO₂.

Figure 3. Example of the prediction made for TiO₂ NPs modified with Au and Pt that is outside the applicability domain.

Figure 4. The Insubria plot generated for the second considered Me_mix-TiO₂.

Figure 5. Graphical user interface allowing user to select the amount of generated nanomaterials.

Figure 6. Example of the Me_mix-TiO₂ NPs nanoparticles.

Figure 7. Insubria plot generated for the theoretical Me_mix-TiO₂.

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