Table 5.

A variety of algorithms (and structural and physico-chemical descriptors) can be used to develop models for the prediction of acute oral toxicity.

Algorithms	References
Random Forests	(Gadaleta et al., 2019; García-Jacas et al., 2019; Lei et al., 2016; Luechtefeld, 2018; Lunghini et al., 2019; Sayed, 2018)
Artificial Neural Networks	(García-Jacas et al., 2019; Kleandrova et al., 2015; Lawless et al., 2018)
Deep Learning	(Jain et al., 2021; Liu et al., 2018b; Sayed, 2018; Zakharov, 2018)
Local Lazy Learning	(Lu et al., 2014)
k-Nearest Neighbors	(Gadaleta et al., 2019; García-Jacas et al., 2019; Roncaglioni et al., 2018; Sayed, 2018; Zhu et al., 2009a, 2009b)
Support Vector Machines	(García-Jacas et al., 2019; Lunghini et al., 2019)
Arithmetic Mean Toxicity	(Raevsky et al., 2010)
Partial Logistic Regression	(Myatt et al., 2018b)
Partial Least Squares Regression	(Myatt et al., 2018b; Sayed, 2018)
Multi-Descriptor Read Across	(Muratov et al., 2018)
Clustering-based QSAR model	(Zhang et al., 2018)
Multiple Linear Regression	(Sayed, 2018)
Global, Adjusted Locally According to Similarity	(Sazonovas et al., 2010)
Decision Trees	(Sayed, 2018)
Expert rule-based methodology	(Bercu et al., 2021)
Read-Across Structure Activity Relationships	(Luechtefeld et al., 2018)
Naïve Bayesian	(Lunghini et al., 2019)